As generative AI tools increasingly influence creative practice, they raise longstanding HCI questions about how creatives learn complex software and how they can be better supported. We conducted an interview study with artists and hobbyists (n=8) and a follow-up survey (n=159) to understand how this population approaches and seeks guidance for GenAI image tools. We found that creatives commonly use either self-experimentation or tutorials to explore GenAI tools, yet many struggle with confusing AI terminology. To gain further insight into creatives’ learning experiences, we developed a research probe to elicit creatives’ perceptions of structured guidance. Our user study with 17 creatives revealed that, even when creatives described the guidance as helpful for understanding AI, many still preferred self-experimentation, feeling that guidance could limit their creativity. Our findings highlight a central tension in supporting AI literacy for creatives: balancing guidance and promoting literacy while preserving creative freedom.

Developing AI literacy is increasingly urgent as generative AI reshapes creative practice. Yet most AI literacy frameworks are top-down and expert-driven, overlooking how literacy emerges organically in creative communities. To address this gap, we performed a large-scale analysis of 122k Reddit conversations from 80 creative-oriented subreddits over a time period of three years. Our analysis identified four consistent themes in AI literacy-related discussions, and we further traced how discourse shifted alongside major AI events. Surprisingly, creators primarily frame AI literacy around how to use tools effectively—foregrounding practice and task skills—while discussions of AI capabilities and ethics surge only around high-profile events. Our findings suggest that AI literacy is dynamic, practice-driven, and event-responsive rather than static or purely conceptual. This study provides insights for researchers, designers, and policymakers to develop learning resources, community support, and policies that better promote AI literacy in creative communities.

This position paper argues that there is an urgent need to restructure markets for the information that goes into AI systems. Specifically, small-to-medium sized producers of information (such as journalists, news organizations, researchers, and creative professionals) need to be able to appoint representatives who can carry out "collective bargaining" with AI product builders in order to receive a reasonable terms and a fair return on the informational value they contribute. Obstacles to this market structure can be removed through technical work that facilitates collective bargaining in the information economy (e.g., explainable data value estimation and federated data management tools) and regulatory/policy interventions (e.g., support for trusted data intermediary organizations that represent guilds or syndicates of information producers). We argue that without collective bargaining in the information economy, AI will exacerbate a large-scale "information market failure" that will lead not only to undesirable concentration of capital, but also to a potential "ecological collapse" in the informational commons. On the other hand, collective bargaining in the information economy can create market conditions necessary for a pro-social AI future. We provide concrete actions that can be taken to support a coalition-based approach to achieve this.

Systemic property dispossession from minority groups has often been carried out in the name of technological progress. In this paper, we identify evidence that the current paradigm of large language models (LLMs) likely continues this long history. Examining common LLM training datasets, we find that a disproportionate amount of content authored by Jewish Americans is used for training without their consent. The degree of over-representation ranges from around 2x to around 6.5x. Given that LLMs may substitute for the paid labor of those who produced their training data, they have the potential to cause even more substantial and disproportionate economic harm to Jewish Americans in the coming years. This paper focuses on Jewish Americans as a case study, but it is probable that other minority communities (e.g., Asian Americans, Hindu Americans) may be similarly affected and, most importantly, the results should likely be interpreted as a “canary in the coal mine” that highlights deep structural concerns about the current LLM paradigm whose harms could soon affect nearly everyone. We discuss the implications of these results for the policymakers thinking about how to regulate LLMs as well as for those in the AI field who are working to advance LLMs. Our findings stress the importance of working together towards alternative LLM paradigms that avoid both disparate impacts and widespread societal harms.

Internet users have formed a wide array of online communities with diverse community goals and nuanced norms. However, most online platforms only offer a limited set of governance models in their software infrastructure and leave little room for customization. Consequently, technical proficiency becomes a prerequisite for online communities to build governance policies in code, excluding non-programmers from participation in designing community governance. In this paper, we present Pika, a system that empowers non-programmers to author a wide range of executable governance policies. At its core, Pika incorporates a declarative language that decomposes governance policies into modular components, thereby facilitating expressive policy authoring through a user-friendly, form-based web interface. Our user studies with 10 non-programmers and 7 programmers show that Pika can empower non-programmers to author policies approximately 2.5 times faster than programmers who author in code. We also provide insights about Pika’s expressivity in supporting diverse policies online communities want.

Peer production systems have frictions-mechanisms that make contributing more effortful-to prevent vandalism and protect information quality. Page protection on Wikipedia is a mechanism where the platform's core values conflict, but there is little quantitative work to ground deliberation. In this paper, we empirically explore the consequences of page protection on Internet Culture articles on Wikipedia (6,264 articles, 108 edit-protected). We first qualitatively analyzed 150 requests for page protection, finding that page protection is motivated by an article's (1) activity, (2) topic area, and (3) visibility. These findings informed a matching approach to compare protected pages and similar unprotected articles. We quantitatively evaluate the differences between protected and unprotected pages across two dimensions: editor engagement and contributor concentration. Protected articles show different trends in editor engagement and equity amongst contributors, affecting the overall disparity in the population. We discuss the role of friction in online platforms, new ways to measure it, and future work.

Showing ads delivers revenue for online content distributors, but ad exposure can compromise user experience and cause user fatigue and frustration. Correctly balancing ads with other content is imperative. Currently, ad allocation relies primarily on demographics and inferred user interests, which are treated as static features and can be privacy-intrusive. This paper uses person-centric and momentary context features to understand optimal ad-timing. In a quasi-experimental study on a three-month longitudinal dataset of 100K Snapchat users, we find ad timing influences ad effectiveness. We draw insights on the relationship between ad effectiveness and momentary behaviors such as duration, interactivity, and interaction diversity. We simulate ad reallocation, finding that our study-driven insights lead to greater value for the platform. This work advances our understanding of ad consumption and bears implications for designing responsible ad allocation systems, improving both user and platform outcomes. We discuss privacy-preserving components and ethical implications of our work.

A growing body of work has highlighted the important role that Wikipedia's volunteer-created content plays in helping search engines achieve their core goal of addressing the information needs of hundreds of millions of people. In this paper, we report the results of an investigation into the incidence of Wikipedia links in search engine results pages (SERPs). Our results extend prior work by considering three U.S. search engines, simulating both mobile and desktop devices, and using a spatial analysis approach designed to study modern SERPs that are no longer just "ten blue links". We find that Wikipedia links are extremely common in important search contexts, appearing in 67-84% of desktop SERPs for common and trending queries, but less often for medical queries. Furthermore, we observe that Wikipedia links often appear in "Knowledge Panel" SERP elements and are in positions visible to users without scrolling, although Wikipedia appears less often and in less prominent positions on mobile devices. Our findings reinforce the complementary notions that (1) Wikipedia content and research has major impact outside of the Wikipedia domain and (2) powerful technologies like search engines are highly reliant on free content created by volunteers.

Tech users currently have limited ability to act on concerns regarding the negative societal impacts of large tech companies. However, recent work suggests that users can exert leverage using their role in the generation of valuable data, for instance by withholding their data contributions to intelligent technologies. We propose and evaluate a new means to exert this type of leverage against tech companies: "conscious data contribution" (CDC). Users who participate in CDC exert leverage against a target tech company by contributing data to technologies operated by a competitor of that company. Using simulations, we find that CDC could be highly effective at reducing the gap in intelligent technologies performance between an incumbent and their competitors. In some cases, just 20% of users contributing data they have produced to a small competitor could help that competitor get 80% of the way towards the original company's best-case performance. We discuss the implications of CDC for policymakers, tech designers, and researchers.

The public is increasingly concerned about the practices of large technology companies with regards to privacy and many other issues. To force changes in these practices, there have been growing calls for “data strikes.” These new types of collective action would seek to create leverage for the public by starving business-critical models (e.g. recommender systems, ranking algorithms) of much-needed training data. However, little is known about how data strikes would work, let alone how effective they would be. Focusing on the important commercial domain of recommender systems, we simulate data strikes under a wide variety of conditions and explore how they can augment traditional boycotts. Our results suggest that data strikes can be effective and that users have more power in their relationship with technology companies than they do with other companies. However, our results also highlight important trade-offs and challenges that must be considered by potential organizers.

Researchers and the media have become increasingly interested in protest users, or people who change (protest use) or stop (protest non-use) their use of a company's products because of the company's values and/or actions. Past work has extensively engaged with the phenomenon of technology non-use but has not focused on non-use (nor changed use) in the context of protest. With recent research highlighting the potential for protest users to exert leverage against technology companies, it is important for technology stakeholders to understand the prevalence of protest users, their motivations, and the specific tactics they currently use. In this paper, we report the results of two surveys (n = 463 and n = 398) of representative samples of American web users that examine if, how, and why people have engaged in protest use and protest non-use of the products of five major technology companies. We find that protest use and protest non-use are relatively common, with 30% of respondents in 2019 reporting they were protesting at least one major tech company. Furthermore, we identify that protest users' most common motivations were (1) concerns about business models that profit from user data and (2) privacy; and the most common tactics were (1) stopping use and (2) leveraging ad blockers. We also identify common challenges and roadblocks faced by active and potential protest users, which include (1) losing social connections and (2) the lack of alternative products. Our results highlight the growing importance of protest users in the technology ecosystem and the need for further social computing research into this phenomenon. We also provide concrete design implications for existing and future technologies to support or account for protest use and protest non-use.

The extensive Wikipedia literature has largely considered Wikipedia in isolation, outside of the context of its broader Internet ecosystem. Very recent research has demonstrated the significance of this limitation, identifying critical relationships between Google and Wikipedia that are highly relevant to many areas of Wikipedia-based research and practice. This paper extends this recent research beyond search engines to examine Wikipedia's relationships with large-scale online communities, Stack Overflow and Reddit in particular. We find evidence of consequential, albeit unidirectional relationships. Wikipedia provides substantial value to both communities, with Wikipedia content increasing visitation, engagement, and revenue, but we find little evidence that these websites contribute to Wikipedia in return. Overall, these findings highlight important connections between Wikipedia and its broader ecosystem that should be considered by researchers studying Wikipedia. Critically, our results also emphasize the key role that volunteer-created Wikipedia content plays in improving other websites, even contributing to revenue generation.

In many traditional labor markets, women earn less on average compared to men. However, it is unclear whether this discrepancy persists in the online gig economy, which bears important differences from the traditional labor market (e.g., more flexible work arrangements, shorter-term engagements, reputation systems). In this study, we collected self-determined hourly bill rates from the public profiles of 48,019 workers in the United States (48.8% women) on Upwork, a popular gig work platform. The median female worker set hourly bill rates that were 74% of the median man's hourly bill rates, a gap than cannot be entirely explained by online and offline work experience, education level, and job category. However, in some job categories, we found evidence of a more complex relationship between gender and earnings: women earned more overall than men by working more hours, outpacing the effect of lower hourly bill rates. To better support equality in the rapidly growing gig economy, we encourage continual evaluation of the complex gender dynamics on these platforms and discuss whose responsibility it is to address inequalities.

In acute ischemic stroke treatment, prediction of tissue survival outcome plays a fundamental role in the clinical decision-making process, as it can be used to assess the balance of risk vs. possible benefit when considering endovascular clot-retrieval intervention. For the first time, we construct a deep learning model of tissue fate based on randomly sampled local patches from the hypoperfusion (Tmax) feature observed in MRI immediately after symptom onset. We evaluate the model with respect to the ground truth established by an expert neurologist four days after intervention. Experiments on 19 acute stroke patients evaluated the accuracy of the model in predicting tissue fate. Results show the superiority of the proposed regional learning framework versus a single-voxel-based regression model.

Hyperperfusion detected on arterial spin labeling (ASL) images acquired after acute stroke onset has been shown to correlate with development of subsequent intracerebral hemorrhage. We present in this study a quantitative hyperperfusion detection model that can provide an objective decision support for the interpretation of ASL cerebral blood flow (CBF) maps and rapidly delineate hyperperfusion regions. The detection problem is solved using Deep Learning such that the model relates ASL image patches to the corresponding label (normal or hyperperfused). Our method takes into account the regional intensity values of contralateral hemisphere during the labeling of a pixel. Each input vector is associated to a label corresponding to the presence of hyperperfusion that was manually established by a clinical researcher in Neurology. When compared to the manually established hyperperfusion, the predicted maps reached an accuracy of 97.45 ± 2.49% after crossvalidation. Pattern recognition based on deep learning can provide an accurate and objective measure of hyperperfusion on ASL CBF images and could therefore improve the detection of hemorrhagic transformation in acute stroke patients.