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The rapid development and deployment of generative AI technologies creates a design challenge of how to proactively understand the implications of productizing and deploying these new technologies, especially with regard to negative design implications. This is especially concerning in CSCW applications, where AI agents can introduce misunderstandings or even misdirections with the people interacting with the agent. In this panel, researchers from academia and industry will reflect on their experiences with ideas, methods, and processes to enable designers to proactively shape the responsible design of genAI in collaborative applications. The panelists represent a range of different approaches, including speculative fiction, design activities, design toolkits, and process guides. We hope that the panel encourages a discussion in the CSCW community around techniques we can put into practice today to enable the responsible design of genAI. 

An emerging body of research indicates that ineffective cross-functional collaboration – the interdisciplinary work done by industry practitioners across roles – represents a major barrier to addressing issues of fairness in AI design and development. In this research, we sought to better understand practitioners’ current practices and tactics to enact cross-functional collaboration for AI fairness, in order to identify opportunities to support more effective collaboration. We conducted a series of interviews and design workshops with 23 industry practitioners spanning various roles from 17 companies. We found that practitioners engaged in bridging work to overcome frictions in understanding, contextualization, and evaluation around AI fairness across roles. In addition, in organizational contexts with a lack of resources and incentives for fairness work, practitioners often piggybacked on existing requirements (e.g., for privacy assessments) and AI development norms (e.g., the use of quantitative evaluation metrics), although they worry that these tactics may be fundamentally compromised. Finally, we draw attention to the invisible labor that practitioners take on as part of this bridging and piggybacking work to enact interdisciplinary collaboration for fairness. We close by discussing opportunities for both FAccT researchers and AI practitioners to better support cross-functional collaboration for fairness in the design and development of AI systems. 

In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. While different research communities may use different terminol- ogy to discuss similar topics, all of this work is ultimately aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map cur- rent and future research trends to advance this important area of research by fostering collaboration and sharing ideas.