<ul><li>Algorithmic fairness in machine learning is a crucial concern, often aiming to meet criteria such as Equal Opportunity.</li><li>This paper delves into the impact of implementing fair classifiers on the broader landscape of ecosystem fairness, particularly in scenarios involving competitive companies.</li><li>The study reveals that fair classifiers may not necessarily result in fair ecosystems, showcasing a decrease in overall fairness in various circumstances determined by classifier correlation and data overlap.</li><li>Even if individual classifiers are fair, ecosystem outcomes may still be unfair, and enhancing the fairness of biased algorithms on an individual level could worsen ecosystem fairness, as indicated by both theory and experiments presented in the paper.</li></ul>

Fairness under Competition

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