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Found 6 papers, 2 papers with code
The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices
Other fields that routinely interact with crowdsourcing workers, such as Psychology, have developed common governance requirements and norms to ensure research is undertaken ethically.
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.
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.
Principles alone cannot guarantee ethical AI
Despite the initial credibility granted to a principled approach to AI Ethics by the connection to principles in medical ethics, there are reasons to be concerned about its future impact on AI development and governance.
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.
