# discrimination Published articles for discrimination. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## One definition of algorithmic fairness: statistical parity DevFeed: [One definition of algorithmic fairness: statistical parity]() Original publisher: [Read original article]() Published: 2015-10-19T09:00:00Z Content type: opinion Language: en Sources: [Jeremy Kun]() Topics: [Algorithms](), [Algorithms, Complexity]() Tags: [algorithm](), [algorithms](), [bias](), [conditional-probability](), [discrimination](), [fairness](), [machine-learning](), [mathematics](), [programming](), [python](), [research](), [reverse-tokenism](), [self-fulfilling-prophecy](), [statistics]() ### AI overview The article examines statistical parity as one mathematical definition of algorithmic fairness. It explains the protected-group and population model, defines bias as the difference in positive classification rates between the complement and the protected group, and discusses the definition's intuitive basis and limitations. ### Source excerpt If you haven't read the first post on fairness, I suggest you go back and read it because it motivates why we're talking about fairness for algorithms in the first place. In this post I'll describe one of the existing mathematical definitions of "fairness," its origin, and discuss its strengths and shortcomings. Before jumping in I should remark that nobody has found a definition which is widely agreed as a good definition of fairness in the same way we have for, say, the security of a random number generator. ## What does it mean for an algorithm to be fair? DevFeed: [What does it mean for an algorithm to be fair?]() Original publisher: [Read original article]() Published: 2015-07-13T09:00:00Z Content type: opinion Language: en Sources: [Jeremy Kun]() Topics: [Algorithms](), [Algorithms, Complexity](), [Machine learning](), [Programming]() Tags: [accountability](), [algorithm](), [algorithms](), [data-mining](), [deep-learning](), [discrimination](), [disparate-impact](), [fairness](), [google](), [law](), [machine-learning](), [mathematics](), [neural-networks](), [transparency]() ### AI overview The article examines algorithmic fairness and argues that algorithms trained on historical human data can facilitate illegal discrimination and reinforce social prejudices. It uses targeted loan advertising, Google autocomplete, and predictive policing as examples, though the supplied text is incomplete. ### Source excerpt In 2014 the White House commissioned a 90-day study that culminated in a report (pdf) on the state of "big data" and related technologies. The authors give many recommendations, including this central warning. Warning: algorithms can facilitate illegal discrimination! Here's a not-so-imaginary example of the problem. A bank wants people to take loans with high interest rates, and it also serves ads for these loans. A modern idea is to use an algorithm to decide, based on the sliver of known information about a user visiting a website, which advertisement to present that gives the largest chance of the user clicking on it. ## Inclusiveness at Mozilla DevFeed: [Inclusiveness at Mozilla]() Original publisher: [Read original article]() Author: Brendan Eich Published: 2014-03-26T18:57:08Z Content type: opinion Language: en Sources: [Brendan Eich]() Topics: [Mozilla](), [Open Source]() Tags: [discrimination](), [gender](), [inclusive](), [learn](), [mozilla](), [uncategorized]() ### AI overview Brendan Eich addresses concerns about his commitment to equality and welcome for LGBT individuals at Mozilla, outlining commitments to inclusive employment, events, community participation, policies, benefits, and outreach to marginalized contributors. He also accepts accountability for his actions as CEO. ### Source excerpt I am deeply honored and humbled by the CEO role. I'm also grateful for the messages of support. At the same time, I know there are concerns about my commitment to fostering equality and welcome for LGBT individuals at Mozilla. I hope to lay those concerns to rest, first by making a set of commitments ... Continue reading "Inclusiveness at Mozilla"