Renee Shelby
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Generative AI’s humanlike qualities are driving its rapid adoption in professional domains. However, this anthropomorphic appeal raises concerns from HCI and responsible AI scholars about potential hazards and harms, such as overtrust in system outputs. To investigate how technology workers navigate these humanlike qualities and anticipate emergent harms, we conducted focus groups with 30 professionals across six job functions (ML engineering, product policy, UX research and design, product management, technology writing, and communications). Our findings reveal an unsettled knowledge environment surrounding humanlike generative AI, where workers’ varying perspectives illuminate a range of potential risks for individuals, knowledge work fields, and society. We argue that workers require comprehensive support, including clearer conceptions of “humanlikeness” to effectively mitigate these risks. To aid in mitigation strategies, we provide a conceptual map articulating the identified hazards and their connection to conflated notions of “humanlikeness.”
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Tech Worker Challenges Managing Humanlike GenAI
Eric Corbett
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, ACM (2026), pp. 1-18
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Organizations are adopting or exploring anthropomorphic genAI — meaning XYZ. Anthropomorphic AI is often held up for its potential to improve the productivity and efficiency of workers and technologies; however, there are not yet accepted industry-wide standards for the responsible development of anthropomorphic technologies. Given their roles as central figures responsible for implementing anthropomorphic genAI into technologies that are served to the broader public, we must understand workers’ reasoning about anthropomorphic genAI to understand its impacts. However, there is a dearth of empirical knowledge about technology workers’ perspectives on anthropomorphic technologies, including their perspectives on potential risks and benefits. To address this gap, we conducted focus groups with 31 technology workers across 6 job roles (UX, software engineers, product managers, designers, marketing, and trust and safety) regarding how they define anthropomorphic genAI, their perceptions of anthropomorphic genAI, and their experiences working with anthropomorphic genAI. We find that workers’ have expansive definitions of what constitutes “humanlike” AI, which at times sit in tension with each other. They draw on their personal and professional standpoints to sensemake about real and possible anthropomorphic genAI hazards to people, knowledge work fields, and society at-large. Importantly, we find that these social hazards map to different facets of anthropomorphic genAI, suggesting that effective mitigation of personal and social risks requires developer attention to specific dimensions of anthropomorphism. We mapped the relationships between dimensions of anthropomorphism and hazards, to support technology workers. We argue that effective mitigation of the risks of anthropomorphism requires attention to the multiple facets of anthropomorphism.
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`It’s still abuse’: Community attitudes and perceptions on AI-generated image-based sexual abuse
Nicola Henry
Gemma Beard
Lisa Given
Information, Communication, & Society (2026)
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There are growing concerns about AI-generated image-based sexual abuse (AI-IBSA), also known as nonconsensual sexualized ′deepfakes.′ Empirical research on AI-IBSA, however, remains very limited. This study surveyed 7231 respondents across Australia, the United Kingdom, and the United States to investigate community attitudes and perceptions on AI-IBSA. Through a vignette study, we explored the relationship between public familiarity with AI-IBSA, normative concerns about consent, and context-dependent judgments that vary based on the target's identity relational status, and how the content was used. Our findings reveal strong condemnation of AI-IBSA, yet respondents demonstrated low familiarity with the technology and their views varied depending on particular contexts. AI-IBSA targeting intimate partners was viewed as more unacceptable than targeting celebrities, and content created solely for personal use was seen as less unacceptable than content intended for distribution. The study highlights the need for approaches that go beyond technical fixes and punitive measures, advocating for a multifaceted response that integrates ethical data governance, digital sexual literacy, and restorative justice approaches.
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Who is At-Risk? Surveying the Prevalence of Risk Factors and Tech-Facilitated Attacks in the United States
Sharon Heung
Claire Weizenegger
Mo Houtti
Tara Matthews
Ashley Walker
2026
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A growing body of qualitative research has identified contextual risk factors that elevate people’s chances of experiencing digital-safety attacks. However, the lack of quantitative data on the population level distribution of these risk factors prevents policymakers and tech companies from developing targeted, evidence-based interventions to improve digital safety. To address this gap, we surveyed 5,001 adults in the United States to analyze: (1) the frequency of and relationship between digital-safety attacks (e.g., scams, harassment, account hacking), and (2) how these attacks align with 10 contextual risk factors. Nearly half of our respondents identify as resource constrained, which significantly correlates with higher likelihood of experiencing four common attacks. We also present qualitative insights to expand our understanding of the factors beyond the existing literature (e.g., “prominence” included high-visibility roles in local communities). This study provides the first large-scale quantitative analysis correlating digital-safety attacks with contextual risk factors and demographics.
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"It didn’t feel right but I needed a job so desperately": Understanding People's Emotions & Help Needs During Financial Scams
G. Jake Chanenson
Tara Matthews
Jessica McClearn
Sarah Meiklejohn
Mia Hassoun
2026
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Online financial scams represent a long-standing and serious threat for which people seek help. We present a study to understand people’s in situ motivations for engaging with scams and the help needs they express before, during, and after encountering a scam. We identify the main emotions scammers exploited (e.g., fear, hope) and characterize how they did so. We examine factors—such as financial insecurity and legal precarity—which elevate people’s risk of engaging with specific scams and experiencing harm. We indicate when people sought help and describe their help-seeking needs and emotions at different stages of the scam. We discuss how these needs could be met through the design of contextually-specific prevention, diagnostic, mitigation, and recovery interventions.
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Responsible AI measures dataset for ethics evaluation of AI systems
Shalaleh Rismani
Leah Davis
Bonam Mingole
AJung Moon
Scientific Data (2025)
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Meaningful governance of any system requires the system to be assessed and monitored effectively. In the domain of Artificial Intelligence (AI), global efforts have established a set of ethical principles, including fairness, transparency, and privacy upon which AI governance expectations are being built. The computing research community has proposed numerous means of measuring an AI system’s normative qualities along these principles. Current reporting of these measures is principle-specific, limited in scope, or otherwise dispersed across publication platforms, hindering the domain’s ability to critique its practices. To address this, we introduce the Responsible AI Measures Dataset, consolidating 12,067 data points across 791 evaluation measures covering 11 ethical principles. It is extracted from a corpus of computing literature (n = 257) published between 2011 and 2023. The dataset includes detailed descriptions of each measure, AI system characteristics, and publication metadata. An accompanying, interactive visualization tool supports usability and interpretation of the dataset. The Responsible AI Measures Dataset enables practitioners to explore existing assessment approaches and critically analyze how the computing domain measures normative concepts.
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Audio description (AD) narrates important visual details which are played during dialogue gaps in video soundtracks, making them accessible to blind and low vision (BLV) audiences. AD professionals (producers, writers, narrators, mixers, and quality control specialists) possess expert knowledge of AD development and the constraints that affect their work. However, their perspectives remain largely absent in AD research. We present interviews with 17 AD professionals (8 BLV), detailing their workflows to produce AD for recorded media and live theater. We additionally explore their perspectives on recent changes impacting their work, revealing tensions between advocacy for culturally competent AD and the rise of automations—some beneficial, others with concerning implications for AD quality. Highlighting these tensions, we offer research directions to support AD professionals, and we pose guiding questions for AD and AI innovators on preserving the high-quality human touch professionals consider fundamental to the accessibility provision.
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Creative ML Assemblages: The Interactive Politics of People, Processes, and Products
Ramya Malur Srinivasan
Katharina Burgdorf
Jennifer Lena
ACM Conference on Computer Supported Cooperative Work and Social Computing (2024) (to appear)
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Creative ML tools are collaborative systems that afford artistic creativity through their myriad interactive relationships. We propose using ``assemblage thinking" to support analyses of creative ML by approaching it as a system in which the elements of people, organizations, culture, practices, and technology constantly influence each other. We model these interactions as ``coordinating elements" that give rise to the social and political characteristics of a particular creative ML context, and call attention to three dynamic elements of creative ML whose interactions provide unique context for the social impact a particular system as: people, creative processes, and products. As creative assemblages are highly contextual, we present these as analytical concepts that computing researchers can adapt to better understand the functioning of a particular system or phenomena and identify intervention points to foster desired change. This paper contributes to theorizing interactions with AI in the context of art, and how these interactions shape the production of algorithmic art.
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Understanding Help-Seeking and Help-Giving on Social Media for Image-Based Sexual Abuse
Tara Matthews
Miranda Wei
Sarah Meiklejohn
(2024)
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Image-based sexual abuse (IBSA), like other forms of technology-facilitated abuse, is a growing threat to peoples' digital safety. Attacks include unwanted solicitations for sexually explicit images, extorting people under threat of leaking their images, or purposefully leaking images to enact revenge or exert control. In this paper, we explore how people experiencing IBSA seek and receive help from social media. Specifically, we identify over 100,000 Reddit posts that engage relationship and advice communities for help related to IBSA. We draw on a stratified sample of these posts to qualitatively examine how various types of IBSA unfold, the support needs of victim-survivors experiencing IBSA, and how communities help victim-survivors navigate their abuse through technical, emotional, and relationship advice. In the process, we highlight how gender, relationship dynamics, and the threat landscape influence the design space of sociotechnical solutions. We also highlight gaps that remain to connecting victim-survivors with important care---regardless of whom they turn to for help.
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Generative AI in Creative Practice: ML-Artist Folk Theories of T2I Use, Harm, and Harm-Reduction
Shalaleh Rismani
Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), Association for Computing Machinery (2024), pp. 1-17 (to appear)
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Understanding how communities experience algorithms is necessary to mitigate potential harmful impacts. This paper presents folk theories of text-to-image (T2I) models to enrich understanding of how artist communities experience creative machine learning (ML) systems. This research draws on data collected from a workshop with 15 artists from 10 countries who incorporate T2I models in their creative practice. Through reflexive thematic analysis of workshop data, we highlight theorization of T2I use, harm, and harm-reduction. Folk theories of use envision T2I models as an artistic medium, a mundane tool, and locate true creativity as rising above model affordances. Theories of harm articulate T2I models as harmed by engineering efforts to eliminate glitches and product policy efforts to limit functionality. Theories of harm-reduction orient towards protecting T2I models for creative practice through transparency and distributed governance. We examine how these theories relate, and conclude by discussing how folk theorization informs responsible AI efforts.
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