Bias and Fairness in AI
Bias in AI refers to systematic errors or prejudices in model outputs, often stemming from unrepresentative or prejudiced training data. Fairness in AI aims to ensure that models treat different groups of people equitably, avoiding discriminatory outcomes. This involves identifying, measuring, and mitigating biases at every stage of the AI lifecycle.
AI bias is like a flawed scale that consistently weighs certain objects more or less heavily, while AI fairness aims to ensure the scale provides an accurate and impartial weight for everyone.
Addressing bias and achieving fairness is essential for developing ethical and trustworthy AI systems that benefit all of society without perpetuating or amplifying existing inequalities.
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