Excerpts from the article:
The majority of algorithms developed to enforce “algorithmic fairness” were built without #policy and societal contexts in mind.
Our motivation for pursuing fairness is to improve the situation of a historically disadvantaged group.
When we build AI systems to make decisions about people's lives, our design decisions encode implicit value judgments about what should be prioritized.
Technical solutions are often only a Band-aid to deal with a broken system. Improving access to #HealthCare, curating more diverse data sets, and developing tools that specifically target the problems faced by historically disadvantaged communities can help make substantive fairness a reality.
#AI systems make life-changing decisions. Choices about how they should be fair, and to whom, are too important to treat #fairness as a simple mathematical problem to be solved.
#AlgorithmicFairness #MedicalSystem #AIEthics #FairML #ArtificialIntelligence
Article:
HealthCare #Bias Is Dangerous. But So Are ‘Fairness’ #Algorithms
https://www-wired-com.cdn.ampproject.org/c/s/www.wired.com/story/bias-statistics-artificial-intelligence-healthcare/amp
Paper:
The Unfairness of Fair #MachineLearning: Levelling down and strict egalitarianism by default
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4331652