Fairness in AI refers to the principle of ensuring that artificial intelligence systems operate without bias and treat all individuals equitably. It encompasses various dimensions, including the avoidance of discrimination based on race, gender, age, or other characteristics. Fairness is critical in applications such as hiring algorithms, credit scoring, and law enforcement, where biased outcomes can have significant societal impacts. The evaluation of fairness involves assessing model outputs and ensuring that they meet ethical standards and societal expectations. Techniques for promoting fairness include data preprocessing, algorithmic adjustments, and post-hoc analysis.
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AI Fundamentals