Integrating complementary fairness metrics into AutoML pipeline optimization yields 14.5% better average fairness, 35.7% less data usage, and simpler models, with a 9.4% drop in predictive performance versus a performance-only baseline.
In: Proceedings of the 2021 AAAI/ACM Confer- ence on AI, Ethics, and Society
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A reinforcement learning model is ethically fine-tuned using aggregated feedback from LLMs embodying five moral principles via Belief Jensen-Shannon Divergence and Dempster-Shafer Theory.
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Exploring the impact of fairness-aware criteria in AutoML
Integrating complementary fairness metrics into AutoML pipeline optimization yields 14.5% better average fairness, 35.7% less data usage, and simpler models, with a 9.4% drop in predictive performance versus a performance-only baseline.
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Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making
A reinforcement learning model is ethically fine-tuned using aggregated feedback from LLMs embodying five moral principles via Belief Jensen-Shannon Divergence and Dempster-Shafer Theory.