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

Juliett Suárez Ferreira, Marija Slavkovik, Jorge Casillas

TL;DR

The paper addresses how individuals can ascertain fairness in automatic decision-making (ADM) by introducing ascertainable fairness, an epistemic right to information about decisions that affect them. It integrates substantive fairness (outcomes) with procedural fairness (transparency, contestability, accountability) through explainability, contestation, and audit mechanisms, forming a four-component framework. The framework comprises tools for assessing fairness of predictions and recourse, a contestation dialogue, and an audit pathway, aimed at empowering end-users and guiding policymakers and organizations. While offering a principled, user-centered blueprint, it notes limitations in metric standardization, user literacy, and regulatory integration, calling for ongoing alignment with evolving governance regimes like the EU AI Act.

Abstract

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.

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

TL;DR

The paper addresses how individuals can ascertain fairness in automatic decision-making (ADM) by introducing ascertainable fairness, an epistemic right to information about decisions that affect them. It integrates substantive fairness (outcomes) with procedural fairness (transparency, contestability, accountability) through explainability, contestation, and audit mechanisms, forming a four-component framework. The framework comprises tools for assessing fairness of predictions and recourse, a contestation dialogue, and an audit pathway, aimed at empowering end-users and guiding policymakers and organizations. While offering a principled, user-centered blueprint, it notes limitations in metric standardization, user literacy, and regulatory integration, calling for ongoing alignment with evolving governance regimes like the EU AI Act.

Abstract

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.

Paper Structure

This paper contains 22 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Algorithmic Decision Making and Fairness Background
  3. Substantive Dimension of Fairness: Algorithmic Fairness
  4. Procedural Dimension of Fairness in ADM Systems
  5. Explainability
  6. Accountability
  7. Contestability
  8. Algorithmic Decision Making and Ascertainable Fairness
  9. Algorithmic Fairness and Ascertainable Fairness
  10. Procedural Fairness and Ascertainable Fairness
  11. Ascertainable Fairness
  12. Asking and Informing About Fairness
  13. Contesting Dialogue
  14. Ascertainable Fairness: a Definition
  15. Ascertainable Fairness: a Conceptual Framework
  16. ...and 7 more sections

Figures (5)

  • Figure 1: Selecting a model for an ADM system.
  • Figure 2: Variations in the perception of fairness by individuals and organizations.
  • Figure 3: Ascertainable Fairness: from an unaddressed question to it's operationalization in ADM systems.
  • Figure 4: Contesting dialog
  • Figure 5: Ascertainable Fairness Framework. The end-user interacts with different tools to ascertain the fairness of the decision received.