As LLMs (large language models) are increasingly used to generate synthetic personas, particularly in data-limited domains such as health, privacy, and HCI, it becomes necessary to understand how these narratives represent identity, especially that of minority communities. In this paper, we audit synthetic personas generated by 3 LLMs (GPT4o, Gemini 1.5 Pro, Deepseek v2.5) through the lens of representational harm, focusing specifically on racial identity. Using a mixed-methods approach combining close reading, lexical analysis, and a parameterized creativity framework, we compare 1,512 LLM-generated persona to human-authored responses. Our findings reveal that LLMs disproportionately foreground racial markers, overproduce culturally coded language, and construct personas that are syntactically elaborate yet narratively reductive. These patterns result in a range of sociotechnical harms, including stereotyping, exoticism, erasure, and benevolent bias, that are often obfuscated by superficially positive narrations. We formalize this phenomenon as algorithmic othering, where minoritized identities are rendered hypervisible but less authentic.

Natural Language Processing tasks that aim to infer an author’s private states, e.g., emotions and opinions, from their written text, typically rely on datasets annotated by third-party annotators. However, the assumption that third-party annotators can accurately capture authors’ private states remains largely unexamined. In this study, we present human subjects experiments on emotion recognition tasks that directly compare third-party annotations with first-party (author-provided) emotion labels. Our findings reveal significant limitations in third-party annotations—whether provided by human annotators or large language models (LLMs)—in faithfully representing authors’ private states. However, LLMs outperform human annotators nearly across the board. We further explore methods to improve third-party annotation quality. We find that demographic similarity between first-party authors and third-party human annotators enhances annotation performance. While incorporating first-party demographic information into prompts leads to a marginal but statistically significant improvement in LLMs’ performance. We introduce a framework for evaluating the limitations of third-party annotations and call for refined annotation practices to accurately represent and model authors’ private states.

High-fidelity personalized human musculoskeletal models are crucial for simulating realistic behavior of physically coupled human-robot interactive systems and verifying their safety-critical applications in simulations before actual deployment, such as human-robot co-transportation and rehabilitation through robotic exoskeletons. Identifying subject-specific Hill-type muscle model parameters and bone dynamic parameters is essential for a personalized musculoskeletal model, but very challenging due to the difficulty of measuring the internal bio-mechanical variables in vivo directly, especially the joint torques. In this paper, we propose using Differentiable MusculoSkeletal Model (Diff-MSM) to simultaneously identify its muscle and bone parameters with an end-to-end automatic differentiation technique differentiating from the measurable muscle activation, through the joint torque, to the resulting observable motion without the need to measure the internal joint torques. Through extensive comparative simulations, the results manifested that our proposed method significantly outperformed the state-of-the-art baseline methods, especially in terms of accurate estimation of the muscle parameters (i.e., initial guess sampled from a normal distribution with the mean being the ground truth and the standard deviation being 10% of the ground truth could end up with an average of the percentage errors of the estimated values as low as 0.05%). In addition to human musculoskeletal modeling and simulation, the new parameter identification technique with the Diff-MSM has great potential to enable new applications in muscle health monitoring, rehabilitation, and sports science.

Synthetic images, audio, and video can now be generated and edited by Artificial Intelligence (AI). In particular, the malicious use of synthetic data has raised concerns about potential harms to cybersecurity, personal privacy, and public trust. Although AI-based detection tools exist to help identify synthetic content, their limitations often lead to user mistrust and confusion between real and fake content. This study examines the role of AI performance in influencing human trust and decision making in synthetic data identification. Through an online human subject experiment involving 400 participants, we examined how varying AI performance impacts human trust and dependence on AI in deepfake detection. Our findings indicate how participants calibrate their dependence on AI based on their perceived risk and the prediction results provided by AI. These insights contribute to the development of transparent and explainable AI systems that better support everyday users in mitigating the harms of synthetic media.

Metaverse platforms have been on the rise in recent years, offering three-dimensional (3D), immersive virtual worlds while encouraging user-generated content (UGC) in various forms. Roblox, a popular metaverse platform, enables its users to create a holistic virtual world (i.e., develop a 3D game) for other users to interact with. However, complex UGC is also challenging to moderate. Roblox has been notorious for its users’ harmful designs, such as Nazi or terrorist role-playing mechanisms. In this study, we explore how harmful design takes place on Roblox. Through a grounded theory analysis of the ‘r/Robloxgamedev’ subreddit, we conceptualize an ecological view of harmful design, foregrounding three interconnected circumstances, namely sociotechnical risks, socioeconomic precarities, and normative (in)sensitivities, which work together to condition and give rise to harmful designs and bring about unique governance challenges to metaverse platforms. We conclude by laying out implications for design moderation.

Moderation systems of online games often follow a retributive model inspired by real-world criminal justice, expecting that punishments can help users to reform behavior. However, decades of criminological research show that punishments alone do not work and call for a rehabilitative approach, such as community-based rehabilitation (CBR), to help offenders transform their minds and behavioral patterns. Motivated by this call, we explore how moderated users view punishments in a community context and how other community members respond in League of Legends (LoL), one of the largest online games. Specifically, we focus on how peer support is sought and provided on the /r/LeagueOfLegends subreddit, the largest LoL-related online community. Our content analysis of player discussions characterized the communication between moderated users and peers as informative, constructive, and reflexive. We highlight the importance of involving community in moderation systems and discuss implications for designing CBR mechanisms that could enhance moderation systems.

The COVID-19 global pandemic has ignited lightning-fast adoption of digital tools in our communities, organizations, and systems of governance. It also inspired an unprecedented level of providing access to digital devices to communities and individuals lacking prior access. The situation and circumstances provide a unique opportunity to understand digital divides through a new lens. In this work, we contribute a contemporaneous understanding of digital divides beyond access by qualitatively analyzing over 300 calls made to a volunteer-based community IT help desk. We highlight the intertwined network of challenges leading to ecosystem digital divides and contribute new insights into how the complex socio-technical systems of practice, and the tools to support them, must adapt to bridge digital divides more effectively.

Social media has become an important source where people gather and communicate news. Prior studies in conventional mass media suggest that gatekeepers play an important role in the production of news messages. Despite the initial claim of social media being a place of democratized participation, we now know, social media is not free of gatekeepers either. However, it is unclear who social media gatekeepers are, how to identify them, and most importantly how do they impact news content production and dissemination. Due to fundamental differences between the structure and workings of social media vs. traditional media, what we know from mass media cannot directly apply in the context of social media. To answer these questions, we propose an actionable definition of social media gatekeepers backed by literature on news reporting in social media and traditional mass media. We then present a case study of identifying gatekeepers on Twitter at scale, using a set of 70k Twitter users interested in the news topic of "immigration''. The results of our mixed research approach highlight that, unlike the general Twitter users, the Twitter gatekeepers are often self-determining citizen journalists who manage their media presentation strategically. Moreover, Twitter gatekeepers tend to exhibit behavior mostly in accordance with the journalism norms and they contribute to and guard the truthfulness and neutrality of content.