CSRAI Research Publications

The following is a list of research articles from current and former CSRAI faculty affiliates.

• Workman, D., & Dancy, C. L. (2023, October). Identifying potential inlets of man in the artificial intelligence development process: man and antiblackness in AI development. In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing (pp. 348-353).
• Ansari, A., Zhang, D. C., Zou, Z., Yin, W., & Lee, D. (2026). M2-Verify: A Large-Scale Multidomain Benchmark for Checking Multimodal Claim Consistency. arXiv preprint arXiv:2604.01306.
• Lucas, J., Murtagh-White, M., Uchendu, A., Al-Lawati, A., Yamashita, M., Macko, D., ... & Lee, D. (2026, August). BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (pp. 9498-9509).
• Al-Lawati, A., Tripto, N., Ansari, A., Lucas, J., Wang, S., & Lee, D. (2026). Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue. arXiv preprint arXiv:2605.12856.
• Yadav, D., & Yadav, A. (2026). The Mirage of LLM Guardrails: A Case Study in AI-Assisted Medical Note Manipulation. arXiv preprint arXiv:2607.24859.
• Jeong, S., Kim, J., Sundar, S. S., & Han, J. (2026, April). Multimodal Spatiotemporal Forecasting of Deepfake Propagation on Social Media. In Proceedings of the ACM Web Conference 2026 (pp. 9656-9664).
• Guo, H., Venkit, P. N., Jang, E., Srinath, M., Zhang, W., Mingole, B., ... & Yadav, A. (2025, October). Exposing ai bias by crowdsourcing: Democratizing critique of large language models. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (Vol. 8, No. 2, pp. 1169-1180).
• Lucas, J. S., White, M. M., Al-Lawati, A., Uchendu, U., Uchendu, A., & Lee, D. (2026, July). DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 3171-3214).
• Majumdar, A., Zhang, W., Prawal, K., & Yadav, A. (2026, June). The Hardness of achieving impact in AI for social impact research: a ground-level view of challenges & opportunities. In The 2026 ACM Conference on Fairness, Accountability, and Transparency (pp. 5886-5914).
• Mingole, B., Majumdar, A., Choudhury, F. A., Kraschnewski, J. L., Sundar, S. S., & Yadav, A. (2026, June). Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models for Health Advice using Crowdsourced Clinical Cases. In The 2026 ACM Conference on Fairness, Accountability, and Transparency (pp. 3274-3301).
• Li, Y., Saragih, I. D., Fick, D. M., Sundar, S. S., & Abdullah, S. (2026, July). A Real-Time Voice Agent for Delivering Individual Cognitive Stimulation Therapy to Persons Living with Dementia. In Proceedings of the 8th ACM Conference on Conversational User Interfaces (pp. 1-6).
• Saragih, I. D., Hwang, P., Yadav, D., Qiu, L., Fick, D. M., Sundar, S. S., & Abdullah, S. (2026). Efficacy of digital cognitive stimulation therapy for people with dementia: a systematic review and meta-analysis. BMC Geriatrics.
• Kim, D., Ju, S., Lee, H. M., Kang, M., Kim, M., Park, Y., Song, H., Sundar, S. S., & Han, J. (2026, April). Using LLM for Conversational Assessment of Mental Health: Testing the Role of Message Contingency and User Agency. In Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (pp. 1-7).
• Liao, M., & Sundar, S. S. (2026). Chat but verify: Combating misinformation in conversational Generative AI with verification affordance. Journal of Computer-Mediated Communication, 31(3), zmag012.
• Sun, Y., Liao, M. M., Sundar, S. S., & Walther, J. B. (2025). Does transparency matter when an AI system meets performance expectations? An experiment with an online dating site. Computers in Human Behavior, 108875.
• Liao, M., Lee, S., Dooley, A., Xiong, A., & Sundar, S. S. (2026). When an AI says it is false: User responses to misinformation flagging by automated vs. human fact-checkers. Media Psychology, 1-29.
• Jang, E., & Sundar, S. S. (2026). Less Interaction But More Explanation: A Communication Perspective on Agentic AI Interfaces. arXiv preprint arXiv:2605.01610.
• Park, S., Cha, J., Park, E., & Sundar, S. S. (2026, April). Thriving, Not Just Growing: Structural Sustainability as the Key to Quality in Online Wellness Communities. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (pp. 1-17).
• Chen, C., Liao, M., Phadnis, A. A., Li, Y., High, A., Abdullah, S., & Sundar, S. S. (2026, April). Prompt coaching for inclusiveness: A media literacy approach to increase users’ awareness of algorithmic bias and prompting efficacy. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
• Tschopp, M. V., Chen, C., Wischnewski, M., Gieselmann, M., Schöning, J., Sundar, S. S., & Sassenberg, K. (2026, July). Does Humanizing Chatbots Promote or Hinder Voice Shopping?. In Proceedings of the 8th ACM Conference on Conversational User Interfaces (pp. 1-14).
• Bao, J. A., Jung, Y., & Sundar, S. S. (2026). Are you fooled by interactivity? The effects of interactivity on privacy disclosure. Behaviour & Information Technology, 45(6), 1030-1050.
• Zhang, H., Zha, S., Cai, J., Wohn, D. Y., & Carroll, J. M. (2025, April). Generative AI in Virtual Reality Communities: A Preliminary Analysis of the VRChat Discord Community. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-11).
• Mahdavi, H., Hashemi, A., Daliri, M., Mohammadipour, P., Farhadi, A., Malek, S., ... & Honavar, V. (2025). Brains vs. Bytes: Evaluating LLM Proficiency in Olympiad Mathematics. arXiv preprint arXiv:2504.01995.
• Zhang, Y., Zheng, W., Madasu, A., Shi, P., Kamoi, R., Zhou, H., ... & Zhang, R. (2025). HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?. arXiv preprint arXiv:2504.18406.
• Zhang, H., Xie, J., Wu, C., Cai, J., Kim, C., & Carroll, J. M. (2024, October). The future of learning: Large language models through the lens of students. In Proceedings of the 25th Annual Conference on Information Technology Education (pp. 12-18).
• Li, J., Nag, S., Liu, H., Tang, X., Sarwar, S., Cui, L., ... & Tang, J. (2024). Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data. arXiv preprint arXiv:2411.08028.
• Cai, J., Chowdhury, S., Zhou, H., & Wohn, D. Y. (2023). Hate raids on twitch: Understanding real-time human-bot coordinated attacks in live streaming communities. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW2), 1-28.
• Zhang, H., Xie, J., Wu, C., Cai, J., Kim, C., & Carroll, J. M. (2024). The Future of Learning: Large Language Models through the Lens of Students. arXiv preprint arXiv:2407.12723.
• Acquisti, A., Adjerid, I., Balebako, R., Brandimarte, L., Cranor, L. F., Komanduri, S., Leon, P. G., Sadeh, N., Schaub, F., Sleeper, M., Wang, Y., & Wilson, S. (2017). Nudges for privacy and security: Understanding and assisting users’ choices online. ACM Computing Surveys, 50(3).
• Adenuga, I. J., Hanrahan, B. V., Wu, C., & Mitra, P. (2022). Living Documents: Designing for User Agency over Automated Text Summarization. In CHI Conference on Human Factors in Computing Systems Extended Abstracts (pp. 1-6).
• Atkins, A. A., Brown, M. S., & Dancy, C. L. (2021). Examining the Effects of Race on Human-AI Cooperation. In R. Thomson, M. N. Hussain, C. Dancy, & A. Pyke (Eds.), Social, Cultural, and Behavioral Modeling (Vol. 12720, pp. 279–288). Springer International Publishing.
• Badr, Y., & Sharma, R. (2022). Data Transparency and Fairness Analysis of the NYPD Stop-and-Frisk Program. Journal of Data and Information Quality, 14(2), 1–14.
• Badr, Y., Zhu, X., & Alraja, M. N. (2021). Security and privacy in the Internet of Things: threats and challenges. Service Oriented Computing and Applications, 15(4), 257-271.
• Bartneck, C., Lütge, C., Wagner, A., & Welsh, S. (2021). An Introduction to Ethics in Robotics and AI. Springer Nature.
• Birhane, A., Ruane, E., Laurent, T., S. Brown, M., Flowers, J., Ventresque, A., & L. Dancy, C. (2022). The forgotten margins of AI ethics. In 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 948-958).
• Borenstein, J., Arkin, R. C., & Wagner, A. R. (2022). A Metaethical Reflection: The Ethics of Embedding Ethics into Robots. 2022 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), 1–3.
• Carroll, J. M. (2022). Why should humans trust AI?. Interactions, 29(4), 73-77.
• Chen, J., Doryab, A., Hanrahan, B. V., Yousfi, A., Beck, J., Wang, X., ... & Carroll, J. M. (2019). Context-Aware Coproduction: Implications for Recommendation Algorithms. In International Conference on Information (pp. 565-577). Springer, Cham.
• Chen, S., Surendran, V., Wagner, A. R., Borenstein, J., & Arkin, R. C. (2022). Toward Ethical Robotic Behavior in Human-Robot Interaction Scenarios (arXiv:2206.10727).
• Cho, E., Sundar, S. S., Abdullah, S., & Motalebi, N. (2020). Will deleting history make alexa more trustworthy? effects of privacy and content customization on user experience of smart speakers. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
• Cotter, K. (2021). “Shadowbanning is not a thing”: Black box gaslighting and the power to independently know and credibly critique algorithms. Information, Communication & Society, 1–18.
• Cotter, K. (2022). Practical knowledge of algorithms: The case of BreadTube. New Media & Society. 14614448221081802
• Cotter, K., & Reisdorf, B. (2020). Algorithmic Knowledge Gaps: A New Dimension of (Digital) Inequality. International Journal of Communication, 14, 745–765.
• Cotter, K., DeCook, J. R., & Kanthawala, S. (2022). Fact-Checking the Crisis: COVID-19, Infodemics, and the Platformization of Truth. Social Media+ Society, 8(1), 20563051211069050.
• Cotter, K., DeCook, J. R., Kanthawala, S., & Foyle, K. (2022). In FYP We Trust: The Divine Force of Algorithmic Conspirituality. International Journal of Communication, 16, 1-23.
• Dai, E., Zhao, T., Zhu, H., Xu, J., Guo, Z., Liu, H., Tang, J., & Wang, S. (2022). A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability (arXiv:2204.08570). arXiv.
• Dancy, C. L. (2022). Using a Cognitive Architecture to consider antiblackness in design and development of AI systems. arXiv preprint arXiv:2207.00644.
• Dancy, C. L., & Saucier, P. K. (2021). AI and Blackness: Towards moving beyond bias and representation. IEEE Transactions on Technology and Society, 1–1.
• Dergiades, T., Mavragani, E., & Pan, B. (2018). Google Trends and tourists’ arrivals: Emerging biases and proposed corrections. Tourism Management, 66, 108–120.
• Gomes, C., Dietterich, T., Barrett, C., Conrad, J., Dilkina, B., Ermon, S., Fang, F., Farnsworth, A., Fern, A., Fern, X., Fink, D., Fisher, D., Flecker, A., Freund, D., Fuller, A., Gregoire, J., Hopcroft, J., Kelling, S., Kolter, Z., Yadav, A., ... Zeeman, M. L. (2019). Computational sustainability: Computing for a better world and a sustainable future. Communications of the ACM, 62(9), 56–65.
• Gupta, A., Badr, Y., Negahban, A., & Qiu, R. G. (2021). Energy-efficient heating control for smart buildings with deep reinforcement learning. Journal of Building Engineering, 34, 101739.
• Hsu, Y. C., Huang, T. H. K., Hu, T. Y., Dille, P., Prendi, S., Hoffman, R., ... & Nourbakhsh, I. (2021, May). Project RISE: recognizing industrial smoke emissions. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 14813-14821).
• Huang, T. H., Lasecki, W., Azaria, A., & Bigham, J. (2016). " Is There Anything Else I Can Help You With?" Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 4, pp. 79-88).
• Khademi, A., & Honavar, V. (2020). A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution. arXiv preprint arXiv:2008.00357.
• Khademi, A., Lee, S., Foley, D., & Honavar, V. (2019). Fairness in algorithmic decision making: An excursion through the lens of causality. In The World Wide Web Conference (pp. 2907-2914).
• Kou, Y., & Gui, X. (2020). Mediating community-AI interaction through situated explanation: the case of AI-Led moderation. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1-27.
• Lee, S., Yu, R., Xie, J., Billah, S. M., & Carroll, J. M. (2022). Opportunities for human-AI collaboration in remote sighted assistance. In 27th International Conference on Intelligent User Interfaces (pp. 63-78).
• Li, X., Chen, L., Zhang, J., Larus, J., & Wu, D. (2021). Watermarking-based Defense against Adversarial Attacks on Deep Neural Networks. 2021 International Joint Conference on Neural Networks (IJCNN), 1–8.
• Li, Y., Yang, D., & Hu, X. (2020). A differential privacy-based privacy-preserving data publishing algorithm for transit smart card data. Transportation Research Part C: Emerging Technologies, 115, 102634.
• Li, Z. S., Li, Y.-F., Qiu, R. G., & Zio, E. (2022). Guest Editorial: Special Section on AI Enhanced Reliability Assessment and Predictive Health Management. IEEE Transactions on Industrial Informatics, 18(10), 7196–7197.
• Liao, Q. V., & Sundar, S. S. (2022). Designing for responsible trust in AI: A communication perspective. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22), 1257–1268.
• Masrur, A., Yu, M., Mitra, P., Peuquet, D., & Taylor, A. (2022). Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time. International Journal of Geographical Information Science, 36(4), 692-719.
• Molina, M. D., & Sundar, S. S. (2022). When AI moderates online content: Effects of human collaboration and interactive transparency on user trust. Journal of Computer-Mediated Communication, 27(4), zmac010.
• Passonneau, R. J., McNamara, D., Muresan, S., & Perin, D. (2017). Preface: Special Issue on Multidisciplinary Approaches to AI and Education for Reading and Writing. International Journal of Artificial Intelligence in Education, 27(4), 665–670.
• Peddinti, V., & Qiu, R. (2022). A Machine Learning Approach to Understanding the Progression of Alzheimer’s Disease. In H. Yang, R. Qiu, & W. Chen (Eds.), AI and Analytics for Public Health (pp. 381–392). Springer International Publishing.
• Plaisance, P., & Cruz, J. (2019). The Incorporation of Moral-Development Language for Machine-Learning Companion Robots. Computer Ethics - Philosophical Enquiry (CEPE) Proceedings, 2019(1).
• Pridmore, J., Zimmer, M., Vitak, J., Mols, A., Trottier, D., Kumar, P. C., & Liao, Y. (2019). Intelligent personal assistants and the intercultural negotiations of dataveillance in platformed households. Surveillance & Society. 17(1/2). 125-131
• Rader, E., Cotter, K., & Cho, J. (2018). Explanations as mechanisms for supporting algorithmic transparency. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-13).
• Rahmattalabi, A., Vayanos, P., Fulginiti, A., Rice, E., Wilder, B., Yadav, A., & Tambe, M. (2019). Exploring Algorithmic Fairness in Robust Graph Covering Problems. Advances in Neural Information Processing Systems, 32.
• Rajtmajer, S., & Susser, D. (2020). Automated influence and the challenge of cognitive security. Proceedings of the 7th Symposium on Hot Topics in the Science of Security, 1–9.
• Ravichander, A., Black, A. W., Norton, T., Wilson, S., & Sadeh, N. (2021). Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy? Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 4125–4140.
• Shen, H., & Huang, T. H. (2020). How useful are the machine-generated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 8, pp. 168-172).
• Shin, D., Zhong, B., & Biocca, F. A. (2020). Beyond user experience: What constitutes algorithmic experiences? International Journal of Information Management, 52, 102061.
• Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36.
• Siefert, J. A., Leister, D. D., Koeln, J. P., & Pangborn, H. C. (2022). Discrete Reachability Analysis With Bounded Error Sets. IEEE Control Systems Letters, 6, 1694–1699.
• Singh, P., Gnesdilow, D., Cang, X., Baker, S., Goss, W., Kim, C., Passonneau, R. J., & Puntambekar, S. (2022). Design of Real-time Scaffolding of Middle School Science Writing Using Automated Techniques. International Society for the Learning Sciences Conference.
• Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human-AI interaction (HAII). Journal of Computer-Mediated Communication, 25 (1), 74-88.
• Sundar, S. S., & Lee, E.-J. (2022). Rethinking Communication in the Era of Artificial Intelligence. Human Communication Research, 48(3), 379–385.
• Susser, D. (2019). Invisible Influence: Artificial Intelligence and the Ethics of Adaptive Choice Architectures. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 403–408.
• Susser, D., & Grimaldi, V. (2021). Measuring Automated Influence: Between Empirical Evidence and Ethical Values. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 242–253.
• Swaminathan, S., Fok, R., Chen, F., Huang, T. H., Lin, I., Jadvani, R., ... & Bigham, J. P. (2017). Wearmail: On-the-go access to information in your email with a privacy-preserving human computation workflow. In Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology (pp. 807-815).
• Taylor, R. D. (2020). Quantum Artificial Intelligence: A “precautionary” U.S. approach? Telecommunications Policy, 44(6), 101909.
• Tsai, C. H., & Carroll, J. M. (2022). Logic and Pragmatics in AI Explanation. In International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (pp. 387-396). Springer, Cham.
• Tsai, C. H., & Carroll, J. M. (2022). Logic and Pragmatics in AI Explanation. In International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (pp. 387-396). Springer, Cham.
• Venkit, P. N., & Wilson, S. (2021). Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models (arXiv:2111.13259).
• Wang, J., Yang, H., Shao, R., Abdullah, S., & Sundar, S. S. (2020). Alexa as coach: Leveraging smart speakers to build social agents that reduce public speaking anxiety. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ‘20), Paper 434.
• Wilder, B., Onasch-Vera, L., Diguiseppi, G., Petering, R., Hill, C., Yadav, A., Rice, E., & Tambe, M. (2021). Clinical Trial of an AI-Augmented Intervention for HIV Prevention in Youth Experiencing Homelessness. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14948–14956.
• Xiao, T., & Wang, S. (2022). Towards Unbiased and Robust Causal Ranking for Recommender Systems. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 1158–1167.
• Xiao, T., Chen, Z., & Wang, S. (2022). Towards Bridging Algorithm and Theory for Unbiased Recommendation (arXiv:2206.03851).
• Xie, J., & Wang, Q. (2019). A personalized diet and exercise recommender system for type 1 diabetes self-management: An in silico study. Smart Health, 13, 100069.
• Xie, J., & Wang, Q. (2020). Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison With Classical Time-Series Models. IEEE Transactions on Biomedical Engineering, 67(11), 3101–3124.
• Xiong, A., Wang, T., Li, N., & Jha, S. (2020). Towards Effective Differential Privacy Communication for Users’ Data Sharing Decision and Comprehension. 2020 IEEE Symposium on Security and Privacy (SP), 392–410.
• Xu, J., & Ding, M. (2019). Using the double transparency of autonomous vehicles to increase fairness and social welfare. Customer Needs and Solutions, 6(1), 26-35.
• Yang, H., Qiu, R. G., & Chen, W. (2022). AI and Analytics for Public Health: Proceedings of the 2020 INFORMS International Conference on Service Science. Springer Nature.
• Yen, J., Wang, L., & Gillespie, C. W. (1998). Improving the interpretability of TSK fuzzy models by combining global learning and local learning. IEEE Transactions on Fuzzy Systems, 6(4), 530–537.
• You, Y., & Gui, X. (2020). Self-diagnosis through AI-enabled chatbot-based symptom checkers: user experiences and design considerations. In AMIA Annual Symposium Proceedings (Vol. 2020, p. 1354). American Medical Informatics Association.
• You, Y., Kou, Y., Ding, X., & Gui, X. (2021). The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
• Zhao, T., Dai, E., Shu, K., & Wang, S. (2022). Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features. Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 1433–1442.