Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness

• URL: http://arxiv.org/abs/2405.20447v1
• Date: Thu, 30 May 2024 19:46:47 GMT
• Title: Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
• Authors: Seamus Somerstep, Ya'acov Ritov, Yuekai Sun,
• Abstract summary: We develop algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees in social classification problems. A crucial benefit of this approach is that it is possible to resolve the incompatibilities between conflicting group fairness definitions.
• Score: 19.183108418687226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In many prediction problems, the predictive model affects the distribution of the prediction target. This phenomenon is known as performativity and is often caused by the behavior of individuals with vested interests in the outcome of the predictive model. Although performativity is generally problematic because it manifests as distribution shifts, we develop algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees in social classification problems (compared to what is achievable in non-performative settings). In particular, we leverage the policymaker's ability to steer the population to remedy inequities in the long term. A crucial benefit of this approach is that it is possible to resolve the incompatibilities between conflicting group fairness definitions.

Related papers

• Fairness-Accuracy Trade-Offs: A Causal Perspective [58.06306331390586]
  We analyze the tension between fairness and accuracy from a causal lens for the first time. We show that enforcing a causal constraint often reduces the disparity between demographic groups. We introduce a new neural approach for causally-constrained fair learning.
  arXiv  Detail & Related papers  (2024-05-24T11:19:52Z)
• On the Fairness ROAD: Robust Optimization for Adversarial Debiasing [46.495095664915986]
  ROAD is designed to prioritize inputs that are likely to be locally unfair. It achieves dominance with respect to local fairness and accuracy for a given global fairness level. It also enhances fairness generalization under distribution shift.
  arXiv  Detail & Related papers  (2023-10-27T18:08:42Z)
• Arbitrariness Lies Beyond the Fairness-Accuracy Frontier [3.383670923637875]
  We show that state-of-the-art fairness interventions can mask high predictive multiplicity behind favorable group fairness and accuracy metrics. We propose an ensemble algorithm applicable to any fairness intervention that provably ensures more consistent predictions.
  arXiv  Detail & Related papers  (2023-06-15T18:15:46Z)
• Fairness in Matching under Uncertainty [78.39459690570531]
  algorithmic two-sided marketplaces have drawn attention to the issue of fairness in such settings. We axiomatize a notion of individual fairness in the two-sided marketplace setting which respects the uncertainty in the merits. We design a linear programming framework to find fair utility-maximizing distributions over allocations.
  arXiv  Detail & Related papers  (2023-02-08T00:30:32Z)
• Fair Inference for Discrete Latent Variable Models [12.558187319452657]
  Machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations. We develop a fair variational inference technique for the discrete latent variables, which is accomplished by including a fairness penalty on the variational distribution. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a special-purpose graphical model for criminal justice risk assessments.
  arXiv  Detail & Related papers  (2022-09-15T04:54:21Z)
• Domain Adaptation meets Individual Fairness. And they get along [48.95808607591299]
  We show that algorithmic fairness interventions can help machine learning models overcome distribution shifts. In particular, we show that enforcing suitable notions of individual fairness (IF) can improve the out-of-distribution accuracy of ML models.
  arXiv  Detail & Related papers  (2022-05-01T16:19:55Z)
• Towards Equal Opportunity Fairness through Adversarial Learning [64.45845091719002]
  Adversarial training is a common approach for bias mitigation in natural language processing. We propose an augmented discriminator for adversarial training, which takes the target class as input to create richer features.
  arXiv  Detail & Related papers  (2022-03-12T02:22:58Z)
• Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach [131.20444904674494]
  We tackle the problem of measuring group fairness under unawareness of sensitive attributes. We show that quantification approaches are particularly suited to tackle the fairness-under-unawareness problem.
  arXiv  Detail & Related papers  (2021-09-17T13:45:46Z)
• Causally-motivated Shortcut Removal Using Auxiliary Labels [63.686580185674195]
  Key challenge to learning such risk-invariant predictors is shortcut learning. We propose a flexible, causally-motivated approach to address this challenge. We show both theoretically and empirically that this causally-motivated regularization scheme yields robust predictors.
  arXiv  Detail & Related papers  (2021-05-13T16:58:45Z)
• All of the Fairness for Edge Prediction with Optimal Transport [11.51786288978429]
  We study the problem of fairness for the task of edge prediction in graphs. We propose an embedding-agnostic repairing procedure for the adjacency matrix of an arbitrary graph with a trade-off between the group and individual fairness.
  arXiv  Detail & Related papers  (2020-10-30T15:33:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.