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Found 6 papers, 4 papers with code
Dataset Fairness: Achievable Fairness on Your Data With Utility Guarantees
In machine learning fairness, training models which minimize disparity across different sensitive groups often leads to diminished accuracy, a phenomenon known as the fairness-accuracy trade-off.
Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits
Off-Policy Evaluation (OPE) in contextual bandits is crucial for assessing new policies using existing data without costly experimentation.
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
However, a major challenge faced by practitioners is the lack of clear guidance on evaluating whether LLM outputs align with social norms, values, and regulations.
Causal Falsification of Digital Twins
We consider how to assess the accuracy of a digital twin using real-world data.
Manifold Restricted Interventional Shapley Values
Shapley values are model-agnostic methods for explaining model predictions.
Conformal Off-Policy Prediction in Contextual Bandits
Most off-policy evaluation methods for contextual bandits have focused on the expected outcome of a policy, which is estimated via methods that at best provide only asymptotic guarantees.
