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

2025-06-20

AI Summary

AI projects frequently fail due to insufficient early attention to the triadic balance among ethics, user acceptance, and commercial viability. This paper addresses the concept screening phase in business-oriented AI development by proposing the first design-driven early-stage prioritization framework that systematically integrates Responsible AI (RAI) principles with service design thinking at the AI incubation front-end. Grounded in the Research through Design paradigm, we conducted three design experiments—including an industry probe study—complemented by qualitative interviews, co-design workshops, and multidimensional risk–benefit assessments. Results demonstrate that the framework effectively supports practitioners in identifying low-risk, high-value AI concepts; significantly improves screening quality; and fosters cross-disciplinary collaborative decision-making. The study contributes a practice-oriented methodological toolkit and empirically grounded insights for the intersection of Human–Computer Interaction (HCI) and RAI.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Address early-stage AI concept selection challenges
• Integrate Responsible AI principles in commercial AI design
• Combine ethical and service design in AI innovation

Innovation

• Design-led approach for early AI concept selection
• Multidisciplinary collaboration to evaluate risks
• Embedding ethical and service design thinking