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Found 2 papers, 1 papers with code
Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation
In this work, we study the correspondence between such decontextualized "trick tests" and evaluations that are more grounded in Realistic Use and Tangible {Effects (i. e. RUTEd evaluations).
Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness
This is an intuitive standard, as reflected in the U. S. legal system, but its use is limited because counterfactuals cannot be directly observed in real-world data.
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