A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making

Abstract

Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield unfair outcomes because their sanity is challenging to assess and calibrate in the first place -- which is particularly worrisome for human decision-subjects. Based on this observation and building upon existing work, I aim to make the following three main contributions through my doctoral thesis: (a) understand how (potential) decision-subjects perceive algorithmic decisions (with varying degrees of transparency of the underlying ADS), as compared to similar decisions made by humans; (b) evaluate different tools for transparent decision-making with respect to their effectiveness in enabling people to appropriately assess the quality and fairness of ADS; and (c) develop human-understandable technical artifacts for fair automated decision-making. Over the course of the first half of my PhD program, I have already addressed substantial pieces of (a) and (c), whereas (b) will be the major focus of the second half.

CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI '22 Extended Abstracts), April 29--May 5, 2022, New Orleans, LA, USA

References in corpus (10)

• Equality of Opportunity in Supervised Learning
• 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
• What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
• Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
• Avoiding Discrimination through Causal Reasoning
• A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
• This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
• Auditing for Discrimination in Algorithms Delivering Job Ads
• Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems
• A Study on Fairness and Trust Perceptions in Automated Decision Making