What happens when a rideshare driver is suddenly locked out of the platform connecting them to riders, wages, and daily work? Deactivation—the abrupt removal of gig workers’ platform access—typically occurs via arbitrary AI and algorithmic decisions with little explanation or recourse. This represents one of the most severe forms of algorithmic control and often devastates workers’ financial stability. Recent U.S. state policies now mandate appeals processes and recovering compensation during periods of wrongful deactivation based on past earnings. Yet, labor organizers still lack effective tools to support these complex, error-prone workflows. We designed FareShare , a computational tool for automating lost wages estimation for deactivated drivers, through a 6-month partnership with the State of Washington’s largest rideshare labor union. Our 3-month field deployment yielded 178 worker account signups. We observed that the tool could reduce lost wages calculation time by over 95%, eliminate manual data entry errors, and enable legal teams to generate arbitration-ready reports more efficiently. Beyond these gains, the deployment also surfaced important socio-technical challenges around trust, consent, and tool adoption in high-stakes labor contexts.

Rideshare workers experience unpredictable working conditions due to gig work platforms’ reliance on opaque AI and algorithmic systems. In response to these challenges, we found that labor organizers want data to help them advocate for legislation to increase the transparency and accountability of these platforms. To address this need, we collaborated with a Colorado-based rideshare union to develop FairFare , a tool that crowdsources and analyzes workers’ data to estimate the “take rate”—the percentage of the rider price retained by the rideshare platform. We deployed FairFare with our partner organization that collaborated with us in collecting data on 76,000+ trips from 45 drivers over 18 months. During evaluation interviews, organizers reported that FairFare helped influence state-level advocacy. Finally, we reflect on the complexities of translating quantitative data into policy outcomes, the nature of community-based audits, and the design implications for future transparency tools.

This paper provides guidance for building and maintaining infrastructure for participatory AI efforts by sharing reflections on building World Wide Dishes (WWD), a bottom-up, community-led image and text dataset of culinary dishes and associated cultural customs. We present WWD as an example of participatory dataset creation, where community members both guide the design of the research process and contribute to the crowdsourced dataset. This approach incorporates localised expertise and knowledge to address the limitations of web-scraped Internet datasets acknowledged in the Participatory AI discourse. We show that our approach can result in curated, high-quality data that supports decentralised contributions from communities that do not typically contribute to datasets due to a variety of systemic factors. Our project demonstrates the importance of participatory mediators in supporting community engagement by identifying the kinds of labour they performed to make WWD possible. We surface three dimensions of labour performed by participatory mediators that are crucial for participatory dataset construction: building trust with community members, making participation accessible, and contextualising community values to support meaningful data collection. Drawing on our findings, we put forth five lessons for building infrastructure to support future participatory AI efforts.

Rideshare platforms exert significant control over workers through algorithmic systems that can result in financial, emotional, and physical harm. What steps can platforms, designers, and practitioners take to mitigate these negative impacts and meet worker needs? In this paper, we identify transparency-related harms, mitigation strategies, and worker needs while validating and contextualizing our findings within the broader worker community. We use a novel mixed-methods study combining an LLM-based analysis of over 1 million comments posted to online platform worker communities with semi-structured interviews with workers. Our findings expose a transparency gap between existing platform designs and the information drivers need, particularly concerning promotions, fares, routes, and task allocation. Our analysis suggests that rideshare workers need key pieces of information, which we refer to as indicators , to make informed work decisions. These indicators include details about rides, driver statistics, algorithmic implementation details, and platform policy information. We argue that instead of relying on platforms to include such information in their designs, new regulations requiring platforms to publish public transparency reports may be a more effective solution to improve worker well-being. We offer recommendations for implementing such a policy.

This paper examines the sociotechnical infrastructure of an “indie” food delivery platform. The platform, Nosh, provides an alternative to mainstream services, such as Doordash and Uber Eats, in several communities in the Western United States. We interviewed 28 stakeholders including restauranteurs, couriers, consumers, and platform administrators. Drawing on infrastructure literature, we learned that the platform is a patchwork of disparate technical systems held together by human intervention. Participants join this platform because they receive greater agency, financial security, and local support. We identify human intervention’s key role in making food delivery platform users feel respected. This study provides insights into the affordances, limitations, and possibilities of food delivery platforms designed to prioritize local contexts over transnational scales.

Accessibility efforts, how we can make the world usable and useful to as many people as possible, have focused on how we can support and allow for the autonomy and independence of people with disabilities, neurodivergencies, chronic conditions, and older adults. Despite these efforts, not all technology is designed or implemented to support everyone's needs. TikTok. Recently, a community-organized push by creators and general users of TikTok urged the platform to add accessibility features, such as closed captioning, to allow more people to use the platform with greater ease. Through an interview study exploring the experiences of creatives on TikTok, we explore the creative practices of people with ADHD and those who experience similar challenges, focusing on the kinds of accessibility they create through their creative work. We find that creatives engage in critical infrastructuring--a process of bottom-up (re)design--to make the platform more accessible despite the challenges the platform presents to them as creators through creating and augmenting video editing and video captioning infrastructures. We then reflect on how the introduction of a top-down infrastructure - the implementation of an auto-captioning feature - shifts the critical infrastructure practices of TikTok creatives. Through their infrastructuring, creatives were revising the broader sociotechnical capabilities of TikTok to support their own needs as well as the broader needs of the TikTok community. We discuss how the routine of infrastructuring accessibility is a form of incidental care work and highlight how accessibility is an evolving sociotechnical construct, forwarding the concept of contextual accessibility.