LLMs show up to 60.58% social bias in generated code; a new Fairness Monitor Agent cuts bias by 65.1% and raises functional correctness from 75.80% to 83.97%.
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A clustering method with an explicit purity-parsimony loss integrates structural equation models by grouping IS constructs via task-adapted text embeddings.
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Social Bias in LLM-Generated Code: Benchmark and Mitigation
LLMs show up to 60.58% social bias in generated code; a new Fairness Monitor Agent cuts bias by 65.1% and raises functional correctness from 75.80% to 83.97%.
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GUT-IS: A Data-Driven Approach to Integrating Constructs and Their Relations in Information Systems
A clustering method with an explicit purity-parsimony loss integrates structural equation models by grouping IS constructs via task-adapted text embeddings.