Can we Debias Social Stereotypes in AI-Generated Images? Examining Text-to-Image Outputs and User Perceptions

Recent advances in generative AI have enabled visual content creation through text-to-image (T2I) generation. However, despite their creative potential, T2I models often replicate and amplify societal stereotypes -- particularly those related to gender, race, and culture -- raising important ethical concerns. This paper proposes a theory-driven bias detection rubric and a Social Stereotype Index (SSI) to systematically evaluate social biases in T2I outputs. We audited three major T2I model outputs -- DALL-E-3, Midjourney-6.1, and Stability AI Core -- using 100 queries across three categories -- geocultural, occupational, and adjectival. Our analysis reveals that initial outputs are prone to include stereotypical visual cues, including gendered professions, cultural markers, and western beauty norms. To address this, we adopted our rubric to conduct targeted prompt refinement using LLMs, which significantly reduced bias -- SSI dropped by 61% for geocultural, 69% for occupational, and 51% for adjectival queries. We complemented our quantitative analysis through a user study examining perceptions, awareness, and preferences around AI-generated biased imagery. Our findings reveal a key tension -- although prompt refinement can mitigate stereotypes, it can limit contextual alignment. Interestingly, users often perceived stereotypical images to be more aligned with their expectations. We discuss the need to balance ethical debiasing with contextual relevance and call for T2I systems that support global diversity and inclusivity while not compromising the reflection of real-world social complexity.

MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance

Large language models (LLMs) have shown great potential in flagging harmful content in online communities. Yet, existing approaches for moderation require a separate model for every community and are opaque in their decision-making, limiting real-world adoption. We introduce Mixture of Moderation Experts (MoMoE), a modular, cross-community framework that adds post-hoc explanations to scalable content moderation. MoMoE orchestrates four operators -- Allocate, Predict, Aggregate, Explain -- and is instantiated as seven community-specialized experts (MoMoE-Community) and five norm-violation experts (MoMoE-NormVio). On 30 unseen subreddits, the best variants obtain Micro-F1 scores of 0.72 and 0.67, respectively, matching or surpassing strong fine-tuned baselines while consistently producing concise and reliable explanations. Although community-specialized experts deliver the highest peak accuracy, norm-violation experts provide steadier performance across domains. These findings show that MoMoE yields scalable, transparent moderation without needing per-community fine-tuning. More broadly, they suggest that lightweight, explainable expert ensembles can guide future NLP and HCI research on trustworthy human-AI governance of online communities.

Communication Styles and Reader Preferences of LLM and Human Experts in Explaining Health Information

With the wide adoption of large language models (LLMs) in information assistance, it is essential to examine their alignment with human communication styles and values. We situate this study within the context of fact-checking health information, given the critical challenge of rectifying conceptions and building trust. Recent studies have explored the potential of LLM for health communication, but style differences between LLMs and human experts and associated reader perceptions remain under-explored. In this light, our study evaluates the communication styles of LLMs, focusing on how their explanations differ from those of humans in three core components of health communication: information, sender, and receiver. We compiled a dataset of 1498 health misinformation explanations from authoritative fact-checking organizations and generated LLM responses to inaccurate health information. Drawing from health communication theory, we evaluate communication styles across three key dimensions of information linguistic features, sender persuasive strategies, and receiver value alignments. We further assessed human perceptions through a blinded evaluation with 99 participants. Our findings reveal that LLM-generated articles showed significantly lower scores in persuasive strategies, certainty expressions, and alignment with social values and moral foundations. However, human evaluation demonstrated a strong preference for LLM content, with over 60% responses favoring LLM articles for clarity, completeness, and persuasiveness. Our results suggest that LLMs' structured approach to presenting information may be more effective at engaging readers despite scoring lower on traditional measures of quality in fact-checking and health communication.

AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression

Recent advancements in LLMs enable chatbots to interact with individuals on a range of queries, including sensitive mental health contexts. Despite uncertainties about their effectiveness and reliability, the development of LLMs in these areas is growing, potentially leading to harms. To better identify and mitigate these harms, it is critical to understand how the values of people with lived experiences relate to the harms. In this study, we developed a technology probe, a GPT-4o based chatbot called Zenny, enabling participants to engage with depression self-management scenarios informed by previous research. We used Zenny to interview 17 individuals with lived experiences of depression. Our thematic analysis revealed key values: informational support, emotional support, personalization, privacy, and crisis management. This work explores the relationship between lived experience values, potential harms, and design recommendations for mental health AI chatbots, aiming to enhance self-management support while minimizing risks.

Interpersonal Theory of Suicide as a Lens to Examine Suicidal Ideation in Online Spaces

Suicide is a critical global public health issue, with millions experiencing suicidal ideation (SI) each year. Online spaces enable individuals to express SI and seek peer support. While prior research has revealed the potential of detecting SI using machine learning and natural language analysis, a key limitation is the lack of a theoretical framework to understand the underlying factors affecting high-risk suicidal intent. To bridge this gap, we adopted the Interpersonal Theory of Suicide (IPTS) as an analytic lens to analyze 59,607 posts from Reddit's r/SuicideWatch, categorizing them into SI dimensions (Loneliness, Lack of Reciprocal Love, Self Hate, and Liability) and risk factors (Thwarted Belongingness, Perceived Burdensomeness, and Acquired Capability of Suicide). We found that high-risk SI posts express planning and attempts, methods and tools, and weaknesses and pain. In addition, we also examined the language of supportive responses through psycholinguistic and content analyses to find that individuals respond differently to different stages of Suicidal Ideation (SI) posts. Finally, we explored the role of AI chatbots in providing effective supportive responses to suicidal ideation posts. We found that although AI improved structural coherence, expert evaluations highlight persistent shortcomings in providing dynamic, personalized, and deeply empathetic support. These findings underscore the need for careful reflection and deeper understanding in both the development and consideration of AI-driven interventions for effective mental health support.

Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries

The ubiquity and widespread use of digital and online technologies have transformed mental health support, with online mental health communities (OMHCs) providing safe spaces for peer support. More recently, generative AI and large language models (LLMs) have introduced new possibilities for scalable, around-the-clock mental health assistance that could potentially augment and supplement the capabilities of OMHCs. Although genAI shows promise in delivering immediate and personalized responses, their effectiveness in replicating the nuanced, experience-based support of human peers remains an open question. In this study, we harnessed 24,114 posts and 138,758 online community (OC) responses from 55 OMHCs on Reddit. We prompted several state-of-the-art LLMs (GPT-4-Turbo, Llama-3, and Mistral-7B) with these posts, and compared their (AI) responses to human-written (OC) responses based on a variety of linguistic measures across psycholinguistics and lexico-semantics. Our findings revealed that AI responses are more verbose, readable, and analytically structured, but lack linguistic diversity and personal narratives inherent in human-human interactions. Through a qualitative examination, we found validation as well as complementary insights into the nature of AI responses, such as its neutrality of stance and the absence of seeking back-and-forth clarifications. We discuss the ethical and practical implications of integrating generative AI into OMHCs, advocating for frameworks that balance AI's scalability and timeliness with the irreplaceable authenticity, social interactiveness, and expertise of human connections that form the ethos of online support communities.

SLM-Mod: Small Language Models Surpass LLMs at Content Moderation

Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for community-specific content moderation tasks. We fine-tune and evaluate SLMs (less than 15B parameters) by comparing their performance against much larger open- and closed-sourced models. Using 150K comments from 15 popular Reddit communities, we find that SLMs outperform LLMs at content moderation -- 11.5% higher accuracy and 25.7% higher recall on average across all communities. We further show the promise of cross-community content moderation, which has implications for new communities and the development of cross-platform moderation techniques. Finally, we outline directions for future work on language model based content moderation. Code and links to HuggingFace models can be found at https://github.com/AGoyal0512/SLM-Mod.

Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care

Telehealth is a valuable tool for primary health care (PHC), where depression is a common condition. PHC is the first point of contact for most people with depression, but about 25% of diagnoses made by PHC physicians are inaccurate. Many other barriers also hinder depression detection and treatment in PHC. Artificial intelligence (AI) may help reduce depression misdiagnosis in PHC and improve overall diagnosis and treatment outcomes. Telehealth consultations often have video issues, such as poor connectivity or dropped calls. Audio-only telehealth is often more practical for lower-income patients who may lack stable internet connections. Thus, our study focused on using audio data to predict depression risk. The objectives were to: 1) Collect audio data from 24 people (12 with depression and 12 without mental health or major health condition diagnoses); 2) Build a machine learning model to predict depression risk. TPOT, an autoML tool, was used to select the best machine learning algorithm, which was the K-nearest neighbors classifier. The selected model had high performance in classifying depression risk (Precision: 0.98, Recall: 0.93, F1-Score: 0.96). These findings may lead to a range of tools to help screen for and treat depression. By developing tools to detect depression risk, patients can be routed to AI-driven chatbots for initial screenings. Partnerships with a range of stakeholders are crucial to implementing these solutions. Moreover, ethical considerations, especially around data privacy and potential biases in AI models, need to be at the forefront of any AI-driven intervention in mental health care.

Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?

Sensing technologies deployed in the workplace can collect detailed data about individual activities and group interactions that are otherwise difficult to capture. A hopeful application of these technologies is that they can help businesses and workers optimize productivity and wellbeing. However, given the inherent and structural power dynamics in the workplace, the prevalent approach of accepting tacit compliance to monitor work activities rather than seeking workers' meaningful consent raises privacy and ethical concerns. This paper unpacks a range of challenges that workers face when consenting to workplace wellbeing technologies. Using a hypothetical case to prompt reflection among six multi-stakeholder focus groups involving 15 participants, we explored participants' expectations and capacity to consent to workplace sensing technologies. We sketched possible interventions that could better support more meaningful consent to workplace wellbeing technologies by drawing on critical computing and feminist scholarship -- which reframes consent from a purely individual choice to a structural condition experienced at the individual level that needs to be freely given, reversible, informed, enthusiastic, and specific (FRIES). The focus groups revealed that workers are vulnerable to meaningless consent -- dynamics that undo the value of data gathered in the name of "wellbeing," as well as an erosion of autonomy in the workplace. To meaningfully consent, participants wanted changes to how the technology works and is being used, as well as to the policies and practices surrounding the technology. Our mapping of what prevents workers from meaningfully consenting to workplace wellbeing technologies (challenges) and what they require to do so (interventions) underscores that the lack of meaningful consent is a structural problem requiring socio-technical solutions.

Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect

The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which we study in this paper. In particular, we adopt a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. We obtain a Twitter dataset with $\sim$2M posts by $\sim$10K individuals. We consider engaging with coping stories as the Treatment intervention, and adopt a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. We measure the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. Our findings reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. Our work discusses the practical and platform design implications in supporting mental wellbeing.