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.

HCI research to date has only scratched the surface of the unique approaches racially minoritized communities take to building, designing, and using technology systems. While there has been an increase in understanding how people across racial groups create community across different platforms, there is still a lack of studies that explicitly center on how Black technologists design with and for their own communities. In this paper, we present findings from a series of semi-structured interviews with Black technologists who have used, created, or curated resources to support lived Black experiences. From their experiences, we find a multifaceted approach to design as a means of survival, to stay connected, for cultural significance, and to bask in celebratory joy. Further, we provide considerations that emphasize the need for centering lived Black experiences in design and share approaches that can empower the broader research community to conduct further inquiries into design focused on those in the margins.

Conversational agents such as chatbots have emerged as a useful resource to access real-time health information online. Perceptions of trust and credibility among chatbots have been attributed to the anthropomorphism and humanness of the chatbot design, with gender and race influencing their reception. Few existing studies have looked specifically at the diversity of chatbot avatar design related to both race, age, and gender, which may have particular significance for racially minoritized users like Black older adults. In this paper, we explored perceptions of chatbots with varying identities for health information seeking in a diary and interview study with 30 Black older adults. Our findings suggest that while racial and age likeness influence feelings of trust and comfort with chatbots, constructs such as professionalism and likeability and overall familiarity also influence reception. Based on these findings, we provide implications for designing text-based chatbots that consider Black older adults.

Various tools and practices have been developed to support practitioners in identifying, assessing, and mitigating fairness-related harms caused by AI systems. However, prior research has highlighted gaps between the intended design of these tools and practices and their use within particular contexts, including gaps caused by the role that organizational factors play in shaping fairness work. In this paper, we investigate these gaps for one such practice: disaggregated evaluations of AI systems, intended to uncover performance disparities between demographic groups. By conducting semi-structured interviews and structured workshops with thirty-three AI practitioners from ten teams at three technology companies, we identify practitioners' processes, challenges, and needs for support when designing disaggregated evaluations. We find that practitioners face challenges when choosing performance metrics, identifying the most relevant direct stakeholders and demographic groups on which to focus, and collecting datasets with which to conduct disaggregated evaluations. More generally, we identify impacts on fairness work stemming from a lack of engagement with direct stakeholders or domain experts, business imperatives that prioritize customers over marginalized groups, and the drive to deploy AI systems at scale.

In an ideal world, deployed machine learning models will enhance our society. We hope that those models will provide unbiased and ethical decisions that will benefit everyone. However, this is not always the case; issues arise during the data preparation process throughout the steps leading to the models’ deployment. The continued use of biased datasets and biased processes will adversely damage communities and increase the cost to fix the problem later. In this work, we walk through the decision making process that a researcher should consider before, during, and after a system deployment to understand the broader impacts of their research in the community. Throughout this paper, we discuss fairness, privacy, and ownership issues in the machine learning pipeline, assert the need for a responsible human-over-the-loop methodology to bring accountability into machine learning pipeline, and finally, reflect on the need to explore research agendas that have harmful societal impacts. We examine visual privacy research and draw lessons that can apply broadly to artificial intelligence. Our goal is to provide a systematic analysis of the machine learning pipeline for visual privacy and bias issues. With this pipeline, we hope to raise stakeholder (e.g., researchers, modelers, corporations) awareness as these issues propagate in the various machine learning phases.