Unlike with traditional predictive AI models, today’s generative AI models are increasingly designed to be general-purpose, able to perform a wide range of tasks. This makes it challenging to develop a reliable and useful understanding of the ways in which this technology is and could be used. As a result, academic and policy researchers and generative AI providers have started to publish the results of their own investigations about the use of generative AI. This information is, however, fragmented, potentially incomplete, sometimes ambiguous, and often lacking in methodological specificity. In this paper, we conducted an integrative review to build a multi-dimensional framework that specifies what kind of information about generative AI use could be reported and how, and illustrated its analytical utility by applying the framework to a collection of over 110 industry documents. Our analysis reveals systematic patterns and omissions in current industry reporting and reflects on the narratives this reporting collectively advance about generative AI use.

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.

Concept inventories are standardized assessments that evaluate student understanding of key concepts within academic disciplines. While prevalent across STEM fields, their development lags for advanced computer science topics like dynamic programming (DP)---an algorithmic technique that poses significant conceptual challenges for undergraduates. To fill this gap, we developed and validated a Dynamic Programming Concept Inventory (DPCI). We detail the iterative process used to formulate multiple-choice questions targeting known student misconceptions about DP concepts identified through prior research studies. We discuss key decisions, tradeoffs, and challenges faced in crafting probing questions to subtly reveal these conceptual misunderstandings. We conducted a preliminary psychometric validation by administering the DPCI to 172 undergraduate CS students finding our questions to be of appropriate difficulty and effectively discriminating between differing levels of student understanding. Taken together, our validated DPCI will enable instructors to accurately assess student mastery of DP. Moreover, our approach for devising a concept inventory for an advanced theoretical computer science concept can guide future efforts to create assessments for other under-evaluated areas currently lacking coverage.

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.

Algorithms courses are a core part of many CS programs, but have received little focus in computing education, lacking statistical data about how they are generally taught. To remedy this, we present the results of the first large-scale comprehensive survey of undergraduate introductory algorithms courses at four-year institutions in the United States. Questions in the survey targeted instructor information, course concepts, the ways students are evaluated, challenges instructors encountered, and instructor envisioned improvements. We received 87 responses from 34 different states, across a wide variety of 4-year institutions. The results indicate that algorithms courses vary dramatically in most surveyed areas.