# Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness

arXiv: https://arxiv.org/abs/2504.02461 · HTML: https://mlsanity.com/paper/2504.02461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 24 of 24)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review](https://mlsanity.com/paper/2010.10596/) (2020)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [On the Fairness of Causal Algorithmic Recourse](https://mlsanity.com/paper/2010.06529/) (2020)
- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals](https://mlsanity.com/paper/2009.08270/) (2020)
- [Equalizing Recourse across Groups](https://mlsanity.com/paper/1909.03166/) (2019)
- [Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals](https://mlsanity.com/paper/2103.11972/) (2021)
- [Machine learning fairness notions: Bridging the gap with real-world applications](https://mlsanity.com/paper/2006.16745/) (2020)
- [Contestable AI needs Computational Argumentation](https://mlsanity.com/paper/2405.10729/) (2024)
- [Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda](https://mlsanity.com/paper/2002.09054/) (2020)
- [Achieving Diversity in Counterfactual Explanations: a Review and Discussion](https://mlsanity.com/paper/2305.05840/) (2023)
- [Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions](https://mlsanity.com/paper/2111.01235/) (2021)
- [Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples](https://mlsanity.com/paper/2001.02271/) (2020)
- [Fair and Responsible AI: A Focus on the Ability to Contest](https://mlsanity.com/paper/2102.10787/) (2021)

## Abstract (arXiv)

Current fairness metrics and mitigation techniques provide tools for practitioners to asses how non-discriminatory Automatic Decision Making (ADM) systems are. What if I, as an individual facing a decision taken by an ADM system, would like to know: Am I being treated fairly? We explore how to create the affordance for users to be able to ask this question of ADM. In this paper, we argue for the reification of fairness not only as a property of ADM, but also as an epistemic right of an individual to acquire information about the decisions that affect them and use that information to contest and seek effective redress against those decisions, in case they are proven to be discriminatory. We examine key concepts from existing research not only in algorithmic fairness but also in explainable artificial intelligence, accountability, and contestability. Integrating notions from these domains, we propose a conceptual framework to ascertain fairness by combining different tools that empower the end-users of ADM systems. Our framework shifts the focus from technical solutions aimed at practitioners to mechanisms that enable individuals to understand, challenge, and verify the fairness of decisions, and also serves as a blueprint for organizations and policymakers, bridging the gap between technical requirements and practical, user-centered accountability.