Bias in Search Engines And Algorithms

This article discusses the story behind a group of Dartmouth College's proposal to the Library of Congress to change the "illegal alien" subject heading to "undocumented immigrant." At first, the Library of Congress was going to change the heading, but the GOP moved to block this change.

In an effort to avoid problematic language, the University of Colorado Boulder added several new immigration subject headings to its library database, such as“noncitizens” and “undocumented immigrants.”

Webber tells the stories of Cathy O'Neil, Frida Polli, Rediet Abebe, Tess Posner, and Chad Steelberg. These founders and CEOs have created organizations dedicated to algorithm auditing, open source AI auditing, empowering marginalized groups to become computer programmers, and building better AI.

Themis-ml is an open-source machine-learning library is free for everyone to use. It defines discrimination as "the preference (bias) for or against a set of social groups that result in the unfair treatment of its members with respect to some outcome." It defines fairness as "the opposite of discrimination, and in the context of a machine learning algorithm, this is measured by the degree to which the algorithm’s predictions favor one social group over another in relation to an outcome that holds socioeconomic, political, or legal importance, e.g. the denial/approval of a loan application."

An algorithm is “fair” depending on the individual's definition of fairness, the intended outcome, and the social attributes relevant to potential discriminatory situations.

The AI Now Institute, an interdisciplinary research center at New York University, calls on public agencies to only use transparent, impartial software algorithms in their practices. The Institute outlines a framework called Algorithmic Impact Assessments (AIAs) to achieve this end.

Spoken word poet Joy Buolamwini delivers a poem about algorithmic bias as it relates to the shortcomings of facial recognition software.