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This article demonstrates how high validation accuracy can conceal proxy bias in AI models, using SHAP for explainability and a runtime governance wrapper to enforce fairness policies and prevent biased decisions in production.
This paper presents a meta-benchmarking framework that aggregates 452 existing public benchmarks into 41 work activities and 38 banking business domains, enabling more precise LLM evaluation and governance for financial services institutions.
The article argues that access rules for frontier AI models are becoming a key part of the product experience, affecting eligibility, preview status, and fallback options, and that serious AI work requires predictable access rules.
In federal court Anthropic admitted it cannot control or recall Claude once deployed, exposing a governance gap where vendors disclaim post-sale control and shifting liability questions toward pre-sale disclosure.