Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

Abstract

Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders -- decision makers (faculty) and decision subjects (students) -- use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.

arXiv commentCSCW 2023

References in corpus (16)

• Improving fairness in machine learning systems: What do industry practitioners need?
• The What-If Tool: Interactive Probing of Machine Learning Models
• Problem Formulation and Fairness
• A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
• Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
• CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis
• Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
• Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems
• Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation
• Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems
• Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"
• Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
• Cicero: Multi-Turn, Contextual Argumentation for Accurate Crowdsourcing
• Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives
• Doubting AI Predictions: Influence-Driven Second Opinion Recommendation

Cited by in corpus (12)

• Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
• Stakeholder-Centered AI Design: Co-Designing Worker Tools with Gig Workers through Data Probes
• 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
• Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia
• From Fitting Participation to Forging Relationships: The Art of Participatory ML
• "This is not a data problem": Algorithms and Power in Public Higher Education in Canada
• Design, Development, and Deployment of Context-Adaptive AI Systems for Enhanced End-User Adoption
• From Prediction to Foresight: The Role of AI in Designing Responsible Futures
• Understanding Collaboration between Professional Designers and Decision-making AI: A Case Study in the Workplace
• Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data
• Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors