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

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.

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Publication Details

Published
2025-07-04
DOI
https://doi.org/10.1145/3715336.3735745
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
2.37

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article

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

Kenneth Holstein, Devansh Saxena, Jini Kim, John Zimmerman et al.
1 citations
Ethics and Social Impacts of AI
2.37
article

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

Kenneth Holstein, Devansh Saxena, Jini Kim, John Zimmerman, Jodi Forlizzi, Minjung Park, Ji-Youn Jung
article en
1 citations

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.

University of Wisconsin–Madison (US), Carnegie Mellon University (US)
National Science Foundation, Carnegie Mellon University
Openalex Percentile: Top 11%
Ethics and Social Impacts of AI
2.37
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