Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation
arXiv:2302.11623 · doi:10.1145/3579601
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.
CSCW 2023
References in corpus (10)
- Improving fairness in machine learning systems: What do industry practitioners need?
- 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
- Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems
- Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems
- Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"
- Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML
- Doubting AI Predictions: Influence-Driven Second Opinion Recommendation