Making the Right Thing: Bridging HCI and Responsible AI in Early-Stage AI Concept Selection

Abstract

AI projects often fail due to financial, technical, ethical, or user acceptance challenges -- failures frequently rooted in early-stage decisions. While HCI and Responsible AI (RAI) research emphasize this, practical approaches for identifying promising concepts early remain limited. Drawing on Research through Design, this paper investigates how early-stage AI concept sorting in commercial settings can reflect RAI principles. Through three design experiments -- including a probe study with industry practitioners -- we explored methods for evaluating risks and benefits using multidisciplinary collaboration. Participants demonstrated strong receptivity to addressing RAI concerns early in the process and effectively identified low-risk, high-benefit AI concepts. Our findings highlight the potential of a design-led approach to embed ethical and service design thinking at the front end of AI innovation. By examining how practitioners reason about AI concepts, our study invites HCI and RAI communities to see early-stage innovation as a critical space for engaging ethical and commercial considerations together.

Accepted for publication in Designing Interactive Systems Conference (DIS '25), July 5--9, 2025, Funchal, Portugal. ACM, New York, NY, USA, 21 pages

References in corpus (10)

• Improving fairness in machine learning systems: What do industry practitioners need?
• Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes
• The Fallacy of AI Functionality
• Problem Formulation and Fairness
• Interactive Model Cards: A Human-Centered Approach to Model Documentation
• The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals
• Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
• Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
• Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design
• Overcoming Failures of Imagination in AI Infused System Development and Deployment