Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems

Abstract

Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions. However, we still lack an understanding of how to develop machine learning systems with fairness criteria that reflect relevant stakeholders' nuanced viewpoints in real-world contexts. To address this gap, we propose a framework for eliciting stakeholders' subjective fairness notions. Combining a user interface that allows stakeholders to examine the data and the algorithm's predictions with an interview protocol to probe stakeholders' thoughts while they are interacting with the interface, we can identify stakeholders' fairness beliefs and principles. We conduct a user study to evaluate our framework in the setting of a child maltreatment predictive system. Our evaluations show that the framework allows stakeholders to comprehensively convey their fairness viewpoints. We also discuss how our results can inform the design of predictive systems.

References in corpus (5)

• Improving fairness in machine learning systems: What do industry practitioners need?
• 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions
• Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
• Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
• A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores

Cited by in corpus (8)

• Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services
• Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
• Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation
• Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis
• Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
• Are We Asking the Right Questions?: Designing for Community Stakeholders' Interactions with AI in Policing
• Fair Machine Guidance to Enhance Fair Decision Making in Biased People
• Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking