Identifying AI Risks and Bias in Copilot Agents
Recognize high-risk AI use cases, identify sources of bias, and assess potential harms before deploying Copilot agents in business workflows.
Not all applications of Copilot are equally safe or appropriate. High-risk use cases include hiring decisions (AI bias may discriminate), medical diagnosis (inaccuracy risks patient harm), financial advice (liability concerns), and content moderation (affects marginalized communities disproportionately). Bias emerges when training data reflects historical discrimination, when specific populations are underrepresented, or when proxy variables correlate with protected characteristics. Professionals must identify risks early by asking: Does this decision affect human welfare? Are there legal/regulatory constraints? Does it involve protected attributes? Could errors cause harm? Could it perpetuate historical inequities? A governance framework requires documenting risk levels, assigning ownership, and implementing…
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