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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. 

Crowdsourcing markets are expanding worldwide, but often feature standardized interfaces that ignore the cultural diversity of their workers, negatively impacting their well-being and productivity. To transform these workplace dynamics, this paper proposes creating culturally-aware workplace tools, specifically designed to adapt to the cultural dimensions of monochronic and polychronic work styles. We illustrate this approach with CultureFit, a tool that we engineered based on extensive research in Chronemics and culture theories. To study and evaluate our tool in the real world, we conducted a field experiment with 55 workers from 24 different countries. Our field experiment revealed that CultureFit significantly improved the earnings of workers from cultural backgrounds often overlooked in design. Our study is among the pioneering efforts to examine culturally aware digital labor interventions. It also provides access to a dataset with over two million data points on culture and digital work, which can be leveraged for future research in this emerging field. The paper concludes by discussing the importance and future possibilities of incorporating cultural insights into the design of tools for digital labor. 

Research has primarily focused on understanding the perspectives and experiences of US based crowdworkers, leaving a notable gap to those within the Global South. To bridge this, we conducted a survey with 100 crowdworkers across 16 Latin American and Caribbean countries. To understand the tensions between the crowdworking economy abroad. In our study we found crowd work being the stepping stone to financial and professional independence among those we surveyed along with the tensions within data extraction among crowdworkers. 

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. 

This paper studies the design of an AI tool that supports gig knowledge workers, rather than displacing them, focusing on text-based generative AI technologies. Through a formative study involving interviews and design activities, gig workers shared their views on text-based generative AI and envisioned applications where AI acts as managers, secretaries, and communication aids. Leveraging these insights, we created a generative-AI enhanced tool, Office-Mind AI, to aid gig workers. Our research advances the conversation around algorithmic labor by designing a worker-focused intelligent tool. This tool harness collective intelligence among workers and AI, fostering productive human-AI partnerships. We conclude by discussing the future prospects of collective intelligence tools designed for worker-AI collaborations. 

Collective action by gig knowledge workers is a potent method for enhancing labor conditions on platforms like Upwork, Amazon Mechanical Turk, and Toloka. However, this type of collective action is still rare today. Existing systems for supporting collective action are inadequate for workers to identify and understand their different workplace problems, plan effective solutions, and put the solutions into action. This talk will discuss how with my research lab we are creating worker-centric AI enhanced technologies that enable collective action among gig knowledge workers. Building solid AI enhanced technologies to enable gig worker collective action will pave the way for a fair and ethical gig economy—one with fair wages, humane working conditions, and increased job security. I will discuss how my proposed approach involves first integrating "sousveillance," a concept by Foucault, into the technologies. Sousveillance involves individuals or groups using surveillance tools to monitor and record those in positions of power. In this case, the technologies enable gig workers to monitor their workplace and their algorithmic bosses, giving them access to their own workplace data for the first time. This facilitates the first stage of collective action: problem identification. I will then discuss how we combine this data with Large-Language-Models (LLMs) and social theories to create intelligent assistants that guide workers to complete collective action via sensemaking and solution implementation. The talk will present a set of case studies to showcase this vision of designing data driven AI technologies to power gig worker collective action. In particular, I will present the systems: 1) GigSousveillance which allows workers to monitor and collect their own job-related data, facilitating quantification of workplace problems; 2) GigSense equips workers with an AI assistant that facilitates sensemaking of their work problems, helping workers to strategically devise solutions to their challenges; 3) GigAction is an AI assistant that guides workers to implement their proposed solutions. I will discuss how we are designing and implementing these systems by adopting a participatory design approach with workers, while also conducting experiments and longitudinal deployments in the real world. I conclude by presenting a research agenda for transforming and rethinking the role of A.I. in our workplaces; and researching effective socio-technical solutions in favor of a worker-centric future and countering technoauthoritarianism 

As independently contracted employees, gig workers disproportionately suffer the consequences of workplace surveillance, which include increased pressures to work, breaches of privacy, and decreased digital autonomy. Despite the negative impacts of workplace surveillance, gig workers lack the tools, strategies, and workplace social support to protect themselves against these harms. Meanwhile, some critical theorists have proposed sousveillance as a potential means of countering such abuses of power, whereby those under surveillance monitor those in positions of authority (e.g., gig workers collect data about requesters or platforms). To understand the benefits of sousveillance systems in the gig economy, we conducted semi-structured interviews and led co-design activities with gig workers. We use care ethics as a guiding concept to understand our interview and co-design data, while also focusing on empathic sousveillance technology design recommendations. Through our study, we identify gig workers' attitudes towards and past experiences with sousveillance. We also uncover the types of sousveillance technologies imagined by workers, provide design recommendations, and finish by discussing how to create empowering, empathic spaces on gig platforms. 

AI has revolutionized the processing of various services, including the automatic facial verification of people. Automated approaches have demonstrated their speed and efficiency in verifying a large volume of faces, but they can face challenges when processing content from certain communities, including communities of people of color. This challenge has prompted the adoption of "human-inthe-loop" (HITL) approaches, where human workers collaborate with the AI to minimize errors. However, most HITL approaches do not consider workers’ individual characteristics and backgrounds. This paper proposes a new approach, called Inclusive Portraits (IP), that connects with social theories around race to design a racially-aware human-in-the-loop system. Our experiments have provided evidence that incorporating race into human-in-the-loop (HITL) systems for facial verification can significantly enhance performance, especially for services delivered to people of color. Our findings also highlight the importance of considering individual worker characteristics in the design of HITL systems, rather than treating workers as a homogenous group. Our research has significant design implications for developing AI-enhanced services that are more inclusive and equitable. 

Crowd-work has increased significantly in recent years, particularly among women from Latin America. However, the specific needs and characteristics of this workforce have not been studied nearly enough. For this reason, we have conducted a series of surveys, questionnaires, and design sessions directly with Latin-American users of crowd-working platforms. Our aim was to create a system to empower crowd-workers with AI enhanced tools for their day-to-day tasks. As a result, we created a customized platform, La Independiente, and two web plugins. This project is unique in that it leverages gender perspective methodologies, AI powered-systems, and public policy analysis to design smart tools that are both professionally useful and culturally relevant.