Responsible AI (rAI) guidance increasingly promotes stakeholder involvement (SHI) during AI development. At the same time, SHI is already common in commercial software development, but with potentially different foci. This study clarifies the extent to which established SHI practices are able to contribute to rAI efforts as well as potential disconnects -- essential insights to inform and tailor future interventions that further shift industry practice towards rAI efforts. First, we analysed 56 rAI guidance documents to identify why SHI is recommended (i.e. its expected benefits for rAI) and uncovered goals such as redistributing power, improving socio-technical understandings, anticipating risks, and enhancing public oversight. To understand why and how SHI is currently practised in commercial settings, we then conducted an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. Our findings reveal that SHI in practice is primarily driven by commercial priorities (e.g. customer value, compliance) and several factors currently discourage more rAI-aligned SHI practices. This suggests that established SHI practices are largely not contributing to rAI efforts. To address this disconnect, we propose interventions and research opportunities to advance rAI development in practice.

Applications of robotics (such as telepresence, transportation, elder-care, remote health care, cleaning, warehouse logistics, and delivery) are bringing significant changes in individuals' lives and are having profound social impact. Despite the envisioned potential of robotics, the goal of ubiquitous robot assistants augmenting quality of life (and quality of work life) has not yet been realized. Key challenges lie in the complexities of four overarching human-centric objectives that such systems must aim for: 1) improving quality of life of people, especially marginalized communities; 2) anticipating and mitigating unintended negative consequences of technological development; 3) enabling robots to adapt to the desires and needs of human counterparts; 4) respecting the need for human autonomy and agency. Pursuing these objectives requires an integrated cohort of technologists, behavioral scientists and social scientists with a shared vision to pursue a deep, multidisciplinary understanding of how robots interact with individuals and society. We introduce a new term, socially cognizant robotics, to describe this multi-faceted interdisciplinary branch of technology. The emerging practitioner, the socially cognizant roboticist, represents the convergence of socially aware technologists, who can develop intelligent devices that adapt to human and social behavior; and technology-aware social scientists and policymakers, who can translate studies of robotics' social effects into actionable and technically-viable principles and policies. A primary element of socially cognizant robotics is a deliberate "invitation to the table" for social scientists, who bring analytical perspectives and methods that are not typically present in robotics. These perspectives cover two levels of human-technology interaction that we view as essential: the human-robot dyad (Section 2) and the robot-society dyad (Section 3). Figure 1 illustrates how these levels might operate in the context of the workplace and everyday life.

There is a growing consensus in HCI and AI research that the design of AI systems needs to engage and empower stakeholders who will be affected by AI. However, the manner in which stakeholders should participate in AI design is unclear. This workshop paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices via a survey of recent published research and a dozen semi-structured interviews with AI researchers and practitioners. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design and articulates a set of empirical findings that in ensemble detail out the contemporary landscape of participatory practice in AI design. These findings can help bootstrap a more principled discussion on how PD of AI should move forward across AI, HCI, and other research communities.