Gathering information about AI systems is essential for contesting their use; it forms the basis of arguments about how and to what extent AI is causing harm. Information thus plays a central role for advocates like lawyers, journalists, and auditors contesting harmful AI systems. However, there is little systematic understanding of how these actors, many of whom are newly encountering AI in their advocacy work, access and use information effectively in this process. Understanding this information work can offer valuable insights for supporting effective contestation of harmful AI systems—work that is typically taken on by underresourced advocacy groups to begin with. To better understand information work in AI contestation, we interviewed 18 advocates in the United States (US) who have contested the use of AI in high-stakes domains, such as public benefits and housing. We characterize advocates’ strategies for accessing information that is useful for contestation, including a range of creative yet resource-intensive and risky workarounds that they use to overcome opacity. We discuss implications of our findings for the effectiveness of popular transparency policy strategies in the US and offer additional ways to support the social fabric that makes advocates’ information work effective.

As text generation systems' outputs are increasingly anthropomorphic-perceived as humanlike-scholars have also increasingly raised concerns about how such outputs can lead to harmful outcomes, such as users over-relying or developing emotional dependence on these systems.How to intervene on such system outputs to mitigate anthropomorphic behaviors and their attendant harmful outcomes, however, remains understudied.With this work, we aim to provide empirical and theoretical grounding for developing such interventions.To do so, we compile an inventory of interventions grounded both in prior literature and a crowdsourcing study where participants edited system outputs to make them less human-like.Drawing on this inventory, we also develop a conceptual framework to help characterize the landscape of possible interventions, articulate distinctions between different types of interventions, and provide a theoretical basis for evaluating the effectiveness of different interventions.

Recent attention to anthropomorphism -- the attribution of human-like qualities to non-human objects or entities -- of language technologies like LLMs has sparked renewed discussions about potential negative impacts of anthropomorphism. To productively discuss the impacts of this anthropomorphism and in what contexts it is appropriate, we need a shared vocabulary for the vast variety of ways that language can be anthropomorphic. In this work, we draw on existing literature and analyze empirical cases of user interactions with language technologies to develop a taxonomy of textual expressions that can contribute to anthropomorphism. We highlight challenges and tensions involved in understanding linguistic anthropomorphism, such as how all language is fundamentally human and how efforts to characterize and shift perceptions of humanness in machines can also dehumanize certain humans. We discuss ways that our taxonomy supports more precise and effective discussions of and decisions about anthropomorphism of language technologies.

Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems. However, we know little about industry practitioners’ current practices and challenges around user-engaged auditing, nor what opportunities exist for them to better leverage such approaches in practice. To investigate, we conducted a series of interviews and iterative co-design activities with practitioners who employ user-engaged auditing approaches in their work. Our findings reveal several challenges practitioners face in appropriately recruiting and incentivizing user auditors, scaffolding user audits, and deriving actionable insights from user-engaged audit reports. Furthermore, practitioners shared organizational obstacles to user-engaged auditing, surfacing a complex relationship between practitioners and user auditors. Based on these findings, we discuss opportunities for future HCI research to help realize the potential (and mitigate risks) of user-engaged auditing in industry practice.