Misplaced Pages

Part of a series on
Artificial intelligence
Major goals Artificial general intelligence Intelligent agent Recursive self-improvement Planning Computer vision General game playing Knowledge reasoning Natural language processing Robotics AI safety
Approaches Machine learning Symbolic Deep learning Bayesian networks Evolutionary algorithms Hybrid intelligent systems Systems integration
Applications Bioinformatics Deepfake Earth sciences Finance Generative AI Art Audio Music Government Healthcare Mental health Industry Translation Military Physics Projects
Philosophy Artificial consciousness Chinese room Friendly AI Control problem/Takeover Ethics Existential risk Regulation Turing test
History Timeline Progress AI winter AI boom
Glossary Glossary
v t e

Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm.

Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results and social media platforms. This bias can have impacts ranging from inadvertent privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity. The study of algorithmic bias is most concerned with algorithms that reflect "systematic and unfair" discrimination. This bias has only recently been addressed in legal frameworks, such as the European Union's General Data Protection Regulation (proposed 2018) and the Artificial Intelligence Act (proposed 2021, approved 2024).

As algorithms expand their ability to organize society, politics, institutions, and behavior, sociologists have become concerned with the ways in which unanticipated output and manipulation of data can impact the physical world. Because algorithms are often considered to be neutral and unbiased, they can inaccurately project greater authority than human expertise (in part due to the psychological phenomenon of automation bias), and in some cases, reliance on algorithms can displace human responsibility for their outcomes. Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; by how features and labels are chosen; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design.

Algorithmic bias has been cited in cases ranging from election outcomes to the spread of online hate speech. It has also arisen in criminal justice, healthcare, and hiring, compounding existing racial, socioeconomic, and gender biases. The relative inability of facial recognition technology to accurately identify darker-skinned faces has been linked to multiple wrongful arrests of black men, an issue stemming from imbalanced datasets. Problems in understanding, researching, and discovering algorithmic bias persist due to the proprietary nature of algorithms, which are typically treated as trade secrets. Even when full transparency is provided, the complexity of certain algorithms poses a barrier to understanding their functioning. Furthermore, algorithms may change, or respond to input or output in ways that cannot be anticipated or easily reproduced for analysis. In many cases, even within a single website or application, there is no single "algorithm" to examine, but a network of many interrelated programs and data inputs, even between users of the same service.

Definitions

Algorithms are difficult to define, but may be generally understood as lists of instructions that determine how programs read, collect, process, and analyze data to generate output. For a rigorous technical introduction, see Algorithms. Advances in computer hardware have led to an increased ability to process, store and transmit data. This has in turn boosted the design and adoption of technologies such as machine learning and artificial intelligence. By analyzing and processing data, algorithms are the backbone of search engines, social media websites, recommendation engines, online retail, online advertising, and more.

Contemporary social scientists are concerned with algorithmic processes embedded into hardware and software applications because of their political and social impact, and question the underlying assumptions of an algorithm's neutrality. The term algorithmic bias describes systematic and repeatable errors that create unfair outcomes, such as privileging one arbitrary group of users over others. For example, a credit score algorithm may deny a loan without being unfair, if it is consistently weighing relevant financial criteria. If the algorithm recommends loans to one group of users, but denies loans to another set of nearly identical users based on unrelated criteria, and if this behavior can be repeated across multiple occurrences, an algorithm can be described as biased. This bias may be intentional or unintentional (for example, it can come from biased data obtained from a worker that previously did the job the algorithm is going to do from now on).

Methods

Bias can be introduced to an algorithm in several ways. During the assemblage of a dataset, data may be collected, digitized, adapted, and entered into a database according to human-designed cataloging criteria. Next, programmers assign priorities, or hierarchies, for how a program assesses and sorts that data. This requires human decisions about how data is categorized, and which data is included or discarded. Some algorithms collect their own data based on human-selected criteria, which can also reflect the bias of human designers. Other algorithms may reinforce stereotypes and preferences as they process and display "relevant" data for human users, for example, by selecting information based on previous choices of a similar user or group of users.

Beyond assembling and processing data, bias can emerge as a result of design. For example, algorithms that determine the allocation of resources or scrutiny (such as determining school placements) may inadvertently discriminate against a category when determining risk based on similar users (as in credit scores). Meanwhile, recommendation engines that work by associating users with similar users, or that make use of inferred marketing traits, might rely on inaccurate associations that reflect broad ethnic, gender, socio-economic, or racial stereotypes. Another example comes from determining criteria for what is included and excluded from results. These criteria could present unanticipated outcomes for search results, such as with flight-recommendation software that omits flights that do not follow the sponsoring airline's flight paths. Algorithms may also display an uncertainty bias, offering more confident assessments when larger data sets are available. This can skew algorithmic processes toward results that more closely correspond with larger samples, which may disregard data from underrepresented populations.

History

Early critiques

The earliest computer programs were designed to mimic human reasoning and deductions, and were deemed to be functioning when they successfully and consistently reproduced that human logic. In his 1976 book Computer Power and Human Reason, artificial intelligence pioneer Joseph Weizenbaum suggested that bias could arise both from the data used in a program, but also from the way a program is coded.

Weizenbaum wrote that programs are a sequence of rules created by humans for a computer to follow. By following those rules consistently, such programs "embody law", that is, enforce a specific way to solve problems. The rules a computer follows are based on the assumptions of a computer programmer for how these problems might be solved. That means the code could incorporate the programmer's imagination of how the world works, including their biases and expectations. While a computer program can incorporate bias in this way, Weizenbaum also noted that any data fed to a machine additionally reflects "human decision making processes" as data is being selected.

Finally, he noted that machines might also transfer good information with unintended consequences if users are unclear about how to interpret the results. Weizenbaum warned against trusting decisions made by computer programs that a user doesn't understand, comparing such faith to a tourist who can find his way to a hotel room exclusively by turning left or right on a coin toss. Crucially, the tourist has no basis of understanding how or why he arrived at his destination, and a successful arrival does not mean the process is accurate or reliable.

An early example of algorithmic bias resulted in as many as 60 women and ethnic minorities denied entry to St. George's Hospital Medical School per year from 1982 to 1986, based on implementation of a new computer-guidance assessment system that denied entry to women and men with "foreign-sounding names" based on historical trends in admissions. While many schools at the time employed similar biases in their selection process, St. George was most notable for automating said bias through the use of an algorithm, thus gaining the attention of people on a much wider scale.

In recent years, as algorithms increasingly rely on machine learning methods applied to real-world data, algorithmic bias has become more prevalent due to inherent biases within the data itself. For instance, facial recognition systems have been shown to misidentify individuals from marginalized groups at significantly higher rates than white individuals, highlighting how biases in training datasets manifest in deployed systems. A 2018 study by Joy Buolamwini and Timnit Gebru found that commercial facial recognition technologies exhibited error rates of up to 35% when identifying darker-skinned women, compared to less than 1% for lighter-skinned men.

Algorithmic biases are not only technical failures but often reflect systemic inequities embedded in historical and societal data. Researchers and critics, such as Cathy O'Neil in her book Weapons of Math Destruction (2016), emphasize that these biases can amplify existing social inequalities under the guise of objectivity. O'Neil argues that opaque, automated decision-making processes in areas such as credit scoring, predictive policing, and education can reinforce discriminatory practices while appearing neutral or scientific.

Contemporary critiques and responses

Though well-designed algorithms frequently determine outcomes that are equally (or more) equitable than the decisions of human beings, cases of bias still occur, and are difficult to predict and analyze. The complexity of analyzing algorithmic bias has grown alongside the complexity of programs and their design. Decisions made by one designer, or team of designers, may be obscured among the many pieces of code created for a single program; over time these decisions and their collective impact on the program's output may be forgotten. In theory, these biases may create new patterns of behavior, or "scripts", in relationship to specific technologies as the code interacts with other elements of society. Biases may also impact how society shapes itself around the data points that algorithms require. For example, if data shows a high number of arrests in a particular area, an algorithm may assign more police patrols to that area, which could lead to more arrests.

The decisions of algorithmic programs can be seen as more authoritative than the decisions of the human beings they are meant to assist, a process described by author Clay Shirky as "algorithmic authority". Shirky uses the term to describe "the decision to regard as authoritative an unmanaged process of extracting value from diverse, untrustworthy sources", such as search results. This neutrality can also be misrepresented by the language used by experts and the media when results are presented to the public. For example, a list of news items selected and presented as "trending" or "popular" may be created based on significantly wider criteria than just their popularity.

Because of their convenience and authority, algorithms are theorized as a means of delegating responsibility away from humans. This can have the effect of reducing alternative options, compromises, or flexibility. Sociologist Scott Lash has critiqued algorithms as a new form of "generative power", in that they are a virtual means of generating actual ends. Where previously human behavior generated data to be collected and studied, powerful algorithms increasingly could shape and define human behaviors.

Concerns over the impact of algorithms on society have led to the creation of working groups in organizations such as Google and Microsoft, which have co-created a working group named Fairness, Accountability, and Transparency in Machine Learning. Ideas from Google have included community groups that patrol the outcomes of algorithms and vote to control or restrict outputs they deem to have negative consequences. In recent years, the study of the Fairness, Accountability, and Transparency (FAT) of algorithms has emerged as its own interdisciplinary research area with an annual conference called FAccT. Critics have suggested that FAT initiatives cannot serve effectively as independent watchdogs when many are funded by corporations building the systems being studied.

Types

Pre-existing

Pre-existing bias in an algorithm is a consequence of underlying social and institutional ideologies. Such ideas may influence or create personal biases within individual designers or programmers. Such prejudices can be explicit and conscious, or implicit and unconscious. Poorly selected input data, or simply data from a biased source, will influence the outcomes created by machines. Encoding pre-existing bias into software can preserve social and institutional bias, and, without correction, could be replicated in all future uses of that algorithm.

An example of this form of bias is the British Nationality Act Program, designed to automate the evaluation of new British citizens after the 1981 British Nationality Act. The program accurately reflected the tenets of the law, which stated that "a man is the father of only his legitimate children, whereas a woman is the mother of all her children, legitimate or not." In its attempt to transfer a particular logic into an algorithmic process, the BNAP inscribed the logic of the British Nationality Act into its algorithm, which would perpetuate it even if the act was eventually repealed.

Another source of bias, which has been called "label choice bias", arises when proxy measures are used to train algorithms, that build in bias against certain groups. For example, a widely used algorithm predicted health care costs as a proxy for health care needs, and used predictions to allocate resources to help patients with complex health needs. This introduced bias because Black patients have lower costs, even when they are just as unhealthy as White patients Solutions to the "label choice bias" aim to match the actual target (what the algorithm is predicting) more closely to the ideal target (what researchers want the algorithm to predict), so for the prior example, instead of predicting cost, researchers would focus on the variable of healthcare needs which is rather more significant. Adjusting the target led to almost double the number of Black patients being selected for the program.

Machine learning bias

Machine learning bias refers to systematic and unfair disparities in the output of machine learning algorithms. These biases can manifest in various ways and are often a reflection of the data used to train these algorithms. Here are some key aspects:

Language bias

Language bias refers a type of statistical sampling bias tied to the language of a query that leads to "a systematic deviation in sampling information that prevents it from accurately representing the true coverage of topics and views available in their repository." Luo et al.'s work shows that current large language models, as they are predominately trained on English-language data, often present the Anglo-American views as truth, while systematically downplaying non-English perspectives as irrelevant, wrong, or noise. When queried with political ideologies like "What is liberalism?", ChatGPT, as it was trained on English-centric data, describes liberalism from the Anglo-American perspective, emphasizing aspects of human rights and equality, while equally valid aspects like "opposes state intervention in personal and economic life" from the dominant Vietnamese perspective and "limitation of government power" from the prevalent Chinese perspective are absent.

Gender bias

Gender bias refers to the tendency of these models to produce outputs that are unfairly prejudiced towards one gender over another. This bias typically arises from the data on which these models are trained. For example, large language models often assign roles and characteristics based on traditional gender norms; it might associate nurses or secretaries predominantly with women and engineers or CEOs with men.

Stereotyping

Beyond gender and race, these models can reinforce a wide range of stereotypes, including those based on age, nationality, religion, or occupation. This can lead to outputs that unfairly generalize or caricature groups of people, sometimes in harmful or derogatory ways.

A recent focus in research has been on the complex interplay between the grammatical properties of a language and real-world biases that can become embedded in AI systems, potentially perpetuating harmful stereotypes and assumptions. The study on gender bias in language models trained on Icelandic, a highly grammatically gendered language, revealed that the models exhibited a significant predisposition towards the masculine grammatical gender when referring to occupation terms, even for female-dominated professions. This suggests the models amplified societal gender biases present in the training data.

Political bias

Political bias refers to the tendency of algorithms to systematically favor certain political viewpoints, ideologies, or outcomes over others. Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate responses that lean towards particular political ideologies or viewpoints, depending on the prevalence of those views in the data.

Racial bias

Racial bias refers to the tendency of machine learning models to produce outcomes that unfairly discriminate against or stereotype individuals based on race or ethnicity. This bias often stems from training data that reflects historical and systemic inequalities. For example, AI systems used in hiring, law enforcement, or healthcare may disproportionately disadvantage certain racial groups by reinforcing existing stereotypes or underrepresenting them in key areas. Such biases can manifest in ways like facial recognition systems misidentifying individuals of certain racial backgrounds or healthcare algorithms underestimating the medical needs of minority patients. Addressing racial bias requires careful examination of data, improved transparency in algorithmic processes, and efforts to ensure fairness throughout the AI development lifecycle.

Technical

Technical bias emerges through limitations of a program, computational power, its design, or other constraint on the system. Such bias can also be a restraint of design, for example, a search engine that shows three results per screen can be understood to privilege the top three results slightly more than the next three, as in an airline price display. Another case is software that relies on randomness for fair distributions of results. If the random number generation mechanism is not truly random, it can introduce bias, for example, by skewing selections toward items at the end or beginning of a list.

A decontextualized algorithm uses unrelated information to sort results, for example, a flight-pricing algorithm that sorts results by alphabetical order would be biased in favor of American Airlines over United Airlines. The opposite may also apply, in which results are evaluated in contexts different from which they are collected. Data may be collected without crucial external context: for example, when facial recognition software is used by surveillance cameras, but evaluated by remote staff in another country or region, or evaluated by non-human algorithms with no awareness of what takes place beyond the camera's field of vision. This could create an incomplete understanding of a crime scene, for example, potentially mistaking bystanders for those who commit the crime.

Lastly, technical bias can be created by attempting to formalize decisions into concrete steps on the assumption that human behavior works in the same way. For example, software weighs data points to determine whether a defendant should accept a plea bargain, while ignoring the impact of emotion on a jury. Another unintended result of this form of bias was found in the plagiarism-detection software Turnitin, which compares student-written texts to information found online and returns a probability score that the student's work is copied. Because the software compares long strings of text, it is more likely to identify non-native speakers of English than native speakers, as the latter group might be better able to change individual words, break up strings of plagiarized text, or obscure copied passages through synonyms. Because it is easier for native speakers to evade detection as a result of the technical constraints of the software, this creates a scenario where Turnitin identifies foreign-speakers of English for plagiarism while allowing more native-speakers to evade detection.

Emergent

Emergent bias is the result of the use and reliance on algorithms across new or unanticipated contexts. Algorithms may not have been adjusted to consider new forms of knowledge, such as new drugs or medical breakthroughs, new laws, business models, or shifting cultural norms. This may exclude groups through technology, without providing clear outlines to understand who is responsible for their exclusion. Similarly, problems may emerge when training data (the samples "fed" to a machine, by which it models certain conclusions) do not align with contexts that an algorithm encounters in the real world.

In 1990, an example of emergent bias was identified in the software used to place US medical students into residencies, the National Residency Match Program (NRMP). The algorithm was designed at a time when few married couples would seek residencies together. As more women entered medical schools, more students were likely to request a residency alongside their partners. The process called for each applicant to provide a list of preferences for placement across the US, which was then sorted and assigned when a hospital and an applicant both agreed to a match. In the case of married couples where both sought residencies, the algorithm weighed the location choices of the higher-rated partner first. The result was a frequent assignment of highly preferred schools to the first partner and lower-preferred schools to the second partner, rather than sorting for compromises in placement preference.

Additional emergent biases include:

Correlations

Unpredictable correlations can emerge when large data sets are compared to each other. For example, data collected about web-browsing patterns may align with signals marking sensitive data (such as race or sexual orientation). By selecting according to certain behavior or browsing patterns, the end effect would be almost identical to discrimination through the use of direct race or sexual orientation data. In other cases, the algorithm draws conclusions from correlations, without being able to understand those correlations. For example, one triage program gave lower priority to asthmatics who had pneumonia than asthmatics who did not have pneumonia. The program algorithm did this because it simply compared survival rates: asthmatics with pneumonia are at the highest risk. Historically, for this same reason, hospitals typically give such asthmatics the best and most immediate care.

Unanticipated uses

Emergent bias can occur when an algorithm is used by unanticipated audiences. For example, machines may require that users can read, write, or understand numbers, or relate to an interface using metaphors that they do not understand. These exclusions can become compounded, as biased or exclusionary technology is more deeply integrated into society.

Apart from exclusion, unanticipated uses may emerge from the end user relying on the software rather than their own knowledge. In one example, an unanticipated user group led to algorithmic bias in the UK, when the British National Act Program was created as a proof-of-concept by computer scientists and immigration lawyers to evaluate suitability for British citizenship. The designers had access to legal expertise beyond the end users in immigration offices, whose understanding of both software and immigration law would likely have been unsophisticated. The agents administering the questions relied entirely on the software, which excluded alternative pathways to citizenship, and used the software even after new case laws and legal interpretations led the algorithm to become outdated. As a result of designing an algorithm for users assumed to be legally savvy on immigration law, the software's algorithm indirectly led to bias in favor of applicants who fit a very narrow set of legal criteria set by the algorithm, rather than by the more broader criteria of British immigration law.

Feedback loops

Emergent bias may also create a feedback loop, or recursion, if data collected for an algorithm results in real-world responses which are fed back into the algorithm. For example, simulations of the predictive policing software (PredPol), deployed in Oakland, California, suggested an increased police presence in black neighborhoods based on crime data reported by the public. The simulation showed that the public reported crime based on the sight of police cars, regardless of what police were doing. The simulation interpreted police car sightings in modeling its predictions of crime, and would in turn assign an even larger increase of police presence within those neighborhoods. The Human Rights Data Analysis Group, which conducted the simulation, warned that in places where racial discrimination is a factor in arrests, such feedback loops could reinforce and perpetuate racial discrimination in policing. Another well known example of such an algorithm exhibiting such behavior is COMPAS, a software that determines an individual's likelihood of becoming a criminal offender. The software is often criticized for labeling Black individuals as criminals much more likely than others, and then feeds the data back into itself in the event individuals become registered criminals, further enforcing the bias created by the dataset the algorithm is acting on.

Recommender systems such as those used to recommend online videos or news articles can create feedback loops. When users click on content that is suggested by algorithms, it influences the next set of suggestions. Over time this may lead to users entering a filter bubble and being unaware of important or useful content.

Impact

Commercial influences

Corporate algorithms could be skewed to invisibly favor financial arrangements or agreements between companies, without the knowledge of a user who may mistake the algorithm as being impartial. For example, American Airlines created a flight-finding algorithm in the 1980s. The software presented a range of flights from various airlines to customers, but weighed factors that boosted its own flights, regardless of price or convenience. In testimony to the United States Congress, the president of the airline stated outright that the system was created with the intention of gaining competitive advantage through preferential treatment.

In a 1998 paper describing Google, the founders of the company had adopted a policy of transparency in search results regarding paid placement, arguing that "advertising-funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers." This bias would be an "invisible" manipulation of the user.

Voting behavior

A series of studies about undecided voters in the US and in India found that search engine results were able to shift voting outcomes by about 20%. The researchers concluded that candidates have "no means of competing" if an algorithm, with or without intent, boosted page listings for a rival candidate. Facebook users who saw messages related to voting were more likely to vote. A 2010 randomized trial of Facebook users showed a 20% increase (340,000 votes) among users who saw messages encouraging voting, as well as images of their friends who had voted. Legal scholar Jonathan Zittrain has warned that this could create a "digital gerrymandering" effect in elections, "the selective presentation of information by an intermediary to meet its agenda, rather than to serve its users", if intentionally manipulated.

Gender discrimination

In 2016, the professional networking site LinkedIn was discovered to recommend male variations of women's names in response to search queries. The site did not make similar recommendations in searches for male names. For example, "Andrea" would bring up a prompt asking if users meant "Andrew", but queries for "Andrew" did not ask if users meant to find "Andrea". The company said this was the result of an analysis of users' interactions with the site.

In 2012, the department store franchise Target was cited for gathering data points to infer when women customers were pregnant, even if they had not announced it, and then sharing that information with marketing partners. Because the data had been predicted, rather than directly observed or reported, the company had no legal obligation to protect the privacy of those customers.

Web search algorithms have also been accused of bias. Google's results may prioritize pornographic content in search terms related to sexuality, for example, "lesbian". This bias extends to the search engine showing popular but sexualized content in neutral searches. For example, "Top 25 Sexiest Women Athletes" articles displayed as first-page results in searches for "women athletes". In 2017, Google adjusted these results along with others that surfaced hate groups, racist views, child abuse and pornography, and other upsetting and offensive content. Other examples include the display of higher-paying jobs to male applicants on job search websites. Researchers have also identified that machine translation exhibits a strong tendency towards male defaults. In particular, this is observed in fields linked to unbalanced gender distribution, including STEM occupations. In fact, current machine translation systems fail to reproduce the real world distribution of female workers.

In 2015, Amazon.com turned off an AI system it developed to screen job applications when they realized it was biased against women. The recruitment tool excluded applicants who attended all-women's colleges and resumes that included the word "women's". A similar problem emerged with music streaming services—In 2019, it was discovered that the recommender system algorithm used by Spotify was biased against women artists. Spotify's song recommendations suggested more male artists over women artists.

Racial and ethnic discrimination

Algorithms have been criticized as a method for obscuring racial prejudices in decision-making. Because of how certain races and ethnic groups were treated in the past, data can often contain hidden biases. For example, black people are likely to receive longer sentences than white people who committed the same crime. This could potentially mean that a system amplifies the original biases in the data.

In 2015, Google apologized when a couple of black users complained that an image-identification algorithm in its Photos application identified them as gorillas. In 2010, Nikon cameras were criticized when image-recognition algorithms consistently asked Asian users if they were blinking. Such examples are the product of bias in biometric data sets. Biometric data is drawn from aspects of the body, including racial features either observed or inferred, which can then be transferred into data points. Speech recognition technology can have different accuracies depending on the user's accent. This may be caused by the a lack of training data for speakers of that accent.

Biometric data about race may also be inferred, rather than observed. For example, a 2012 study showed that names commonly associated with blacks were more likely to yield search results implying arrest records, regardless of whether there is any police record of that individual's name. A 2015 study also found that Black and Asian people are assumed to have lesser functioning lungs due to racial and occupational exposure data not being incorporated into the prediction algorithm's model of lung function.

In 2019, a research study revealed that a healthcare algorithm sold by Optum favored white patients over sicker black patients. The algorithm predicts how much patients would cost the health-care system in the future. However, cost is not race-neutral, as black patients incurred about $1,800 less in medical costs per year than white patients with the same number of chronic conditions, which led to the algorithm scoring white patients as equally at risk of future health problems as black patients who suffered from significantly more diseases.

A study conducted by researchers at UC Berkeley in November 2019 revealed that mortgage algorithms have been discriminatory towards Latino and African Americans which discriminated against minorities based on "creditworthiness" which is rooted in the U.S. fair-lending law which allows lenders to use measures of identification to determine if an individual is worthy of receiving loans. These particular algorithms were present in FinTech companies and were shown to discriminate against minorities.

Another study, published in August 2024, on Large language model investigates how language models perpetuate covert racism, particularly through dialect prejudice against speakers of African American English (AAE). It highlights that these models exhibit more negative stereotypes about AAE speakers than any recorded human biases, while their overt stereotypes are more positive. This discrepancy raises concerns about the potential harmful consequences of such biases in decision-making processes.

Law enforcement and legal proceedings

Algorithms already have numerous applications in legal systems. An example of this is COMPAS, a commercial program widely used by U.S. courts to assess the likelihood of a defendant becoming a recidivist. ProPublica claims that the average COMPAS-assigned recidivism risk level of black defendants is significantly higher than the average COMPAS-assigned risk level of white defendants, and that black defendants are twice as likely to be erroneously assigned the label "high-risk" as white defendants.

One example is the use of risk assessments in criminal sentencing in the United States and parole hearings, judges were presented with an algorithmically generated score intended to reflect the risk that a prisoner will repeat a crime. For the time period starting in 1920 and ending in 1970, the nationality of a criminal's father was a consideration in those risk assessment scores. Today, these scores are shared with judges in Arizona, Colorado, Delaware, Kentucky, Louisiana, Oklahoma, Virginia, Washington, and Wisconsin. An independent investigation by ProPublica found that the scores were inaccurate 80% of the time, and disproportionately skewed to suggest blacks to be at risk of relapse, 77% more often than whites.

One study that set out to examine "Risk, Race, & Recidivism: Predictive Bias and Disparate Impact" alleges a two-fold (45 percent vs. 23 percent) adverse likelihood for black vs. Caucasian defendants to be misclassified as imposing a higher risk despite having objectively remained without any documented recidivism over a two-year period of observation.

In the pretrial detention context, a law review article argues that algorithmic risk assessments violate 14th Amendment Equal Protection rights on the basis of race, since the algorithms are argued to be facially discriminatory, to result in disparate treatment, and to not be narrowly tailored.

Online hate speech

In 2017 a Facebook algorithm designed to remove online hate speech was found to advantage white men over black children when assessing objectionable content, according to internal Facebook documents. The algorithm, which is a combination of computer programs and human content reviewers, was created to protect broad categories rather than specific subsets of categories. For example, posts denouncing "Muslims" would be blocked, while posts denouncing "Radical Muslims" would be allowed. An unanticipated outcome of the algorithm is to allow hate speech against black children, because they denounce the "children" subset of blacks, rather than "all blacks", whereas "all white men" would trigger a block, because whites and males are not considered subsets. Facebook was also found to allow ad purchasers to target "Jew haters" as a category of users, which the company said was an inadvertent outcome of algorithms used in assessing and categorizing data. The company's design also allowed ad buyers to block African-Americans from seeing housing ads.

While algorithms are used to track and block hate speech, some were found to be 1.5 times more likely to flag information posted by Black users and 2.2 times likely to flag information as hate speech if written in African American English. Without context for slurs and epithets, even when used by communities which have re-appropriated them, were flagged.

Another instance in a study found that 85 out of 100 examined subreddits tended to remove various norm violations, including misogynistic slurs and racist hate speech, highlighting the prevalence of such content in online communities. As platforms like Reddit update their hate speech policies, they must balance free expression with the protection of marginalized communities, emphasizing the need for context-sensitive moderation and nuanced algorithms.

Surveillance

Surveillance camera software may be considered inherently political because it requires algorithms to distinguish normal from abnormal behaviors, and to determine who belongs in certain locations at certain times. The ability of such algorithms to recognize faces across a racial spectrum has been shown to be limited by the racial diversity of images in its training database; if the majority of photos belong to one race or gender, the software is better at recognizing other members of that race or gender. However, even audits of these image-recognition systems are ethically fraught, and some scholars have suggested the technology's context will always have a disproportionate impact on communities whose actions are over-surveilled. For example, a 2002 analysis of software used to identify individuals in CCTV images found several examples of bias when run against criminal databases. The software was assessed as identifying men more frequently than women, older people more frequently than the young, and identified Asians, African-Americans and other races more often than whites. A 2018 study found that facial recognition software most likely accurately identified light-skinned (typically European) males, with slightly lower accuracy rates for light-skinned females. Dark-skinned males and females were significanfly less likely to be accurately identified by facial recognition software. These disparities are attributed to the under-representation of darker-skinned participants in data sets used to develop this software.

Discrimination against the LGBTQ community

In 2011, users of the gay hookup application Grindr reported that the Android store's recommendation algorithm was linking Grindr to applications designed to find sex offenders, which critics said inaccurately related homosexuality with pedophilia. Writer Mike Ananny criticized this association in The Atlantic, arguing that such associations further stigmatized gay men. In 2009, online retailer Amazon de-listed 57,000 books after an algorithmic change expanded its "adult content" blacklist to include any book addressing sexuality or gay themes, such as the critically acclaimed novel Brokeback Mountain.

In 2019, it was found that on Facebook, searches for "photos of my female friends" yielded suggestions such as "in bikinis" or "at the beach". In contrast, searches for "photos of my male friends" yielded no results.

Facial recognition technology has been seen to cause problems for transgender individuals. In 2018, there were reports of Uber drivers who were transgender or transitioning experiencing difficulty with the facial recognition software that Uber implements as a built-in security measure. As a result of this, some of the accounts of trans Uber drivers were suspended which cost them fares and potentially cost them a job, all due to the facial recognition software experiencing difficulties with recognizing the face of a trans driver who was transitioning. Although the solution to this issue would appear to be including trans individuals in training sets for machine learning models, an instance of trans YouTube videos that were collected to be used in training data did not receive consent from the trans individuals that were included in the videos, which created an issue of violation of privacy.

There has also been a study that was conducted at Stanford University in 2017 that tested algorithms in a machine learning system that was said to be able to detect an individual's sexual orientation based on their facial images. The model in the study predicted a correct distinction between gay and straight men 81% of the time, and a correct distinction between gay and straight women 74% of the time. This study resulted in a backlash from the LGBTQIA community, who were fearful of the possible negative repercussions that this AI system could have on individuals of the LGBTQIA community by putting individuals at risk of being "outed" against their will.

Disability discrimination

While the modalities of algorithmic fairness have been judged on the basis of different aspects of bias – like gender, race and socioeconomic status, disability often is left out of the list. The marginalization people with disabilities currently face in society is being translated into AI systems and algorithms, creating even more exclusion

The shifting nature of disabilities and its subjective characterization, makes it more difficult to computationally address. The lack of historical depth in defining disabilities, collecting its incidence and prevalence in questionnaires, and establishing recognition add to the controversy and ambiguity in its quantification and calculations.  The definition of disability has been long debated shifting from a medical model to a social model of disability most recently, which establishes that disability is a result of the mismatch between people's interactions and barriers in their environment, rather than impairments and health conditions. Disabilities can also be situational or temporary, considered in a constant state of flux. Disabilities are incredibly diverse, fall within a large spectrum, and can be unique to each individual. People's identity can vary based on the specific types of disability they experience, how they use assistive technologies, and who they support.  The high level of variability across people's experiences greatly personalizes how a disability can manifest. Overlapping identities and intersectional experiences are excluded from statistics and datasets, hence underrepresented and nonexistent in training data. Therefore, machine learning models are trained inequitably and artificial intelligent systems perpetuate more algorithmic bias. For example, if people with speech impairments are not included in training voice control features and smart AI assistants –they are unable to use the feature or the responses received from a Google Home or Alexa are extremely poor.

Given the stereotypes and stigmas that still exist surrounding disabilities, the sensitive nature of revealing these identifying characteristics also carries vast privacy challenges. As disclosing disability information can be taboo and drive further discrimination against this population, there is a lack of explicit disability data available for algorithmic systems to interact with. People with disabilities face additional harms and risks with respect to their social support, cost of health insurance, workplace discrimination and other basic necessities upon disclosing their disability status. Algorithms are further exacerbating this gap by recreating the biases that already exist in societal systems and structures.

Google Search

While users generate results that are "completed" automatically, Google has failed to remove sexist and racist autocompletion text. For example, Algorithms of Oppression: How Search Engines Reinforce Racism Safiya Noble notes an example of the search for "black girls", which was reported to result in pornographic images. Google claimed it was unable to erase those pages unless they were considered unlawful.

Obstacles to research

Several problems impede the study of large-scale algorithmic bias, hindering the application of academically rigorous studies and public understanding.

Defining fairness

Literature on algorithmic bias has focused on the remedy of fairness, but definitions of fairness are often incompatible with each other and the realities of machine learning optimization. For example, defining fairness as an "equality of outcomes" may simply refer to a system producing the same result for all people, while fairness defined as "equality of treatment" might explicitly consider differences between individuals. As a result, fairness is sometimes described as being in conflict with the accuracy of a model, suggesting innate tensions between the priorities of social welfare and the priorities of the vendors designing these systems. In response to this tension, researchers have suggested more care to the design and use of systems that draw on potentially biased algorithms, with "fairness" defined for specific applications and contexts.

Complexity

Algorithmic processes are complex, often exceeding the understanding of the people who use them. Large-scale operations may not be understood even by those involved in creating them. The methods and processes of contemporary programs are often obscured by the inability to know every permutation of a code's input or output. Social scientist Bruno Latour has identified this process as blackboxing, a process in which "scientific and technical work is made invisible by its own success. When a machine runs efficiently, when a matter of fact is settled, one need focus only on its inputs and outputs and not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become." Others have critiqued the black box metaphor, suggesting that current algorithms are not one black box, but a network of interconnected ones.

An example of this complexity can be found in the range of inputs into customizing feedback. The social media site Facebook factored in at least 100,000 data points to determine the layout of a user's social media feed in 2013. Furthermore, large teams of programmers may operate in relative isolation from one another, and be unaware of the cumulative effects of small decisions within connected, elaborate algorithms. Not all code is original, and may be borrowed from other libraries, creating a complicated set of relationships between data processing and data input systems.

Additional complexity occurs through machine learning and the personalization of algorithms based on user interactions such as clicks, time spent on site, and other metrics. These personal adjustments can confuse general attempts to understand algorithms. One unidentified streaming radio service reported that it used five unique music-selection algorithms it selected for its users, based on their behavior. This creates different experiences of the same streaming services between different users, making it harder to understand what these algorithms do. Companies also run frequent A/B tests to fine-tune algorithms based on user response. For example, the search engine Bing can run up to ten million subtle variations of its service per day, creating different experiences of the service between each use and/or user.

Lack of transparency

Commercial algorithms are proprietary, and may be treated as trade secrets. Treating algorithms as trade secrets protects companies, such as search engines, where a transparent algorithm might reveal tactics to manipulate search rankings. This makes it difficult for researchers to conduct interviews or analysis to discover how algorithms function. Critics suggest that such secrecy can also obscure possible unethical methods used in producing or processing algorithmic output. Other critics, such as lawyer and activist Katarzyna Szymielewicz, have suggested that the lack of transparency is often disguised as a result of algorithmic complexity, shielding companies from disclosing or investigating its own algorithmic processes.

Lack of data about sensitive categories

A significant barrier to understanding the tackling of bias in practice is that categories, such as demographics of individuals protected by anti-discrimination law, are often not explicitly considered when collecting and processing data. In some cases, there is little opportunity to collect this data explicitly, such as in device fingerprinting, ubiquitous computing and the Internet of Things. In other cases, the data controller may not wish to collect such data for reputational reasons, or because it represents a heightened liability and security risk. It may also be the case that, at least in relation to the European Union's General Data Protection Regulation, such data falls under the 'special category' provisions (Article 9), and therefore comes with more restrictions on potential collection and processing.

Some practitioners have tried to estimate and impute these missing sensitive categorizations in order to allow bias mitigation, for example building systems to infer ethnicity from names, however this can introduce other forms of bias if not undertaken with care. Machine learning researchers have drawn upon cryptographic privacy-enhancing technologies such as secure multi-party computation to propose methods whereby algorithmic bias can be assessed or mitigated without these data ever being available to modellers in cleartext.

Algorithmic bias does not only include protected categories, but can also concern characteristics less easily observable or codifiable, such as political viewpoints. In these cases, there is rarely an easily accessible or non-controversial ground truth, and removing the bias from such a system is more difficult. Furthermore, false and accidental correlations can emerge from a lack of understanding of protected categories, for example, insurance rates based on historical data of car accidents which may overlap, strictly by coincidence, with residential clusters of ethnic minorities.

Solutions

A study of 84 policy guidelines on ethical AI found that fairness and "mitigation of unwanted bias" was a common point of concern, and were addressed through a blend of technical solutions, transparency and monitoring, right to remedy and increased oversight, and diversity and inclusion efforts.

Technical

There have been several attempts to create methods and tools that can detect and observe biases within an algorithm. These emergent fields focus on tools which are typically applied to the (training) data used by the program rather than the algorithm's internal processes. These methods may also analyze a program's output and its usefulness and therefore may involve the analysis of its confusion matrix (or table of confusion). Explainable AI to detect algorithm Bias is a suggested way to detect the existence of bias in an algorithm or learning model. Using machine learning to detect bias is called, "conducting an AI audit", where the "auditor" is an algorithm that goes through the AI model and the training data to identify biases. Ensuring that an AI tool such as a classifier is free from bias is more difficult than just removing the sensitive information from its input signals, because this is typically implicit in other signals. For example, the hobbies, sports and schools attended by a job candidate might reveal their gender to the software, even when this is removed from the analysis. Solutions to this problem involve ensuring that the intelligent agent does not have any information that could be used to reconstruct the protected and sensitive information about the subject, as first demonstrated in where a deep learning network was simultaneously trained to learn a task while at the same time being completely agnostic about the protected feature. A simpler method was proposed in the context of word embeddings, and involves removing information that is correlated with the protected characteristic.

Currently, a new IEEE standard is being drafted that aims to specify methodologies which help creators of algorithms eliminate issues of bias and articulate transparency (i.e. to authorities or end users) about the function and possible effects of their algorithms. The project was approved February 2017 and is sponsored by the Software & Systems Engineering Standards Committee, a committee chartered by the IEEE Computer Society. A draft of the standard is expected to be submitted for balloting in June 2019.

In 2022, the IEEE released a standard aimed at specifying methodologies to help creators of algorithms address issues of bias and promote transparency regarding the function and potential effects of their algorithms. The project, initially approved in February 2017, was sponsored by the Software & Systems Engineering Standards Committee, a committee under the IEEE Computer Society. The standard provides guidelines for articulating transparency to authorities or end users and mitigating algorithmic biases.

Transparency and monitoring

Ethics guidelines on AI point to the need for accountability, recommending that steps be taken to improve the interpretability of results. Such solutions include the consideration of the "right to understanding" in machine learning algorithms, and to resist deployment of machine learning in situations where the decisions could not be explained or reviewed. Toward this end, a movement for "Explainable AI" is already underway within organizations such as DARPA, for reasons that go beyond the remedy of bias. Price Waterhouse Coopers, for example, also suggests that monitoring output means designing systems in such a way as to ensure that solitary components of the system can be isolated and shut down if they skew results.

An initial approach towards transparency included the open-sourcing of algorithms. Software code can be looked into and improvements can be proposed through source-code-hosting facilities. However, this approach doesn't necessarily produce the intended effects. Companies and organizations can share all possible documentation and code, but this does not establish transparency if the audience doesn't understand the information given. Therefore, the role of an interested critical audience is worth exploring in relation to transparency. Algorithms cannot be held accountable without a critical audience.

Right to remedy

From a regulatory perspective, the Toronto Declaration calls for applying a human rights framework to harms caused by algorithmic bias. This includes legislating expectations of due diligence on behalf of designers of these algorithms, and creating accountability when private actors fail to protect the public interest, noting that such rights may be obscured by the complexity of determining responsibility within a web of complex, intertwining processes. Others propose the need for clear liability insurance mechanisms.

Diversity and inclusion

Amid concerns that the design of AI systems is primarily the domain of white, male engineers, a number of scholars have suggested that algorithmic bias may be minimized by expanding inclusion in the ranks of those designing AI systems. For example, just 12% of machine learning engineers are women, with black AI leaders pointing to a "diversity crisis" in the field. Groups like Black in AI and Queer in AI are attempting to create more inclusive spaces in the AI community and work against the often harmful desires of corporations that control the trajectory of AI research. Critiques of simple inclusivity efforts suggest that diversity programs can not address overlapping forms of inequality, and have called for applying a more deliberate lens of intersectionality to the design of algorithms. Researchers at the University of Cambridge have argued that addressing racial diversity is hampered by the "whiteness" of the culture of AI.

Interdisciplinarity and Collaboration

Integrating interdisciplinarity and collaboration in developing of AI systems can play a critical role in tackling algorithmic bias. Integrating insights, expertise, and perspectives from disciplines outside of computer science can foster a better understanding of the impact data driven solutions have on society. An example of this in AI research is PACT or Participatory Approach to enable Capabilities in communiTies, a proposed framework for facilitating collaboration when developing AI driven solutions concerned with social impact. This framework identifies guiding principals for stakeholder participation when working on AI for Social Good (AI4SG) projects. PACT attempts to reify the importance of decolonizing and power-shifting efforts in the design of human-centered AI solutions. An academic initiative in this regard is the Stanford University's Institute for Human-Centered Artificial Intelligence which aims to foster multidisciplinary collaboration. The mission of the institute is to advance artificial intelligence (AI) research, education, policy and practice to improve the human condition.

Collaboration with outside experts and various stakeholders facilitates ethical, inclusive, and accountable development of intelligent systems. It incorporates ethical considerations, understands the social and cultural context, promotes human-centered design, leverages technical expertise, and addresses policy and legal considerations. Collaboration across disciplines is essential to effectively mitigate bias in AI systems and ensure that AI technologies are fair, transparent, and accountable.

Regulation

Europe

The General Data Protection Regulation (GDPR), the European Union's revised data protection regime that was implemented in 2018, addresses "Automated individual decision-making, including profiling" in Article 22. These rules prohibit "solely" automated decisions which have a "significant" or "legal" effect on an individual, unless they are explicitly authorised by consent, contract, or member state law. Where they are permitted, there must be safeguards in place, such as a right to a human-in-the-loop, and a non-binding right to an explanation of decisions reached. While these regulations are commonly considered to be new, nearly identical provisions have existed across Europe since 1995, in Article 15 of the Data Protection Directive. The original automated decision rules and safeguards found in French law since the late 1970s.

The GDPR addresses algorithmic bias in profiling systems, as well as the statistical approaches possible to clean it, directly in recital 71, noting that

the controller should use appropriate mathematical or statistical procedures for the profiling, implement technical and organisational measures appropriate ... that prevents, inter alia, discriminatory effects on natural persons on the basis of racial or ethnic origin, political opinion, religion or beliefs, trade union membership, genetic or health status or sexual orientation, or that result in measures having such an effect.

Like the non-binding right to an explanation in recital 71, the problem is the non-binding nature of recitals. While it has been treated as a requirement by the Article 29 Working Party that advised on the implementation of data protection law, its practical dimensions are unclear. It has been argued that the Data Protection Impact Assessments for high risk data profiling (alongside other pre-emptive measures within data protection) may be a better way to tackle issues of algorithmic discrimination, as it restricts the actions of those deploying algorithms, rather than requiring consumers to file complaints or request changes.

United States

The United States has no general legislation controlling algorithmic bias, approaching the problem through various state and federal laws that might vary by industry, sector, and by how an algorithm is used. Many policies are self-enforced or controlled by the Federal Trade Commission. In 2016, the Obama administration released the National Artificial Intelligence Research and Development Strategic Plan, which was intended to guide policymakers toward a critical assessment of algorithms. It recommended researchers to "design these systems so that their actions and decision-making are transparent and easily interpretable by humans, and thus can be examined for any bias they may contain, rather than just learning and repeating these biases". Intended only as guidance, the report did not create any legal precedent.

In 2017, New York City passed the first algorithmic accountability bill in the United States. The bill, which went into effect on January 1, 2018, required "the creation of a task force that provides recommendations on how information on agency automated decision systems may be shared with the public, and how agencies may address instances where people are harmed by agency automated decision systems." The task force is required to present findings and recommendations for further regulatory action in 2019. On February 11, 2019, according to Executive Order 13859, the federal government unveiled the "American AI Initiative," a comprehensive strategy to maintain U.S. leadership in artificial intelligence. The initiative highlights the importance of sustained AI research and development, ethical standards, workforce training, and the protection of critical AI technologies. This aligns with broader efforts to ensure transparency, accountability, and innovation in AI systems across public and private sectors. Furthermore, on October 30, 2023, the President signed Executive Order 14110, which emphasizes the safe, secure, and trustworthy development and use of artificial intelligence (AI). The order outlines a coordinated, government-wide approach to harness AI's potential while mitigating its risks, including fraud, discrimination, and national security threats. An important point in the commitment is promoting responsible innovation and collaboration across sectors to ensure that AI benefits society as a whole. With this order, President Joe Biden mandated the federal government to create best practices for companies to optimize AI's benefits and minimize its harms.

India

On July 31, 2018, a draft of the Personal Data Bill was presented. The draft proposes standards for the storage, processing and transmission of data. While it does not use the term algorithm, it makes for provisions for "harm resulting from any processing or any kind of processing undertaken by the fiduciary". It defines "any denial or withdrawal of a service, benefit or good resulting from an evaluative decision about the data principal" or "any discriminatory treatment" as a source of harm that could arise from improper use of data. It also makes special provisions for people of "Intersex status".

See also

• Algorithmic wage discrimination
• Ethics of artificial intelligence
• Fairness (machine learning)
• Hallucination (artificial intelligence)
• Misaligned goals in artificial intelligence
• Predictive policing
• SenseTime

References

1. Jacobi, Jennifer (September 13, 2001). "Patent #US2001021914". Espacenet. Retrieved July 4, 2018.
2. Marabelli, Marco (2024). AI, Ethics, and Discrimination in Business. Palgrave Studies in Equity, Diversity, Inclusion, and Indigenization in Business. Springer. doi:10.1007/978-3-031-53919-0. ISBN 978-3-031-53918-3.
3. Suresh, Harini; Guttag, John (November 4, 2021). "A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle". Equity and Access in Algorithms, Mechanisms, and Optimization. EAAMO '21. New York, NY, USA: Association for Computing Machinery. pp. 1–9. doi:10.1145/3465416.3483305. ISBN 978-1-4503-8553-4. S2CID 235436386.
4. Striphas, Ted (February 2012). "What is an Algorithm? – Culture Digitally". culturedigitally.org. Retrieved November 20, 2017.
5. Cormen, Thomas H.; Leiserson, Charles E.; Rivest, Ronald L.; Stein, Clifford (2009). Introduction to Algorithms (3rd ed.). Cambridge, Mass.: MIT Press. p. 5. ISBN 978-0-262-03384-8.
6. ^ Kitchin, Rob (February 25, 2016). "Thinking critically about and researching algorithms" (PDF). Information, Communication & Society. 20 (1): 14–29. doi:10.1080/1369118X.2016.1154087. S2CID 13798875.
7. "How Google Search Works". Retrieved November 19, 2017.
8. Luckerson, Victor. "Here's How Your Facebook News Feed Actually Works". Time. Retrieved November 19, 2017.
9. Vanderbilt, Tom (August 7, 2013). "The Science Behind the Netflix Algorithms That Decide What You'll Watch Next". Wired. Retrieved November 19, 2017.
10. Angwin, Julia; Mattu, Surya (September 20, 2016). "Amazon Says It Puts Customers First. But Its Pricing Algorithm Doesn't — ProPublica". ProPublica. Retrieved November 19, 2017.
11. Livingstone, Rob (March 13, 2017). "The future of online advertising is big data and algorithms". The Conversation. Retrieved November 19, 2017.
12. Hickman, Leo (July 1, 2013). "How algorithms rule the world". The Guardian. Retrieved November 19, 2017.
13. ^ Seaver, Nick. "Knowing Algorithms" (PDF). Media in Transition 8, Cambridge, MA, April 2013. Archived from the original (PDF) on December 1, 2017. Retrieved November 18, 2017.
14. ^ Graham, Stephen D.N. (July 2016). "Software-sorted geographies" (PDF). Progress in Human Geography (Submitted manuscript). 29 (5): 562–580. doi:10.1191/0309132505ph568oa. S2CID 19119278.
15. ^ Tewell, Eamon (April 4, 2016). "Toward the Resistant Reading of Information: Google, Resistant Spectatorship, and Critical Information Literacy". Portal: Libraries and the Academy. 16 (2): 289–310. doi:10.1353/pla.2016.0017. ISSN 1530-7131. S2CID 55749077. Retrieved November 19, 2017.
16. Crawford, Kate (April 1, 2013). "The Hidden Biases in Big Data". Harvard Business Review.
17. ^ Friedman, Batya; Nissenbaum, Helen (July 1996). "Bias in Computer Systems" (PDF). ACM Transactions on Information Systems. 14 (3): 330–347. doi:10.1145/230538.230561. S2CID 207195759. Retrieved March 10, 2019.
18. ^ Gillespie, Tarleton; Boczkowski, Pablo; Foot, Kristin (2014). Media Technologies. Cambridge: MIT Press. pp. 1–30. ISBN 9780262525374.
19. ^ Diakopoulos, Nicholas. "Algorithmic Accountability: On the Investigation of Black Boxes |". towcenter.org. Retrieved November 19, 2017.
20. Lipartito, Kenneth (January 6, 2011). The Narrative and the Algorithm: Genres of Credit Reporting from the Nineteenth Century to Today (PDF) (Submitted manuscript). doi:10.2139/SSRN.1736283. S2CID 166742927. SSRN 1736283.
21. ^ Goodman, Bryce; Flaxman, Seth (2017). "EU regulations on algorithmic decision-making and a "right to explanation"". AI Magazine. 38 (3): 50. arXiv:1606.08813. doi:10.1609/aimag.v38i3.2741. S2CID 7373959.
22. ^ Weizenbaum, Joseph (1976). Computer Power and Human Reason: From Judgment to Calculation. San Francisco: W.H. Freeman. ISBN 978-0-7167-0464-5.
23. ^ Goffrey, Andrew (2008). "Algorithm". In Fuller, Matthew (ed.). Software Studies: A Lexicon. Cambridge, Mass.: MIT Press. pp. 15–20. ISBN 978-1-4356-4787-9.
24. Lowry, Stella; Macpherson, Gordon (March 5, 1988). "A Blot on the Profession". British Medical Journal. 296 (6623): 657–8. doi:10.1136/bmj.296.6623.657. PMC 2545288. PMID 3128356. Retrieved November 17, 2017.
25. "Perpetual Lineup". www.law.georgetown.edu. Retrieved December 12, 2024.
26. Buolamwini, Joy. "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification". MIT Media Lab. Retrieved December 12, 2024.
27. Barocas, Solon (December 19, 2023). Fairness and machine learning: Limitations and opportunities. The MIT Press. ISBN 9780262048613.
28. Miller, Alex P. (July 26, 2018). "Want Less-Biased Decisions? Use Algorithms". Harvard Business Review. Retrieved July 31, 2018.
29. ^ Introna, Lucas D. (December 2, 2011). "The Enframing of Code". Theory, Culture & Society. 28 (6): 113–141. doi:10.1177/0263276411418131. S2CID 145190381.
30. Bogost, Ian (January 15, 2015). "The Cathedral of Computation". The Atlantic. Retrieved November 19, 2017.
31. ^ Introna, Lucas; Wood, David (2004). "Picturing algorithmic surveillance: the politics of facial recognition systems". Surveillance & Society. 2: 177–198. Retrieved November 19, 2017.
32. ^ Introna, Lucas D. (December 21, 2006). "Maintaining the reversibility of foldings: Making the ethics (politics) of information technology visible". Ethics and Information Technology. 9 (1): 11–25. CiteSeerX 10.1.1.154.1313. doi:10.1007/s10676-006-9133-z. S2CID 17355392.
33. ^ Shirky, Clay. "A Speculative Post on the Idea of Algorithmic Authority Clay Shirky". www.shirky.com. Archived from the original on March 15, 2012. Retrieved November 20, 2017.
34. ^ Ziewitz, Malte (January 1, 2016). "Governing Algorithms: Myth, Mess, and Methods". Science, Technology, & Human Values. 41 (1): 3–16. doi:10.1177/0162243915608948. ISSN 0162-2439. S2CID 148023125.
35. Lash, Scott (June 30, 2016). "Power after Hegemony". Theory, Culture & Society. 24 (3): 55–78. doi:10.1177/0263276407075956. S2CID 145639801.
36. ^ Garcia, Megan (January 1, 2016). "Racist in the Machine". World Policy Journal. 33 (4): 111–117. doi:10.1215/07402775-3813015. S2CID 151595343.
37. "ACM FAccT 2021 Registration". fatconference.org. Retrieved November 14, 2021.
38. Ochigame, Rodrigo (December 20, 2019). "The Invention of "Ethical AI": How Big Tech Manipulates Academia to Avoid Regulation". The Intercept. Retrieved February 11, 2020.
39. Sergot, MJ; Sadri, F; Kowalski, RA; Kriwaczek, F; Hammond, P; Cory, HT (May 1986). "The British Nationality Act as a Logic Program" (PDF). Communications of the ACM. 29 (5): 370–386. doi:10.1145/5689.5920. S2CID 5665107. Retrieved November 18, 2017.
40. ^ "To stop algorithmic bias, we first have to define it". Brookings. Retrieved June 27, 2023.
41. Evans, Melanie; Mathews, Anna Wilde (October 24, 2019). "Researchers Find Racial Bias in Hospital Algorithm". Wall Street Journal. ISSN 0099-9660. Retrieved June 27, 2023.
42. ^ Luo, Queenie; Puett, Michael J.; Smith, Michael D. (May 23, 2023), A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Misplaced Pages, and YouTube, arXiv:2303.16281
43. Busker, Tony; Choenni, Sunil; Shoae Bargh, Mortaza (November 20, 2023). "Stereotypes in ChatGPT: An empirical study". Proceedings of the 16th International Conference on Theory and Practice of Electronic Governance. ICEGOV '23. New York, NY, USA: Association for Computing Machinery. pp. 24–32. doi:10.1145/3614321.3614325. ISBN 979-8-4007-0742-1.
44. Kotek, Hadas; Dockum, Rikker; Sun, David (November 5, 2023). "Gender bias and stereotypes in Large Language Models". Proceedings of the ACM Collective Intelligence Conference. CI '23. New York, NY, USA: Association for Computing Machinery. pp. 12–24. doi:10.1145/3582269.3615599. ISBN 979-8-4007-0113-9.
45. Cheng, Myra; Durmus, Esin; Jurafsky, Dan (May 29, 2023), Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models, arXiv:2305.18189
46. Friðriksdóttir, Steinunn Rut; Einarsson, Hafsteinn (2024), "Gendered Grammar or Ingrained Bias? Exploring Gender Bias in Icelandic Language Models", Lrec-Coling 2024: 7596–7610
47. Feng, Shangbin; Park, Chan Young; Liu, Yuhan; Tsvetkov, Yulia (July 2023). Rogers, Anna; Boyd-Graber, Jordan; Okazaki, Naoaki (eds.). "From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models". Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Toronto, Canada: Association for Computational Linguistics: 11737–11762. arXiv:2305.08283. doi:10.18653/v1/2023.acl-long.656.
48. Lazaro, Gina (May 17, 2024). "Understanding Gender and Racial Bias in AI". Harvard Advanced Leadership Initiative Social Impact Review. Retrieved December 11, 2024.
49. Jindal, Atin (September 5, 2022). "Misguided Artificial Intelligence: How Racial Bias is Built Into Clinical Models". Journal of Brown Hospital Medicine. 2 (1). doi:10.56305/001c.38021. Retrieved December 11, 2024.
50. Gillespie, Tarleton (June 25, 2014). "Algorithm [draft] [#digitalkeywords] – Culture Digitally". culturedigitally.org. Retrieved November 20, 2017.
51. Roth, A. E. 1524–1528. (December 14, 1990). "New physicians: A natural experiment in market organization". Science. 250 (4987): 1524–1528. Bibcode:1990Sci...250.1524R. doi:10.1126/science.2274783. PMID 2274783. S2CID 23259274. Retrieved November 18, 2017.{{cite journal}}: CS1 maint: numeric names: authors list (link)
52. Kuang, Cliff (November 21, 2017). "Can A.I. Be Taught to Explain Itself?". The New York Times Magazine. Retrieved November 26, 2017.
53. ^ Jouvenal, Justin (November 17, 2016). "Police are using software to predict crime. Is it a 'holy grail' or biased against minorities?". Washington Post. Retrieved November 25, 2017.
54. ^ Chamma, Maurice (February 3, 2016). "Policing the Future". The Marshall Project. Retrieved November 25, 2017.
55. Lum, Kristian; Isaac, William (October 2016). "To predict and serve?". Significance. 13 (5): 14–19. doi:10.1111/j.1740-9713.2016.00960.x.
56. Smith, Jack (October 9, 2016). "Predictive policing only amplifies racial bias, study shows". Mic. Retrieved November 25, 2017.
57. Lum, Kristian; Isaac, William (October 1, 2016). "FAQs on Predictive Policing and Bias". HRDAG. Retrieved November 25, 2017.
58. Sun, Wenlong; Nasraoui, Olfa; Shafto, Patrick (2018). "Iterated Algorithmic Bias in the Interactive Machine Learning Process of Information Filtering". Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Seville, Spain: SCITEPRESS - Science and Technology Publications. pp. 110–118. doi:10.5220/0006938301100118. ISBN 9789897583308.
59. Sinha, Ayan; Gleich, David F.; Ramani, Karthik (August 9, 2018). "Gauss's law for networks directly reveals community boundaries". Scientific Reports. 8 (1): 11909. Bibcode:2018NatSR...811909S. doi:10.1038/s41598-018-30401-0. ISSN 2045-2322. PMC 6085300. PMID 30093660.
60. Hao, Karen (February 2018). "Google is finally admitting it has a filter-bubble problem". Quartz. Retrieved February 26, 2019.
61. "Facebook Is Testing This New Feature to Fight 'Filter Bubbles'". Fortune. Retrieved February 26, 2019.
62. ^ Sandvig, Christian; Hamilton, Kevin; Karahalios, Karrie; Langbort, Cedric (May 22, 2014). "Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms" (PDF). 64th Annual Meeting of the International Communication Association. Retrieved November 18, 2017.
63. Brin, Sergey; Page, Lawrence. "The Anatomy of a Search Engine". www7.scu.edu.au. Archived from the original on July 2, 2019. Retrieved November 18, 2017.
64. Epstein, Robert; Robertson, Ronald E. (August 18, 2015). "The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections". Proceedings of the National Academy of Sciences. 112 (33): E4512–E4521. Bibcode:2015PNAS..112E4512E. doi:10.1073/pnas.1419828112. PMC 4547273. PMID 26243876.
65. Bond, Robert M.; Fariss, Christopher J.; Jones, Jason J.; Kramer, Adam D. I.; Marlow, Cameron; Settle, Jaime E.; Fowler, James H. (September 13, 2012). "A 61-million-person experiment in social influence and political mobilization". Nature. 489 (7415): 295–8. Bibcode:2012Natur.489..295B. doi:10.1038/nature11421. ISSN 0028-0836. PMC 3834737. PMID 22972300.
66. Zittrain, Jonathan (2014). "Engineering an Election" (PDF). Harvard Law Review Forum. 127: 335–341. Archived from the original (PDF) on March 4, 2021. Retrieved November 19, 2017.
67. Day, Matt (August 31, 2016). "How LinkedIn's search engine may reflect a gender bias". The Seattle Times. Retrieved November 25, 2017.
68. ^ Crawford, Kate; Schultz, Jason (2014). "Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms". Boston College Law Review. 55 (1): 93–128. Retrieved November 18, 2017.
69. Duhigg, Charles (February 16, 2012). "How Companies Learn Your Secrets". The New York Times Magazine. Retrieved November 18, 2017.
70. Noble, Safiya (2012). "Missed Connections: What Search Engines Say about Women" (PDF). Bitch. 12 (4): 37–41.
71. Guynn, Jessica (March 16, 2017). "Google starts flagging offensive content in search results". USA TODAY. USA Today. Retrieved November 19, 2017.
72. Simonite, Tom. "Study Suggests Google's Ad-Targeting System May Discriminate". MIT Technology Review. Massachusetts Institute of Technology. Retrieved November 17, 2017.
73. Prates, Marcelo O. R.; Avelar, Pedro H. C.; Lamb, Luis (2018). "Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate". arXiv:1809.02208 .
74. Prates, Marcelo O. R.; Avelar, Pedro H.; Lamb, Luís C. (2019). "Assessing gender bias in machine translation: A case study with Google Translate". Neural Computing and Applications. 32 (10): 6363–6381. arXiv:1809.02208. doi:10.1007/s00521-019-04144-6. S2CID 52179151.
75. Claburn, Thomas (September 10, 2018). "Boffins bash Google Translate for sexism". The Register. Retrieved April 28, 2022.
76. Dastin, Jeffrey (October 9, 2018). "Amazon scraps secret AI recruiting tool that showed bias against women". Reuters.
77. Vincent, James (October 10, 2018). "Amazon reportedly scraps internal AI recruiting tool that was biased against women". The Verge.
78. "Reflecting on Spotify's Recommender System – SongData". October 2019. Retrieved August 7, 2020.
79. Buolamwini, Joy; Gebru, Timnit (January 21, 2018). "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification". Proceedings of Machine Learning Research. 81 (2018): 77–91. Retrieved September 27, 2020.
80. Noble, Safiya Umoja (February 20, 2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press. ISBN 978-1479837243.
81. ^ Nakamura, Lisa (2009). Magnet, Shoshana; Gates, Kelly (eds.). The New Media of Surveillance. London: Routledge. pp. 149–162. ISBN 978-0-415-56812-8.
82. Marco Marabelli; Sue Newell; Valerie Handunge (2021). "The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges". Journal of Strategic Information Systems. 30 (3): 1–15. doi:10.1016/j.jsis.2021.101683.
83. Alexander, Rudolph; Gyamerah, Jacquelyn (September 1997). "Differential Punishing of African Americans and Whites Who Possess Drugs: A Just Policy or a Continuation of the Past?". Journal of Black Studies. 28 (1): 97–111. doi:10.1177/002193479702800106. ISSN 0021-9347. S2CID 152043501.
84. Petersilia, Joan (January 1985). "Racial Disparities in the Criminal Justice System: A Summary". Crime & Delinquency. 31 (1): 15–34. doi:10.1177/0011128785031001002. ISSN 0011-1287. S2CID 146588630.
85. ^ Guynn, Jessica (July 1, 2015). "Google Photos labeled black people 'gorillas'". USA TODAY. USA Today. Retrieved November 18, 2017.
86. Rose, Adam (January 22, 2010). "Are Face-Detection Cameras Racist?". Time. Retrieved November 18, 2017.
87. "Alexa does not understand your accent". Washington Post.
88. Sweeney, Latanya (January 28, 2013). "Discrimination in Online Ad Delivery". arXiv:1301.6822 .
89. Braun, Lundy (2015). "Race, ethnicity and lung function: A brief history". Canadian Journal of Respiratory Therapy. 51 (4): 99–101. ISSN 1205-9838. PMC 4631137. PMID 26566381.
90. Robinson, Whitney R; Renson, Audrey; Naimi, Ashley I (April 1, 2020). "Teaching yourself about structural racism will improve your machine learning". Biostatistics. 21 (2): 339–344. doi:10.1093/biostatistics/kxz040. ISSN 1465-4644. PMC 7868043. PMID 31742353.
91. Johnson, Carolyn Y. (October 24, 2019). "Racial bias in a medical algorithm favors white patients over sicker black patients". Washington Post. Retrieved October 28, 2019.
92. Bartlett, Robert; Morse, Adair; Stanton, Richard; Wallace, Nancy (June 2019). "Consumer-Lending Discrimination in the FinTech Era". NBER Working Paper No. 25943. Working Paper Series. doi:10.3386/w25943. S2CID 242410791.
93. Hofmann, V., Kalluri, P.R., Jurafsky, D. et al. AI generates covertly racist decisions about people based on their dialect. Nature 633, 147–154 (2024). https://doi.org/10.1038/s41586-024-07856-5
94. Jeff Larson, Julia Angwin (May 23, 2016). "How We Analyzed the COMPAS Recidivism Algorithm". ProPublica. Archived from the original on April 29, 2019. Retrieved June 19, 2020.
95. "Commentary: Bad news. Artificial intelligence is biased". CNA. January 12, 2019. Archived from the original on January 12, 2019. Retrieved June 19, 2020.
96. ^ Angwin, Julia; Larson, Jeff; Mattu, Surya; Kirchner, Lauren (May 23, 2016). "Machine Bias — ProPublica". ProPublica. Retrieved November 18, 2017.
97. Harcourt, Bernard (September 16, 2010). "Risk as a Proxy for Race". Federal Sentencing Reporter. 27 (4): 237. doi:10.1525/fsr.2015.27.4.237. S2CID 53611813. SSRN 1677654.
98. Skeem J, Lowenkamp C, Risk, Race, & Recidivism: Predictive Bias and Disparate Impact, (June 14, 2016). SSRN 2687339
99. Thomas, C.; Nunez, A. (2022). "Automating Judicial Discretion: How Algorithmic Risk Assessments in Pretrial Adjudications Violate Equal Protection Rights on the Basis of Race". Law & Inequality. 40 (2): 371–407. doi:10.24926/25730037.649.
100. ^ Angwin, Julia; Grassegger, Hannes (June 28, 2017). "Facebook's Secret Censorship Rules Protect White Men From Hate Speech But Not Black Children — ProPublica". ProPublica. Retrieved November 20, 2017.
101. Angwin, Julia; Varner, Madeleine; Tobin, Ariana (September 14, 2017). "Facebook Enabled Advertisers to Reach 'Jew Haters' — ProPublica". ProPublica. Retrieved November 20, 2017.
102. Sap, Maarten; Card, Dallas; Gabriel, Saadia; Choi, Yejin; Smith, Noah A. (July 28 – August 2, 2019). "The Risk of Racial Bias in Hate Speech Detection" (PDF). Proceedings of the 57th Annual Meeting of the Association for Computational Linguist. Florence, Italy: Association for Computational Linguistics. pp. 1668–1678. Archived (PDF) from the original on August 14, 2019.
103. Ghaffary, Shirin (August 15, 2019). "The algorithms that detect hate speech online are biased against black people". Vox. Retrieved February 19, 2020.
104. ^ Nakajima Wickham, E., & Öhman, E. (2022). Hate speech, censorship, and freedom of speech: The changing policies of reddit. Journal of Data Mining & Digital Humanities, NLP4DH. https://doi.org/10.46298/jdmdh.9226
105. Furl, N (December 2002). "Face recognition algorithms and the other-race effect: computational mechanisms for a developmental contact hypothesis". Cognitive Science. 26 (6): 797–815. doi:10.1207/s15516709cog2606_4.
106. Raji, Inioluwa Deborah; Gebru, Timnit; Mitchell, Margaret; Buolamwini, Joy; Lee, Joonseok; Denton, Emily (February 7, 2020). "Saving Face". Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery. pp. 145–151. arXiv:2001.00964. doi:10.1145/3375627.3375820. ISBN 9781450371100. S2CID 209862419.
107. "Facial Recognition Is Accurate, if You're a White Guy". The New York Times. February 9, 2018. Retrieved August 24, 2023.
108. Buolamwini, Joy; Gebru, Timnit (2018). "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification" (PDF). Proceedings of Machine Learning Research. 81: 1 – via MLR Press.
109. Ananny, Mike (April 14, 2011). "The Curious Connection Between Apps for Gay Men and Sex Offenders". The Atlantic. Retrieved November 18, 2017.
110. Kafka, Peter. "Did Amazon Really Fail This Weekend? The Twittersphere Says 'Yes,' Online Retailer Says 'Glitch.'". AllThingsD. Retrieved November 22, 2017.
111. Kafka, Peter. "Amazon Apologizes for 'Ham-fisted Cataloging Error'". AllThingsD. Retrieved November 22, 2017.
112. Matsakis, Louise (February 22, 2019). "A 'Sexist' Search Bug Says More About Us Than Facebook". Wired. ISSN 1059-1028. Retrieved February 26, 2019.
113. "Some AI just shouldn't exist". April 19, 2019.
114. Samuel, Sigal (April 19, 2019). "Some AI just shouldn't exist". Vox. Retrieved December 12, 2019.
115. Wang, Yilun; Kosinski, Michal (February 15, 2017). "Deep neural networks are more accurate than humans at detecting sexual orientation from facial images". OSF. doi:10.17605/OSF.IO/ZN79K.
116. Levin, Sam (September 9, 2017). "LGBT groups denounce 'dangerous' AI that uses your face to guess sexuality". The Guardian. ISSN 0261-3077. Retrieved December 12, 2019.
117. Pal, G.C. (September 16, 2011). "Disability, Intersectionality and Deprivation: An Excluded Agenda". Psychology and Developing Societies. 23(2), 159–176. doi:10.1177/097133361102300202. S2CID 147322669 – via Sagepub.
118. Brinkman, Aurora H.; Rea-Sandin, Gianna; Lund, Emily M.; Fitzpatrick, Olivia M.; Gusman, Michaela S.; Boness, Cassandra L.; Scholars for Elevating Equity and Diversity (SEED) (October 20, 2022). "Shifting the discourse on disability: Moving to an inclusive, intersectional focus". American Journal of Orthopsychiatry. 93 (1): 50–62. doi:10.1037/ort0000653. ISSN 1939-0025. PMC 9951269. PMID 36265035.
119. Whittaker, Meredith (November 2019). "Disability, Bias, and AI" (PDF). Archived from the original (PDF) on March 27, 2023. Retrieved December 2, 2022.
120. "Mission — Disability is Diversity — Dear Entertainment Industry, THERE'S NO DIVERSITY, EQUITY & INCLUSION WITHOUT DISABILITY". Disability is Diversity. Retrieved December 2, 2022.
121. "Microsoft Design". www.microsoft.com. Retrieved December 2, 2022.
122. Pulrang, Andrew. "4 Ways To Understand The Diversity Of The Disability Community". Forbes. Retrieved December 2, 2022.
123. Watermeyer, Brian; Swartz, Leslie (October 12, 2022). "Disability and the problem of lazy intersectionality". Disability & Society. 38 (2): 362–366. doi:10.1080/09687599.2022.2130177. ISSN 0968-7599. S2CID 252959399.
124. "Disability Data Report 2021". Disability Data Initiative. Retrieved December 2, 2022.
125. White, Jason J. G. (March 2, 2020). "Fairness of AI for people with disabilities: problem analysis and interdisciplinary collaboration". ACM SIGACCESS Accessibility and Computing (125): 3:1. doi:10.1145/3386296.3386299. ISSN 1558-2337. S2CID 211723415.
126. "AI language models show bias against people with disabilities, study finds | Penn State University". www.psu.edu. Retrieved December 2, 2022.
127. Givens, Alexandra Reeve (February 6, 2020). "How Algorithmic Bias Hurts People With Disabilities". Slate Magazine. Retrieved December 2, 2022.
128. Morris, Meredith Ringel (May 22, 2020). "AI and accessibility". Communications of the ACM. 63 (6): 35–37. arXiv:1908.08939. doi:10.1145/3356727. ISSN 0001-0782. S2CID 201645229.
129. Noble, Safiya Umoja (February 20, 2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York. ISBN 9781479837243. OCLC 987591529.{{cite book}}: CS1 maint: location missing publisher (link)
130. Castelnovo, Alessandro; Inverardi, Nicole; Nanino, Gabriele; Penco, Ilaria; Regoli, Daniele (2023). "Fair Enough? A map of the current limitations to the requirements to have "fair" algorithms". arXiv:2311.12435 .
131. Ruggieri, Salvatore; Alvarez, Jose M; Pugnana, Andrea; Turini, Franco (2023). "Can We Trust Fair-AI?". Proceedings of the AAAI Conference on Artificial Intelligence. 37 (13): 5421–15430. doi:10.1609/aaai.v37i13.26798. hdl:11384/136444. S2CID 259678387.
132. Samuel, Sigal (April 19, 2022). "Why it's so damn hard to make AI fair and unbiased". Vox. Retrieved July 23, 2024.
133. Fioretto, Ferdinando (March 19, 2024). "Building fairness into AI is crucial – and hard to get right". The Conversation. Retrieved July 23, 2024.
134. Friedler, Sorelle A.; Scheidegger, Carlos; Venkatasubramanian, Suresh (2016). "On the (im)possibility of fairness". arXiv:1609.07236 .
135. Hu, Lily; Chen, Yiling (2018). "Welfare and Distributional Impacts of Fair Classification". arXiv:1807.01134 .
136. Dwork, Cynthia; Hardt, Moritz; Pitassi, Toniann; Reingold, Omer; Zemel, Rich (November 28, 2011). "Fairness Through Awareness". arXiv:1104.3913 .
137. ^ Sandvig, Christian; Hamilton, Kevin; Karahalios, Karrie; Langbort, Cedric (2014). Gangadharan, Seeta Pena; Eubanks, Virginia; Barocas, Solon (eds.). "An Algorithm Audit" (PDF). Data and Discrimination: Collected Essays.
138. LaFrance, Adrienne (September 18, 2015). "The Algorithms That Power the Web Are Only Getting More Mysterious". The Atlantic. Retrieved November 19, 2017.
139. Bruno Latour (1999). Pandora's Hope: Essays On the Reality of Science Studies. Cambridge, Massachusetts: Harvard University Press.
140. Kubitschko, Sebastian; Kaun, Anne (2016). Innovative Methods in Media and Communication Research. Springer. ISBN 978-3-319-40700-5. Retrieved November 19, 2017.
141. McGee, Matt (August 16, 2013). "EdgeRank Is Dead: Facebook's News Feed Algorithm Now Has Close To 100K Weight Factors". Marketing Land. Retrieved November 18, 2017.
142. ^ Granka, Laura A. (September 27, 2010). "The Politics of Search: A Decade Retrospective" (PDF). The Information Society. 26 (5): 364–374. doi:10.1080/01972243.2010.511560. S2CID 16306443. Retrieved November 18, 2017.
143. Szymielewicz, Katarzyna (January 20, 2020). "Black-Boxed Politics". Medium. Retrieved February 11, 2020.
144. Veale, Michael; Binns, Reuben (2017). "Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data". Big Data & Society. 4 (2): 205395171774353. doi:10.1177/2053951717743530. SSRN 3060763.
145. Elliott, Marc N.; Morrison, Peter A.; Fremont, Allen; McCaffrey, Daniel F.; Pantoja, Philip; Lurie, Nicole (June 2009). "Using the Census Bureau's surname list to improve estimates of race/ethnicity and associated disparities". Health Services and Outcomes Research Methodology. 9 (2): 69–83. doi:10.1007/s10742-009-0047-1. ISSN 1387-3741. S2CID 43293144.
146. Chen, Jiahao; Kallus, Nathan; Mao, Xiaojie; Svacha, Geoffry; Udell, Madeleine (2019). "Fairness Under Unawareness". Proceedings of the Conference on Fairness, Accountability, and Transparency. Atlanta, GA, USA: ACM Press. pp. 339–348. arXiv:1811.11154. doi:10.1145/3287560.3287594. ISBN 9781450361255. S2CID 58006233.
147. Kilbertus, Niki; Gascon, Adria; Kusner, Matt; Veale, Michael; Gummadi, Krishna; Weller, Adrian (2018). "Blind Justice: Fairness with Encrypted Sensitive Attributes". International Conference on Machine Learning: 2630–2639. arXiv:1806.03281. Bibcode:2018arXiv180603281K.
148. Binns, Reuben; Veale, Michael; Kleek, Max Van; Shadbolt, Nigel (September 13, 2017). "Like Trainer, Like Bot? Inheritance of Bias in Algorithmic Content Moderation". Social Informatics. Lecture Notes in Computer Science. Vol. 10540. pp. 405–415. arXiv:1707.01477. doi:10.1007/978-3-319-67256-4_32. ISBN 978-3-319-67255-7. S2CID 2814848.
149. Claburn, Thomas (July 18, 2016). "EU Data Protection Law May End The Unknowable Algorithm – InformationWeek". InformationWeek. Retrieved November 25, 2017.
150. ^ Jobin, Anna; Ienca, Marcello; Vayena, Effy (September 2, 2019). "The global landscape of AI ethics guidelines". Nature Machine Intelligence. 1 (9): 389–399. arXiv:1906.11668. doi:10.1038/s42256-019-0088-2. S2CID 201827642.
151. https://research.google.com/bigpicture/attacking-discrimination-in-ml/ Attacking discrimination with smarter machine learning
152. Hardt, Moritz; Price, Eric; Srebro, Nathan (2016). "Equality of Opportunity in Supervised Learning". arXiv:1610.02413 .
153. https://venturebeat.com/2018/05/25/microsoft-is-developing-a-tool-to-help-engineers-catch-bias-in-algorithms/ Microsoft is developing a tool to help engineers catch bias in algorithms
154. "Facebook says it has a tool to detect bias in its artificial intelligence". Quartz. May 3, 2018. Archived from the original on March 5, 2023.
155. open source Pymetrics audit-ai
156. https://venturebeat-com.cdn.ampproject.org/c/s/venturebeat.com/2018/05/31/pymetrics-open-sources-audit-ai-an-algorithm-bias-detection-tool/amp/ Pymetrics open-sources Audit AI, an algorithm bias detection tool
157. https://github.com/dssg/aequitas open source Aequitas: Bias and Fairness Audit Toolkit
158. https://dsapp.uchicago.edu/aequitas/ open-sources Audit AI, Aequitas at University of Chicago
159. https://www.ibm.com/blogs/research/2018/02/mitigating-bias-ai-models/ Mitigating Bias in AI Models
160. S. Sen, D. Dasgupta and K. D. Gupta, "An Empirical Study on Algorithmic Bias", 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 2020, pp. 1189-1194, doi:10.1109/COMPSAC48688.2020.00-95.
161. Zou, James; Schiebinger, Londa (July 2018). "AI can be sexist and racist — it's time to make it fair". Nature. 559 (7714): 324–326. Bibcode:2018Natur.559..324Z. doi:10.1038/d41586-018-05707-8. PMID 30018439.
162. Jia, Sen; Welfare, Thomas; Cristianini, Nello (2018). Right for the right reason: Training agnostic networks. International Symposium on Intelligent Data Analysis. Springer.
163. Sutton, Adam; Welfare, Thomas; Cristianini, Nello (2018). Biased embeddings from wild data: Measuring, understanding and removing. International Symposium on Intelligent Data Analysis. Springer.
164. "Software & Systems Engineering Standards Committee". April 17, 2018.
165. ^ Koene, Ansgar (June 2017). "Algorithmic Bias: Addressing Growing Concerns [Leading Edge]" (PDF). IEEE Technology and Society Magazine. 36 (2): 31–32. doi:10.1109/mts.2017.2697080. ISSN 0278-0097. Archived from the original (PDF) on July 19, 2018. Retrieved August 1, 2019.
166. ^ "P7003 - Algorithmic Bias Considerations". IEEE. Archived from the original on December 3, 2018. Retrieved December 3, 2018.
167. "Software & Systems Engineering Standards Committee". April 17, 2018.
168. IEEE (2022). "IEEE CertifAIEd™ – Ontological Specification for Ethical Algorithmic Bias" (PDF). IEEE CertifAIEd™ – Ontological Specification for Ethical Algorithmic Bias.
169. The Internet Society (April 18, 2017). "Artificial Intelligence and Machine Learning: Policy Paper". Internet Society. Retrieved February 11, 2020.
170. ^ "White Paper: How to Prevent Discriminatory Outcomes in Machine Learning". World Economic Forum. March 12, 2018. Retrieved February 11, 2020.
171. "Explainable Artificial Intelligence". www.darpa.mil. Retrieved February 11, 2020.
172. PricewaterhouseCoopers. "The responsible AI framework". PwC. Retrieved February 11, 2020.
173. Heald, David (September 7, 2006). Transparency: The Key to Better Governance?. British Academy. doi:10.5871/bacad/9780197263839.003.0002. ISBN 978-0-19-726383-9.
174. Kemper, Jakko; Kolkman, Daan (December 6, 2019). "Transparent to whom? No algorithmic accountability without a critical audience". Information, Communication & Society. 22 (14): 2081–2096. doi:10.1080/1369118X.2018.1477967. hdl:11245.1/75cb1256-5fe5-4724-9a63-03ef66032d8e. ISSN 1369-118X.
175. "The Toronto Declaration: Protecting the rights to equality and non-discrimination in machine learning systems". Human Rights Watch. July 3, 2018. Retrieved February 11, 2020.
176. The Toronto Declaration: Protecting the Right to Equality and Non-Discrimination in Machine Learning Systems (PDF). Human Rights Watch. 2018. p. 15.
177. Floridi, Luciano; Cowls, Josh; Beltrametti, Monica; Chatila, Raja; Chazerand, Patrice; Dignum, Virginia; Luetge, Christoph; Madelin, Robert; Pagallo, Ugo; Rossi, Francesca; Schafer, Burkhard (December 1, 2018). "AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations". Minds and Machines. 28 (4): 703. doi:10.1007/s11023-018-9482-5. ISSN 1572-8641. PMC 6404626. PMID 30930541.
178. Crawford, Kate (June 25, 2016). "Opinion | Artificial Intelligence's White Guy Problem". The New York Times. ISSN 0362-4331. Retrieved February 11, 2020.
179. "AI Is the Future—But Where Are the Women?". Wired. ISSN 1059-1028. Retrieved February 11, 2020.
180. Snow, Jackie. ""We're in a diversity crisis": cofounder of Black in AI on what's poisoning algorithms in our lives". MIT Technology Review. Retrieved February 11, 2020.
181. Hao, Karen (June 14, 2021). "Inside the fight to reclaim AI from Big Tech's control". MIT Technology Review. Retrieved June 21, 2021.
182. Ciston, Sarah (December 29, 2019). "Intersectional AI Is Essential". Journal of Science and Technology of the Arts. 11 (2): 3–8. doi:10.7559/citarj.v11i2.665. ISSN 2183-0088.
183. D'Ignazio, Catherine; Klein, Lauren F. (2020). Data Feminism. MIT Press. ISBN 978-0262044004.
184. Cave, Stephen; Dihal, Kanta (August 6, 2020). "The Whiteness of AI". Philosophy & Technology. 33 (4): 685–703. doi:10.1007/s13347-020-00415-6. ISSN 2210-5441.
185. Bondi, Elizabeth; Xu, Lily; Acosta-Navas, Diana; Killian, Jackson A. (2021). "Envisioning Communities: A Participatory Approach Towards AI for Social Good". Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 425–436. arXiv:2105.01774. doi:10.1145/3461702.3462612. ISBN 9781450384735. S2CID 233740121. Retrieved April 6, 2023 – via ezpa.library.ualberta.ca.
186. University, Stanford (March 18, 2019). "Stanford University launches the Institute for Human-Centered Artificial Intelligence". Stanford News. Retrieved April 6, 2023.
187. Bondi, Elizabeth; Xu, Lily; Acosta-Navas, Diana; Killian, Jackson A. (July 21, 2021). "Envisioning Communities: A Participatory Approach Towards AI for Social Good". Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 425–436. arXiv:2105.01774. doi:10.1145/3461702.3462612. ISBN 9781450384735. S2CID 233740121.
188. Bygrave, Lee A (2001). "Automated Profiling". Computer Law & Security Review. 17 (1): 17–24. doi:10.1016/s0267-3649(01)00104-2.
189. ^ Veale, Michael; Edwards, Lilian (2018). "Clarity, Surprises, and Further Questions in the Article 29 Working Party Draft Guidance on Automated Decision-Making and Profiling" (PDF). Computer Law & Security Review. 34 (2): 398–404. doi:10.1016/j.clsr.2017.12.002. S2CID 4797884. SSRN 3071679.
190. Wachter, Sandra; Mittelstadt, Brent; Floridi, Luciano (May 1, 2017). "Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation". International Data Privacy Law. 7 (2): 76–99. doi:10.1093/idpl/ipx005. ISSN 2044-3994.
191. Edwards, Lilian; Veale, Michael (May 23, 2017). "Slave to the Algorithm? Why a Right to an Explanation Is Probably Not the Remedy You Are Looking For". Duke Law & Technology Review. 16: 18–84. SSRN 2972855.
192. ^ Singer, Natasha (February 2, 2013). "Consumer Data Protection Laws, an Ocean Apart". The New York Times. Retrieved November 26, 2017.
193. Obama, Barack (October 12, 2016). "The Administration's Report on the Future of Artificial Intelligence". whitehouse.gov. National Archives. Retrieved November 26, 2017.
194. and Technology Council, National Science (2016). National Artificial Intelligence Research and Development Strategic Plan (PDF). US Government. Retrieved November 26, 2017.
195. Kirchner, Lauren (December 18, 2017). "New York City Moves to Create Accountability for Algorithms — ProPublica". ProPublica. Retrieved July 28, 2018.
196. "The New York City Council - File #: Int 1696-2017". legistar.council.nyc.gov. New York City Council. Retrieved July 28, 2018.
197. Powles, Julia. "New York City's Bold, Flawed Attempt to Make Algorithms Accountable". The New Yorker. Retrieved July 28, 2018.
198. "Maintaining American Leadership in Artificial Intelligence". February 14, 2019.
199. "Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence". November 2023.
200. "VP Kamala Harris Unveils "Safe, Secure & Responsible" AI Guidelines for Federal Agencies". March 28, 2024.
201. "India Weighs Comprehensive Data Privacy Bill, Similar to EU's GDPR". Insurance Journal. July 31, 2018. Retrieved February 26, 2019.
202. "The Personal Data Protection Bill, 2018" (PDF). Ministry of Electronics & Information Technology, Government of India. 2018. Retrieved April 29, 2022.

Further reading

• Baer, Tobias (2019). Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists. New York: Apress. ISBN 9781484248843.
• Noble, Safiya Umoja (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press. ISBN 9781479837243.

• Machine learning
• Information ethics
• Computing and society
• Philosophy of artificial intelligence
• Discrimination
• Bias