Data Management for Causal Algorithmic Fairness
Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflects discrimination, suggesting a data management problem. In this paper, we first make a distinction between associational and causal definitions of fairness in the literature and argue that the concept of fairness requires causal reasoning. We then review existing works and identify future opportunities for applying data management techniques to causal algorithmic fairness.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningFairnessManagementSimilar Papers 제목 키워드 기반
Algorithmic Bias in Recidivism Prediction: A Causal Perspective
ProPublica's analysis of recidivism predictions produced by Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) software tool for the task, has shown that the predictions were racially biased ag…
Causal InferenceFairnessManagementPredictionCausal Feature Selection for Algorithmic Fairness
The use of machine learning (ML) in high-stakes societal decisions has encouraged the consideration of fairness throughout the ML lifecycle. Although data integration is one of the primary steps to generate high quality …
Data IntegrationFairnessfeature selectionManagementCounterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
As machine learning models are increasingly used in educational settings, from detecting at-risk students to predicting student performance, algorithmic bias and its potential impacts on students raise critical concerns …
counterfactualFairnessCausal Equal Protection as Algorithmic Fairness
By combining the philosophical literature on statistical evidence and the interdisciplinary literature on algorithmic fairness, we revisit recent objections against classification parity in light of causal analyses of al…
ClassificationDiagnosticFairnessPhilosophyCausal Reasoning for Algorithmic Fairness
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in causality necessary for the understandi…
Decision MakingFairness