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Algorithmic Accountability

The principle that organizations deploying AI systems should be responsible for their outcomes and able to explain how algorithmic decisions are made, especially in high-stakes contexts.

Algorithmic accountability legislation is advancing globally: the EU AI Act requires impact assessments for high-risk AI, New York City's Local Law 144 mandates bias audits for AI hiring tools, and the Colorado AI Act requires impact assessments for high-risk AI systems. Accountability frameworks typically require documentation of training data, model performance metrics, bias testing results, and human oversight mechanisms. The challenge is that modern AI models — particularly large neural networks — operate as black boxes, making full accountability technically difficult.

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