Fairness in machine learning: A survey

Caton, Simon, Haas, Christian·2024·ACM Computing Surveys

Cites (366)

Items 201–363 of 363

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  92. An Ontology for Fairness Metrics
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  117. Fair Algorithms for Infinite and Contextual Bandits
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  118. Achieving Fairness in the Stochastic Multi-armed Bandit Problem
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  120. Impartial Predictive Modeling: Ensuring Group Fairness in Arbitrary Models
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  122. Fair Algorithms for Multi-Agent Multi-Armed Bandits
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  123. Mitigating Included- and Omitted-Variable Bias in Estimates of Disparate Impact
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  124. Proxy Fairness
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  125. The cost of fairness in classification
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  126. Algorithmic Pollution: Understanding and Responding to Negative Consequences of Algorithmic Decision-Making
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  131. Unleashing Linear Optimizers for Group-Fair Learning and Optimization
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  132. Causal Interventions for Fairness
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  133. FairGrad: Fairness Aware Gradient Descent
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  134. Data Management for Causal Algorithmic Fairness
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  135. A Neural Network Framework for Fair Classifier
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  136. Capuchin: Causal Database Repair for Algorithmic Fairness
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  137. An Interactive Approach to Bias Mitigation in Machine Learning
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  138. Stable and Fair Classification
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  139. A Notion of Individual Fairness for Clustering
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  140. Achieving User-Side Fairness in Contextual Bandits
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  141. Survey on Fair Reinforcement Learning: Theory and Practice
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  142. Gender differences in beliefs about algorithmic fairness.
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  143. Fair Decisions Despite Imperfect Predictions
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  144. Decision making with limited feedback:Error bounds for predictive policing and recidivism prediction
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  146. Using Pareto simulated annealing to address algorithmic bias in machine learning
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  148. Preventing Disparities: Bayesian and Frequentist Methods for Assessing Fairness in Machine-Learning Decision-Support Models
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  158. Group Meritocratic Fairness in Linear Contextual Bandits
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  159. Improved Approximation for Fair Correlation Clustering
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