Human-Computer Interaction practitioners have been proposing best practices in user interface design for decades. However, generative Artificial Intelligence (GenAI) brings additional design considerations and currently lacks sufficient user guidance regarding affordances, inputs, and outputs. In this context, we developed a recommender system to promote responsible AI (RAI) practices while people prompt GenAI systems. We detail 10 interviews with IT professionals, the resulting recommender system developed, and 20 user sessions with IT professionals interacting with our prompt recommendations. Results indicate that responsible prompting recommendations have the potential to support novice prompt engineers and raise awareness about RAI in prompting-time. They also suggest that recommendations should simultaneously maximize both a prompt’s similarity to a user’s input as well as a diversity of associated social values provided. These findings contribute to RAI by offering practical ways to provide user guidance and enrich human-GenAI interaction via prompt recommendations.

Human-Computer Interaction practitioners have been proposing best practices in user interface design for decades. However, generative Artificial Intelligence (GenAI) brings additional design considerations and currently lacks sufficient user guidance regarding affordances, inputs, and outputs. In this context, we developed a recommender system to promote responsible AI (RAI) practices while people prompt GenAI systems, by recommending addition of sentences based on social values and removal of harmful sentences. We detail a lightweight recommender system designed to be used in prompting-time and compare its recommendations to the ones provided by three base large language models (LLMs) and two LLMs fine-tuned for the task, i.e., recommending inclusion of sentences based on social values and removal of harmful sentences from a given prompt. Results indicate that our approach has the best F1-score balance in terms of recommendations for additions and removal of sentences to promote responsible prompts, while a fine-tuned model obtained the best F1-score for additions, and our approach obtained the best F1-score for removals of harmful sentences. In addition, fine-tuned models improved the objectiveness of responses by reducing the verbosity of generated content in 93% when compared to the content generated by base models. Presented findings contribute to RAI by showing the limits and bias of existing LLMs in terms of recommendations on how to create more responsible prompts and how open-source technologies can fill this gap in prompting-time.

In this paper, we gather 12 workers from a large technology company, as recent participants of a research initiative on the social impact of emerging technologies, to present a collaborative analysis of the opportunities and limitations of dissensus-based approaches to technology research and design. We introduce a series of speculative and deconstructive probes and present findings from their use in four collaborative design sessions. We then draw on the theoretical tradition of Agonism to identify moments of friction, refusal, and disagreement over the course of these sessions. We contend that this approach offers a politically important alternative to consensus-based collaborative design methods and can even surface new rhetorics of contestation within discourses on technology futures. We conclude with a discussion of the importance of worker-authored research and an initial set opportunities, challenges, and paradoxes as a resource for future efforts to "Design for Agonism."

Societal implications of technology are often considered after public deployment. However, broader impacts ought to be considered during the onset and throughout development to reduce potential for harmful uses, biases, and exclusions. There is a need for tools and frameworks that help technologists become more aware of broader contexts of their work and engage in more responsible and inclusive practices. In this paper, we introduce an online card tool containing questions to scaffold critical reflection about projects’ impacts on society, business, and research. We present the iterative design of the Responsible & Inclusive Cards and findings from five workshops (n=21 participants) with teams distributed across a multinational technology corporation, as well as interviews with people with disabilities to assess gameplay and mental models. We found the tool promoted discussions about challenging topics, reduced power gaps through democratized turn-taking, and enabled participants to identify concrete areas to improve their practice.