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ved data points that led to bias in its AI models.

Yet as it’s widely established, AIs are only as good as the data they’re trained on. Bad data that accommodate absolute racial, gender, or brainy biases can also creep into the systems, consistent in a abnormality called disparate impact, wherein some candidates may be unfairly alone or afar altogether because they don’t fit a assertive analogue of “fairness.”

Regulating the use of AI-based tools necessitates the need for algebraic transparency, bias testing, and assessing them for risks associated with automatic discrimination.

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