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The rapid adoption of generative AI in software development has impacted the industry, yet its efects on developers with visual impairments remain largely unexplored. To address this gap, we used an Activity Theory framework to examine how developers with visual impairments interact with AI coding assistants. For this purpose, we conducted a study where developers who are visually impaired completed a series of programming tasks using a generative AI coding assistant. We uncovered that, while participants found the AI assistant benefcial and reported signifcant advantages, they also highlighted accessibility challenges. Specifcally, the AI coding assistant often exacerbated existing accessibility barriers and introduced new challenges. For example, it overwhelmed users with an excessive number of suggestions, leading developers who are visually impaired to express a desire for “AI timeouts.” Additionally, the generative AI coding assistant made it more difcult for developers to switch contexts between the AI-generated content and their own code. Despite these challenges, participants were optimistic about the potential of AI coding assistants to transform the coding experience for developers with visual impairments. Our fndings emphasize the need to apply activity-centered design principles to generative AI assistants, ensuring they better align with user behaviors and address specifc accessibility needs. This approach can enable the assistants to provide more intuitive, inclusive, and efective experiences, while also contributing to the broader goal of enhancing accessibility in software development 

Collective intelligence among gig workers yields considerable ad- vantages, including improved information exchange, deeper social bonds, and stronger advocacy for better labor conditions. Especially as it enables workers to collaboratively pinpoint shared challenges and devise optimal strategies for addressing these issues. However, enabling collective intelligence remains challenging, as existing tools often overestimate gig workers’ available time and uniformity in analytical reasoning. To overcome this, we introduce GigSense, a tool that leverages large language models alongside theories of collective intelligence and sensemaking. GigSense enables gig workers to rapidly understand and address shared challenges effectively, irrespective of their diverse backgrounds. GigSense not only empowers gig workers but also opens new possibilities for supporting workers more broadly, demonstrating the potential of large language model interfaces to enhance collective intelligence efforts in the evolving workplace. 

As we enter an era where the synergy between AI technologies and human effort is paramount, the Future of Work is undergoing a radical transformation. Emerging AI tools will profoundly influence how we work, the tools we use, and the very nature of work itself. The ’Human Computation, Equitable, and Innovative Future of Work AI Tools’ workshop at HCOMP’24 aims to explore groundbreaking solutions for developing fair and inclusive AI tools that shape how we will work. This workshop will delve into the collaborative potential of human computation and artificial intelligence in crafting equitable Future of Work AI tools. Participants will critically examine the current challenges in designing fair and innovative AI systems for the evolving workplace, as well as strategies for effectively integrating human insights into these tools. The primary objective is to foster a rich discourse on scalable, sustainable solutions that promote equitable Future of Work tools for all, with a particular focus on empowering marginalized communities. By bringing together experts from diverse fields, we aim to catalyze ideas that bridge the gap between technological advancement and social equity.