Description
Imagine a hiring algorithm that favors certain demographics or a loan approval system that unfairly discriminates against specific groups. These scenarios highlight the critical need to address bias in AI. Biased models can perpetuate and even amplify existing societal inequalities, leading to unfair and harmful outcomes.
Here’s the process of identifying and mitigating bias in your AI models. From understanding different types of bias to implementing fairness-aware techniques, here’s how to build AI systems that are ethical, inclusive, and promote equitable outcomes for all.
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