The Unfairness of $\varepsilon$-Fairness

• URL: http://arxiv.org/abs/2405.09360v2
• Date: Mon, 17 Jun 2024 18:38:17 GMT
• Title: The Unfairness of $\varepsilon$-Fairness
• Authors: Tolulope Fadina, Thorsten Schmidt,
• Abstract summary: We show that if the concept of $varepsilon$-fairness is employed, it can possibly lead to outcomes that are maximally unfair in the real-world context. We illustrate our findings with two real-world examples: college admissions and credit risk assessment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fairness in decision-making processes is often quantified using probabilistic metrics. However, these metrics may not fully capture the real-world consequences of unfairness. In this article, we adopt a utility-based approach to more accurately measure the real-world impacts of decision-making process. In particular, we show that if the concept of $\varepsilon$-fairness is employed, it can possibly lead to outcomes that are maximally unfair in the real-world context. Additionally, we address the common issue of unavailable data on false negatives by proposing a reduced setting that still captures essential fairness considerations. We illustrate our findings with two real-world examples: college admissions and credit risk assessment. Our analysis reveals that while traditional probability-based evaluations might suggest fairness, a utility-based approach uncovers the necessary actions to truly achieve equality. For instance, in the college admission case, we find that enhancing completion rates is crucial for ensuring fairness. Summarizing, this paper highlights the importance of considering the real-world context when evaluating fairness.

Related papers

• Implementing Fairness: the view from a FairDream [0.0]
  We train an AI model and develop our own fairness package FairDream to detect inequalities and then to correct for them. Our experiments show that it is a property of FairDream to fulfill fairness objectives which are conditional on the ground truth.
  arXiv  Detail & Related papers  (2024-07-20T06:06:24Z)
• 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)
• Auditing Fairness under Unobserved Confounding [56.61738581796362]
  We show that we can still give meaningful bounds on treatment rates to high-risk individuals, even when entirely eliminating or relaxing the assumption that all relevant risk factors are observed. This result is of immediate practical interest: we can audit unfair outcomes of existing decision-making systems in a principled manner.
  arXiv  Detail & Related papers  (2024-03-18T21:09:06Z)
• Navigating Fairness Measures and Trade-Offs [0.0]
  I show that by using Rawls' notion of justice as fairness, we can create a basis for navigating fairness measures and the accuracy trade-off. This also helps to close part of the gap between philosophical accounts of distributive justice and the fairness literature.
  arXiv  Detail & Related papers  (2023-07-17T13:45:47Z)
• FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods [84.1077756698332]
  This paper introduces the Fair Fairness Benchmark (textsfFFB), a benchmarking framework for in-processing group fairness methods. We provide a comprehensive analysis of state-of-the-art methods to ensure different notions of group fairness.
  arXiv  Detail & Related papers  (2023-06-15T19:51:28Z)
• Reconciling Predictive and Statistical Parity: A Causal Approach [68.59381759875734]
  We propose a new causal decomposition formula for the fairness measures associated with predictive parity. We show that the notions of statistical and predictive parity are not really mutually exclusive, but complementary and spanning a spectrum of fairness notions.
  arXiv  Detail & Related papers  (2023-06-08T09:23:22Z)
• 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)
• Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research [2.6397379133308214]
  We argue that such assumptions, which are often left implicit and unexamined, lead to inconsistent conclusions. While the intended goal of this work may be to improve the fairness of machine learning models, these unexamined, implicit assumptions can in fact result in emergent unfairness.
  arXiv  Detail & Related papers  (2021-02-01T22:02:14Z)
• Survey on Causal-based Machine Learning Fairness Notions [4.157415305926584]
  This paper examines an exhaustive list of causal-based fairness notions and study their applicability in real-world scenarios. As the majority of causal-based fairness notions are defined in terms of non-observable quantities, their deployment in practice requires to compute or estimate those quantities.
  arXiv  Detail & Related papers  (2020-10-19T14:28:55Z)
• Algorithmic Decision Making with Conditional Fairness [48.76267073341723]
  We define conditional fairness as a more sound fairness metric by conditioning on the fairness variables. We propose a Derivable Conditional Fairness Regularizer (DCFR) to track the trade-off between precision and fairness of algorithmic decision making.
  arXiv  Detail & Related papers  (2020-06-18T12:56:28Z)
• Statistical Equity: A Fairness Classification Objective [6.174903055136084]
  We propose a new fairness definition motivated by the principle of equity. We formalize our definition of fairness, and motivate it with its appropriate contexts. We perform multiple automatic and human evaluations to show the effectiveness of our definition.
  arXiv  Detail & Related papers  (2020-05-14T23:19:38Z)

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.