Inherent Trade-Offs in the Fair Determination of Risk Scores

Cited by (945)

Items 101–200 of 945

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3. Operationalizing individual fairness with pairwise fair representations
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4. A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
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5. Trustworthy AI in the public sector: An empirical analysis of a Swedish labor market decision-support system
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6. Engaging the ethics of data science in practice
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7. The false promise of risk assessments: epistemic reform and the limits of fairness
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8. A translational perspective towards clinical AI fairness
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9. Fairness issues, current approaches, and challenges in machine learning models
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10. Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction
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11. Responsible data management
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12. Improving fairness in machine learning systems: What do industry practitioners need?
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13. Ethical and social considerations of applying artificial intelligence in healthcare—a two-pronged scoping review
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14. Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
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16. Methodological Challenges and Opportunities in Testing for Racial Discrimination in Policing
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17. A Framework for Assurance Audits of Algorithmic Systems
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18. Fair classification and social welfare
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19. Conceptual challenges for interpretable machine learning
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20. Non-Discriminatory Machine Learning through Convex Fairness Criteria
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21. Active Fairness in Algorithmic Decision Making
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22. What's Fair About Individual Fairness?
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23. Artificial Intelligence in Cardiovascular Care—Part 2: Applications
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24. Towards a Unified Framework for Fair and Stable Graph Representation Learning
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25. REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
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26. Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability
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27. Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
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28. Fair Influence Maximization: a Welfare Optimization Approach
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29. Let Me Take Over: Variable Autonomy for Meaningful Human Control
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30. When Users Control the Algorithms
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31. Incomplete Contracting and AI Alignment
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32. Fair Coresets and Streaming Algorithms for Fair k-means
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33. Improving fairness of artificial intelligence algorithms in Privileged-Group Selection Bias data settings
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34. Ethics-based auditing of automated decision-making systems: intervention points and policy implications
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35. FlipTest
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36. Machine learning fairness notions: Bridging the gap with real-world applications
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37. Responsible Artificial Intelligence in Human Resources Technology: An innovative inclusive and fair by design matching algorithm for job recruitment purposes
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38. Downstream Effects of Affirmative Action
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39. Towards Guidelines for Assessing Qualities of Machine Learning Systems
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40. Probabilistic verification of fairness properties via concentration
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41. Against Predictive Optimization: On the Legitimacy of Decision-making Algorithms That Optimize Predictive Accuracy
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42. Community-in-the-loop: towards pluralistic value creation in AI, or—why AI needs business ethics
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43. Transport-based Counterfactual Models
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44. The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems
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45. Risk-Based Sentencing and Predictive Accuracy
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46. Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
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47. Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach
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48. Predicting high-risk opioid prescriptions before they are given
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49. Group Fairness for the Allocation of Indivisible Goods
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50. Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems
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51. Fair Conformal Predictors for Applications in Medical Imaging
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52. Tragic Choices and the Virtue of Techno-Responsibility Gaps
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53. D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias
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54. Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport
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55. Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
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56. An Economic Approach to Regulating Algorithms
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57. Algorithmic Bias and Access to Opportunities
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58. Predictive policing and algorithmic fairness
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59. Transparency, Fairness, Data Protection, Neutrality
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60. Balanced Ranking with Diversity Constraints
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61. Constitutional Challenges in the Algorithmic Society
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63. Ethical Data Curation for AI: An Approach based on Feminist Epistemology and Critical Theories of Race
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64. Axiological futurism: The systematic study of the future of values
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65. Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability
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66. Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems
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72. Who Gets a Second Chance? Effectiveness and Equity in Supervision of Criminal Offenders
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73. Fairness in Machine Learning: Against False Positive Rate Equality as a Measure of Fairness
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74. Are algorithms biased in education? Exploring racial bias in predicting community college student success
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82. Algorithmic fairness in credit scoring
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83. The Public Safety Assessment: A Re-Validation and Assessment of Predictive Utility and Differential Prediction by Race and Gender in Kentucky
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85. Assessing trustworthy AI: Technical and legal perspectives of fairness in AI
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86. A Harm-Reduction Framework for Algorithmic Fairness
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87. Learning fair representations via an adversarial framework
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88. Fair Algorithms for Clustering
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89. Fairness in Learning-Based Sequential Decision Algorithms: A Survey
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90. With Malice Toward None: Assessing Uncertainty via Equalized Coverage
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91. Group Fairness
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92. On the Inequity of Predicting A While Hoping for B
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94. The Current State and Challenges of Fairness in Federated Learning
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95. To "See" is to Stereotype
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96. Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
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97. On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes
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98. Engines of Order
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99. The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric
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100. Machine Learning for Sociology
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