21 Facts About Algorithmic bias


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.

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For example, algorithmic bias has been observed in search engine results and social media platforms.

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The study of algorithmic bias is most concerned with algorithms that reflect "systematic and unfair" discrimination.

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Algorithmic bias has been cited in cases ranging from election outcomes to the spread of online hate speech.

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The term algorithmic bias describes systematic and repeatable errors that create unfair outcomes, such as privileging one arbitrary group of users over others.

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Machine learning

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.

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In recent years, when more algorithms started to use machine learning methods on real world data, algorithmic bias can be found more often due to the bias existing in the data.

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The complexity of analyzing algorithmic bias has grown alongside the complexity of programs and their design.

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Pre-existing Algorithmic bias in an algorithm is a consequence of underlying social and institutional ideologies.

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An example of this form of Algorithmic bias is the British Nationality Act Program, designed to automate the evaluation of new British citizens after the 1981 British Nationality Act.

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Such Algorithmic bias can 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.

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Lastly, technical Algorithmic bias can be created by attempting to formalize decisions into concrete steps on the assumption that human behavior works in the same way.

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Emergent Algorithmic bias is the result of the use and reliance on algorithms across new or unanticipated contexts.

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In 1990, an example of emergent Algorithmic bias was identified in the software used to place US medical students into residencies, the National Residency Match Program .

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Emergent Algorithmic bias can occur when an algorithm is used by unanticipated audiences.

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Algorithmic bias said this was the result of an analysis of users' interactions with the site.

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Algorithmic bias's design allowed ad buyers to block African-Americans from seeing housing ads.

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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.

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Algorithmic bias processes are complex, often exceeding the understanding of the people who use them.

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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.

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Study of 84 policy guidelines on ethical AI found that fairness and "mitigation of unwanted Algorithmic 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.

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