Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper

• URL: http://arxiv.org/abs/2509.20387v1
• Date: Mon, 22 Sep 2025 19:37:18 GMT
• Title: Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper
• Abstract summary: We argue that discrimination is often the result of a lack of well-specified fairness requirements and their verification.<n>The fact that experts' knowledge about fairness is often implicit makes the task of specifying precise and verifiable fairness requirements difficult.<n>We propose the development of a knowledge graph-based framework for fairness.
• Score: 2.8091685003204905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decisions suggested by improperly designed software systems might be prone to discriminate against people based on protected characteristics, such as gender and ethnicity. Previous studies attribute such undesired behavior to flaws in algorithmic design or biased data. However, these studies ignore that discrimination is often the result of a lack of well-specified fairness requirements and their verification. The fact that experts' knowledge about fairness is often implicit makes the task of specifying precise and verifiable fairness requirements difficult. In related domains, such as security engineering, knowledge graphs have been proven to be effective in formalizing knowledge to assist requirements specification and verification. To address the lack of formal mechanisms for specifying and verifying fairness requirements, we propose the development of a knowledge graph-based framework for fairness. In this paper, we discuss the challenges, research questions, and a road map towards addressing the research questions.

Related papers

• Learning When Not to Decide: A Framework for Overcoming Factual Presumptuousness in AI Adjudication [4.991125406994611]
  A well-known limitation of AI systems is the tendency to provide confident answers when information may be lacking.<n>This challenge is particularly acute in legal applications, where a core task for attorneys, judges, and administrators is to determine whether evidence is sufficient to reach a conclusion.<n>We study this problem in the important setting of unemployment insurance adjudication, which has seen rapid integration of AI systems.
  arXiv  Detail & Related papers  (2026-04-21T18:17:08Z)
• Fairness Constraints in High-Dimensional Generalized Linear Models [0.030261056927077812]
  We propose a framework that infers sensitive attributes from auxiliary features and integrates fairness constraints into model training.<n>Our approach mitigates bias while preserving predictive accuracy, offering a practical solution for fairness-aware learning.
  arXiv  Detail & Related papers  (2026-04-17T18:06:59Z)
• A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineering [3.5429774642987915]
  This paper presents a review of existing gray literature, examining fairness requirements in AI context.<n>Our gray literature investigation shows various definitions of fairness requirements in AI systems.<n>Fairness requirement violations are frequently linked, but not limited, to data representation bias, algorithmic and model design bias, human judgment, and evaluation and transparency gaps.
  arXiv  Detail & Related papers  (2025-12-08T19:22:01Z)
• Partial Identification Approach to Counterfactual Fairness Assessment [50.88100567472179]
  We introduce a Bayesian approach to bound unknown counterfactual fairness measures with high confidence.<n>Our results reveal a positive (spurious) effect on the COMPAS score when changing race to African-American (from all others) and a negative (direct causal) effect when transitioning from young to old age.
  arXiv  Detail & Related papers  (2025-09-30T18:35:08Z)
• FairPFN: A Tabular Foundation Model for Causal Fairness [39.83807136585407]
  Causal fairness provides a transparent, human-in-the-loop framework to mitigate algorithmic discrimination.<n>We propose FairPFN, a model pre-trained on synthetic causal fairness data to identify and mitigate the causal effects of protected attributes in its predictions.
  arXiv  Detail & Related papers  (2025-06-08T09:15:45Z)
• Causal Discovery with Language Models as Imperfect Experts [119.22928856942292]
  We consider how expert knowledge can be used to improve the data-driven identification of causal graphs. We propose strategies for amending such expert knowledge based on consistency properties. We report a case study, on real data, where a large language model is used as an imperfect expert.
  arXiv  Detail & Related papers  (2023-07-05T16:01:38Z)
• A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges [11.885166133818819]
  Adoption of Machine Learning systems has led to increased concerns about fairness implications. We present a taxonomy for intersectional notions of fairness and mitigation. We identify the key challenges and provide researchers with guidelines for future directions.
  arXiv  Detail & Related papers  (2023-05-11T16:49:22Z)
• 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)
• SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles [50.90773979394264]
  This paper studies a model that protects the privacy of individuals' sensitive information while also allowing it to learn non-discriminatory predictors. A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.
  arXiv  Detail & Related papers  (2022-04-11T14:42:54Z)
• Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There? [0.0]
  We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority. We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.
  arXiv  Detail & Related papers  (2021-05-04T12:09:26Z)
• Fairness in Machine Learning [15.934879442202785]
  We show how causal Bayesian networks can play an important role to reason about and deal with fairness. We present a unified framework that encompasses methods that can deal with different settings and fairness criteria.
  arXiv  Detail & Related papers  (2020-12-31T18:38:58Z)
• Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty [66.17147341354577]
  We argue for considering a complementary form of transparency by estimating and communicating the uncertainty associated with model predictions. We describe how uncertainty can be used to mitigate model unfairness, augment decision-making, and build trustworthy systems. This work constitutes an interdisciplinary review drawn from literature spanning machine learning, visualization/HCI, design, decision-making, and fairness.
  arXiv  Detail & Related papers  (2020-11-15T17:26:14Z)
• Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach [54.32266555843765]
  This paper studies a model that protects the privacy of the individuals sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints.
  arXiv  Detail & Related papers  (2020-09-26T10:50:33Z)

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.