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

TL;DR

This paper explores how early-stage AI concept selection can incorporate Responsible AI principles through design experiments, enabling practitioners to identify promising, low-risk AI ideas that align with ethical and commercial goals.

Contribution

It introduces a design-led approach for integrating RAI considerations into early AI concept sorting, supported by empirical experiments with industry practitioners.

Findings

Practitioners are receptive to early RAI integration.

Early-stage evaluation can identify low-risk, high-benefit AI concepts.

Design methods effectively support multidisciplinary collaboration.

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.