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

arXiv 2506.17494 ↗First posted 20 Jun 2025cs.HCcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Jun 2025.

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.