We investigate how the presence and type of interaction context shapes sycophancy in LLMs. While real-world interactions allow models to mirror a user’s values, preferences, and self-image, prior work often studies sycophancy in zero-shot settings devoid of context. Using two weeks of interaction context from 38 users, we evaluate two forms of sycophancy: (1) agreement sycophancy — the tendency of models to produce overly affirmative responses, and (2) perspective sycophancy — the extent to which models reflect a user’s viewpoint. Agreement sycophancy tends to increase with the presence of user context, though model behavior varies based on the context type. User memory profiles are associated with the largest increases in agreement sycophancy (e.g. + 45% for Gemini 2.5 Pro), and some models become more sycophantic even with non-user synthetic contexts (e.g. + 15% for Llama 4 Scout). Perspective sycophancy increases only when models can accurately infer user viewpoints from interaction context. Overall, context shapes sycophancy in heterogeneous ways, underscoring the need for evaluations grounded in real-world interactions and raising questions for system design around alignment, memory, and personalization.

Despite high approval ratings for unions and growing worker interest in organizing, employees in the United States still face significant barriers to securing collective bargaining agreements. A key factor is employer counter-organizing: efforts to suppress unionization through rule changes, retaliation, and disruption. Designing sociotechnical tools and strategies to resist these tactics requires a deeper understanding of the role computing technologies play in counter-organizing against unionization. In this paper, we examine three high-profile organizing efforts—at Amazon, Starbucks, and Boston University—using publicly available sources to identify four recurring technological tactics: surveillance, spacing, screaming and scabbing. We analyze how these tactics operate across contexts, highlighting their digital dimensions and strategic deployment. We conclude with implications for organizing in digitally-mediated workplaces, directions for future research, and emergent forms of worker resistance.

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.

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.

We investigate the phenomenon of norm inconsistency: where LLMs apply different norms in similar situations. Specifically, we focus on the high-risk application of deciding whether to call the police in Amazon Ring home surveillance videos. We evaluate the decisions of three state-of-the-art LLMs — GPT-4, Gemini 1.0, and Claude 3 Sonnet — in relation to the activities portrayed in the videos, the subjects' skin-tone and gender, and the characteristics of the neighborhoods where the videos were recorded. Our analysis reveals significant norm inconsistencies: (1) a discordance between the recommendation to call the police and the actual presence of criminal activity, and (2) biases influenced by the racial demographics of the neighborhoods. These results highlight the arbitrariness of model decisions in the surveillance context and the limitations of current bias detection and mitigation strategies in normative decision-making.

Data generated by users on digital platforms are a crucial resource for advocates and researchers interested in uncovering digital inequities, auditing algorithms, and understanding human behavior. Yet data access is often restricted. How can researchers both effectively and ethically collect user data? This paper shares an innovative approach to crowdsourcing user data to collect otherwise inaccessible Amazon purchase histories, spanning 5 years, from more than 5,000 U.S. users. We developed a data collection tool that prioritizes participant consent and includes an experimental study design. The design allows us to study multiple important aspects of privacy perception and user data sharing behavior, including how socio-demographics, monetary incentives and transparency can impact share rates. Experiment results (N=6,325) reveal both monetary incentives and transparency can significantly increase data sharing. Age, race, education, and gender also played a role, where female and less-educated participants were more likely to share. Our study design enables a unique empirical evaluation of the 'privacy paradox', where users claim to value their privacy more than they do in practice. We set up both real and hypothetical data sharing scenarios and find measurable similarities and differences in share rates across these contexts. For example, increasing monetary incentives had a 6 times higher impact on share rates in real scenarios. In addition, we study participants' opinions on how data should be used by various third parties, again finding that gender, age, education, and race have a significant impact. Notably, the majority of participants disapproved of government agencies using purchase data yet the majority approved of use by researchers. Overall, our findings highlight the critical role that transparency, incentive design, and user demographics play in ethical data collection practices, and provide guidance for future researchers seeking to crowdsource user generated data.

In 2020, Google announced it would disable third-party cookies in the Chrome browser to improve user privacy. In order to continue to enable interest-based advertising while mitigating risks of individualized user tracking, Google proposed FLoC. The FLoC algorithm assigns users to "cohorts" that represent groups of users with similar browsing behaviors so that ads can be served to users based on their cohort. In 2022, after testing FLoC in a real world trial, Google canceled the proposal with little explanation in favor of another way to enable interest-based advertising. This work provides a post-mortem analysis of two critical privacy risks for FloC by applying an implementation of FLoC to a real-world browsing history dataset collected from over 90,000 U.S. devices over a one year period.

The increasing prevalence of large-scale labor aggregation platforms, worker analytics, and algorithmic decision-making by management raises the question of whether workers can use similar technologies to advocate for their own goals. Yet, there are inherent challenges in building worker-centric tools that collect, aggregate, and share data in responsible and ethical ways. In this paper, we present the design and deployment of the Shipt Calculator, a tool developed in collaboration with non-profit worker groups that allows app-based delivery workers to track and share aggregate data about their pay, increasing wage transparency. We first discuss the design challenges inherent to building worker-centric technologies, particularly for informally organized workers, and ground our discussion in the history of worker inquiry and co-research. We then describe some principles from this history and our own lessons in designing the Calculator that can be applied by future researchers and advocates seeking to build technical tools for organizing campaigns. Finally, we share the results of using the Calculator to audit an app's shift to a black-box pay model using data contributed by 140 workers in the Summer of 2020, finding that although the average pay per-order increased under the new payment model, almost half of workers experienced an unannounced pay cut during the shift, and many workers worked shifts that paid under their state's minimum wage. Finally, we discuss how tools like the Calculator demonstrate the important role that aggregate worker data, and a new Digital Workerism, can serve in creating and maintaining a more balanced platform economy.

Consumer surveillance products such as 'smart' doorbell cameras are an already-pervasive phenomenon in the U.S. These devices are marketed as personal and community security tools that allow users to answer their front door remotely, record "suspicious activity" captured by their cameras, and share reports with neighbors. The widespread use of doorbell cameras specifically, however, has created an opaque, wide-reaching surveillance network used by thousands of law enforcement agencies nationwide. The full breadth of this network and how users operate on such platforms is largely unknown. Amazon Ring, one of the largest manufacturers of smart doorbells, offers a companion social networking app to their physical doorbells called Ring Neighbors that allows camera owners to share video and text posts with other camera owners that live nearby. In this paper, we use data collected from public posts on Neighbors to create the first comprehensive map and analysis of smart doorbell camera use across the continental U.S. We use spatial regression methods to estimate the county-level predictors of Neighbors app usage nationally. We then use Los Angeles, one of the most active areas of Ring usage in the country, as a case study to investigate how different neighborhoods in a racially heterogeneous city use a platform like Ring. Using a structured topic analysis and experimental survey design, we show that users actively frame video subjects as criminal and suspicious, that the race of a neighborhood has a significant impact on posting rates, and provide some evidence that Neighbors may be used as a racial gatekeeping tool, particularly by white neighborhoods that border non-white areas in Los Angeles.

Oren Tsur, Dan Calacci, David Lazer. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.