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In this episode of our Defensible Decisions podcast series, shareholders Scott Kelly (Birmingham/Washington) and Lauren Hicks (Indianapolis/Atlanta) examine what happens when AI produces written content that is inconsistent, biased, or legally problematic in the employment context. Scott, who is chair of the firm’s Workforce Analytics and Compliance Practice Group, and Lauren cover how large language models work as prediction engines rather than knowledge bases, and why that distinction creates real legal exposure when AI-generated outputs differ based on demographic descriptors. The speakers walk through a concrete qualitative test illustrating how the same prompt can yield meaningfully different results depending on a racial modifier, and what that means for employers using AI in hiring assessments and performance management.

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Practice Group

Workforce Analytics and Compliance

Ogletree Deakins’ Workforce Analytics and Compliance Practice Group provides tailored guidance and legal recommendations for a myriad of workforce issues, informed by data-driven, state-of-the-art compliance and risk assessment services. Our services encompass all stages of the employment life cycle, such as selections, career advancement, compensation and benefits, and retention, which enables employers to make informed decisions […]

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