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Accuracy and individual fairness are both crucial for trustworthy machine learning, but these two aspects are often incompatible with each other so that enhancing one aspect may sacrifice the other inevitably with side effects of true bias or false fairness.
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Fairness in Criminal Justice Risk Assessments: The State of the Art
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Lambrecht, A.; and Tucker, C. 2019 · 2019
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Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
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ϵ \epsilon -weakened robustness of deep neural networks
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SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
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Out-of-Distribution Detection through Relative Activation-Deactivation Abstractions
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Adversarial Input Detection Based on Critical Transformation Robustness
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