Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines

• URL: http://arxiv.org/abs/2207.02912v2
• Date: Mon, 11 Jul 2022 00:40:57 GMT
• Title: Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines
• Authors: Falaah Arif Khan, Eleni Manis and Julia Stoyanovich
• Abstract summary: We use Equal Oppportunity doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest.
• Score: 6.751310968561177
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work we use Equal Oppportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We contrast formal EO approaches that narrowly focus on fair contests at discrete decision points, with substantive EO doctrines that look at people's fair life chances more holistically over the course of a lifetime. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest -- foward-looking versus backward-looking -- when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible procedures based on the luck-egalitarian doctrine of EO, and Rawls's principle of fair equality of opportunity.

Related papers

• What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from the Perspective of Approximate Justice [1.8434042562191815]
  We argue that in the context of imperfect decision-making systems, we should not only care about what the ideal distribution of benefits/harms among individuals would look like. This requires us to rethink the way in which we, as algorithmic fairness researchers, view distributive justice and use fairness criteria.
  arXiv  Detail & Related papers  (2024-07-17T11:13:23Z)
• AI Fairness in Practice [0.46671368497079174]
  There is a broad spectrum of views across society on what the concept of fairness means and how it should be put to practice. This workbook explores how a context-based approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow.
  arXiv  Detail & Related papers  (2024-02-19T23:02:56Z)
• Evaluating the Fairness of Discriminative Foundation Models in Computer Vision [51.176061115977774]
  We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP) We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning.
  arXiv  Detail & Related papers  (2023-10-18T10:32:39Z)
• 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)
• Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP [64.45845091719002]
  Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. This paper seeks to clarify the current situation and plot a course for meaningful progress in fair learning.
  arXiv  Detail & Related papers  (2023-02-11T14:54:00Z)
• 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)
• 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)
• Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions (preprint) [0.0]
  We explain the different kinds of fairness concepts that might be applicable for the specific application of hiring decisions. We analyze their pros and cons with regard to the respective fairness interpretation and evaluate them from a legal perspective.
  arXiv  Detail & Related papers  (2021-08-16T06:41:39Z)
• Fairness as Equality of Opportunity: Normative Guidance from Political Philosophy [8.228275343025462]
  We introduce a taxonomy of fairness ideals using doctrines of Equality of Opportunity (EOP) from political philosophy. We clarify their conceptions in philosophy and the proposed codification in fair machine learning. We use our fairness-as-EOP framework to re-interpret the impossibility results from a philosophical perspective.
  arXiv  Detail & Related papers  (2021-06-15T16:07:58Z)
• The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairness [1.4449464910072918]
  The FairCeptron framework is an approach for studying perceptions of fairness in algorithmic decision making such as in ranking or classification. The framework includes fairness scenario generation, fairness perception elicitation and fairness perception analysis. An implementation of the FairCeptron framework is openly available, and it can easily be adapted to study perceptions of algorithmic fairness in other application contexts.
  arXiv  Detail & Related papers  (2021-02-08T10:47:24Z)
• 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)

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.