Algorithmic Bias
Algorithmic bias describes systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. 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 (2018) and the proposed Artificial Intelligence Act (2021).
Source: Algorithmic Bias | WikipediaText To Speech
Other Links ︎︎︎
- How Do Biased Algorithms Damage Marginalized Communities? | NPR
- Why Algorithms Can Be Racist and Sexist | Vox
- What is AI Bias Really | ITRex
- Proxy Discrimination in the Age of Artificial Intelligence and Big Data | Iowa Law Review
- Biased Algorithms Learn From Biased Data | Forbes
- What is Algorithmic Bias | Anti-Defamation League