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Large language models (LLMs) are increasingly used as judges to predict which of two candidate answers a human evaluator would prefer, providing a scalable alternative when human evaluation is too costly. Unfortunately, LLM judgments can exhibit systematic biases. Prior work has documented position, verbosity or model...
In many high-stakes settings, human decision-makers can acquire support information before making a decision. However, acquiring information is costly, and disclosure may fail to improve human decisions or may even impair them. We tackle this problem by studying selective disclosure, i.e., the problem of learning when...
This overview addresses this issue by providing a gentle introduction to RSs, discussing their causes and consequences in intuitive terms, and details methods for dealing with RSs, including mitigation and awareness strategies, and maps their benefits and limitations.
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