Corporate AI safety research is dominated by pre-deployment alignment and evaluation work, while high-risk deployment topics such as medical error, misinformation, bias, behavioral design, and copyright are measured to be sharply under-researched.
Assessing Bias in Metric Models for LLM Open-Ended Generation Bias Benchmarks
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abstract
Open-generation bias benchmarks evaluate social biases in Large Language Models (LLMs) by analyzing their outputs. However, the classifiers used in analysis often have inherent biases, leading to unfair conclusions. This study examines such biases in open-generation benchmarks like BOLD and SAGED. Using the MGSD dataset, we conduct two experiments. The first uses counterfactuals to measure prediction variations across demographic groups by altering stereotype-related prefixes. The second applies explainability tools (SHAP) to validate that the observed biases stem from these counterfactuals. Results reveal unequal treatment of demographic descriptors, calling for more robust bias metric models.
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2025 1verdicts
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Real-World Gaps in AI Governance Research
Corporate AI safety research is dominated by pre-deployment alignment and evaluation work, while high-risk deployment topics such as medical error, misinformation, bias, behavioral design, and copyright are measured to be sharply under-researched.