Formal regulation designed to protect workers is often opaque, with narrow definitions of injury. This means that even when workers report harm, regulators fail to intervene on cumulative, chronic, and collective experiences. Through interviews and co-design workshops with unionized cleaning workers, we explore alternative forms of documentation that support collective action rather than institutional proof. Our study illuminates four interlocking worker-led documentation practices: (1) Surfacing experiences of working conditions, (2) Collectively making sense of existing reporting avenues, (3) Negotiating with management and publics, and (4) Building trust and solidarity within the union. For each of these practices, we offer concrete design concepts developed in collaboration with workers. Finally, we contribute conceptual knowledge on how engaging in the process of documentation functions as an engine of solidarity-building, a prerequisite for addressing workplace harms beyond disparate data points.

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.

Innovators transform the world by understanding where services are successfully meeting customers' needs and then using this knowledge to identify failsafe opportunities for innovation. Pre-trained models have changed the AI innovation landscape, making it faster and easier to create new AI products and services. Understanding where pre-trained models are successful is critical for supporting AI innovation. Unfortunately, the hype cycle surrounding pre-trained models makes it hard to know where AI can really be successful. To address this, we investigated pre-trained model applications developed by HCI researchers as a proxy for commercially successful applications. The research applications demonstrate technical capabilities, address real user needs, and avoid ethical challenges. Using an artifact analysis approach, we categorized capabilities, opportunity domains, data types, and emerging interaction design patterns, uncovering some of the opportunity space for innovation with pre-trained models.

Matching the autonomous vehicle’s (AV) driving style to its user’s preference is core to a satisfactory user experience. The recent HCI community has undertaken a significant amount of research to understand user-preferred driving styles in AVs. Due to its multifaceted nature, understanding these driving preferences is difficult unless users take roles in an adaptive system and share their needs explicitly. However, there is a lack of a proper channel for users to express their driving-style needs in AVs. To bridge this gap, we suggest a user’s preferred driving-style guidance using voice as a novel input channel for human-centric AV control. We conducted a Wizard-of-Oz driving study on real roads, aiming to explore the guiding experience with the AV agent to reflect their driving-style preferences. This paper presents the value of driving-style guidance along with its burden to users, and concludes with its implications in designing a better AV-guiding experience.