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Found 5 papers, 2 papers with code
The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default
Many current fairness measures suffer from both fairness and performance degradation, or "levelling down," where fairness is achieved by making every group worse off, or by bringing better performing groups down to the level of the worst off.
The Theory of Artificial Immutability: Protecting Algorithmic Groups Under Anti-Discrimination Law
Artificial Intelligence (AI) is increasingly used to make important decisions about people.
Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI
Through this proposal for procedural regularity in the identification and assessment of automated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law.
Explaining Explanations in AI
Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions.
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
We suggest data controllers should offer a particular type of explanation, unconditional counterfactual explanations, to support these three aims.
