Penn State CSRAI Research
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 . Nahar, M., Lee, E. J., Park, J. W., & Lee, D . (2025). Catch me if you search: When contextual web search results affect the detection of hallucinations . Computers in Human Behavior , 108763. Chakravorti, T., Gautam, S., & Rajtmajer, S. M . (2024). Embracing Transparency: A Study of Open Science Practices Among Early Career HCI Researchers . arXiv preprint arXiv:2410.04286 . Ramakrishnan, A. A., Ramakrishnan, A. A., & Lee, D . (2025). IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detectio n. arXiv preprint arXiv:2505.16258 . Guo, H., Xiong, X., Zhang, W., & Yadav, A. (2024). ReLax: Efficient and Scalable Recourse Explanation Benchmarking using JAX. Journal of Open Source Software, 9 (103), 6567. Xie, L., Liu, H., Zeng, J., Tang, X., Han, Y., Luo, C., Huang, J., Li, Z., Wang, S. , & He, Q. (2024). A Survey of Calibration Process for Black-Box LLMs . arXiv preprint arXiv:2412.12767. Li, P., Xu, J., Wu, D. J., & Ding, M. (2024). When Interpretations and Predictions Help Each Other: A Novel Dual Learning Framework and Its Application in Visual Analytics. Available at SSRN 4985197. Seo, H., Lee, S., Lee, D. , & Xiong, A. (2024, May). Reliability Matters: Exploring the Effect of AI Explanations on Misinformation Detection with a Warning. In Proceedings of the International AAAI Conference on Web and Social Media, 18, 1395-1407. Chen, C., Liao, M., & Sundar, S. S. (2024). When to explain? Exploring the effects of explanation timing on user perceptions and trust in AI systems. Proceedings of the Second International Symposium on Trustworthy Autonomous Systems (TAS’24) , Article No. 10. Tsai, C. H., Nandy, G., House, D., & Carroll, J. (2024). Ensuring transparency in using ChatGPT for public sentiment analysis. In Proceedings of the 25th Annual International Conference on Digital Government Research (pp. 627-636). 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. Zhang, X., Lee, D., & Wang, S. (2024, July 27). Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector . Seo, H., Lee, S., Lee, D., & Xiong, A. (2024). Reliability Matters: Exploring the Effect of AI Explanations on Misinformation Detection with a Warning. Proceedings of the International AAAI Conference on Web and Social Media , 18 , 1395–1407. Liao, Q. V., & Sundar, S. S. (2022). Designing for Responsible Trust in AI Systems: A Communication Perspective. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency , 1257–1268. 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.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). 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 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. 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). 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. 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. 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.
Articles
Miró-Nicolau, M., Moyà-Alcover, G., Jaume-i-Capó, A., González-Hidalgo, M., Ghazel, A., Sempere Campello, M. G., & Palmer Sancho, J. A. (2026). Toward a novel measure of user trust in XAI systems . Frontiers in Computer Science , 8 , 1746591. Spillner, L., Ringe, R., Porzel, R., & Malaka, R. (2025, November). Not all trust is the same: Effects of decision workflow and explanations in human-ai decision making. In International Symposium on Chatbots and Human-Centered AI (pp. 281-298). Cham: Springer Nature Switzerland. Lukassen, F., Herrmann, J., Weisser, C., Silbersdorff, A., Saefken, B., & Kneib, T. (2026). Quality Without Usefulness: LLM-Generated XAI Narratives as Trust Heuristics Rather Than Decision Aids . arXiv preprint arXiv:2605.26770 . Moon, J., Huang, C. M., & Xiao, Z. (2026, April). Understanding the Affordances of Control in AI Reasoning for Human-AI Decision-Making . In Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (pp. 1-9). Aggarwal, S., Mishra, R. V., & Awekar, A. (2026). Evaluating chain-of-thought reasoning through reusability and verifiability . arXiv preprint arXiv:2602.17544 . Zhu, X., Truong, K. L., Fogliato, R., Swamy, G., Zhang, W., Yang, M., ... & Wu, S. (2026). Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality . arXiv preprint arXiv:2604.04418 . Alon, B., Zimerman, I., & Wolf, L. (2026, July). Faithful Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance . In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 6622-6645). Nussbaum-Hoffer, N., Calderon, N., Ein-Dor, L., & Reichart, R. (2026). LLM Explainability with Counterfactual Chains and Causal Graphs . arXiv preprint arXiv:2606.05972 . Fan, S., Zhang, L., & Yuan, X. (2026). When AI persuades: Adversarial explanation attacks on human trust in AI-assisted decision making . arXiv preprint arXiv:2602.04003 . Akpinar, N. J., Avula, S., Lee, C. J., Dang, B., Razat, K., & Murdock, V. (2026). Llm or human? Perceptions of trust and information quality in research summaries . arXiv preprint arXiv:2601.15556 . Shafik, W., Hidayatullah, A. F., Kalinaki, K., Gul, H., Zakari, R. Y., & Tufail, A. (2026). A systematic literature review on transparency and interpretability of AI models in healthcare: taxonomies, tools, techniques, datasets, open research challenges, and future trends . Health and Technology , 1-22. Guo, H., & Polak, P. (2026). Building trustworthy Artificial Intelligence through transparency explainability uncertainty and trust calibration . Discover Artificial Intelligence . Hoang, A., Phan, H., & Nguyen, V. D. (2026). Explainable AI in Finance: Enhancing Transparency and Interpretability of AI Models in Financial Decision-Making . Data Science in Finance and Accounting , 193-211. Salampasis, D. (2026). Making AI Make Sense: Explainable AI for Trustworthy Intelligence . Research-Technology Management , 69 (2), 57-62. Dang, Q., & Li, G. (2026). Unveiling trust in AI: The interplay of antecedents, consequences, and cultural dynamics . AI & SOCIETY , 41 (1), 669-692. Li, J., Han, M., & Yuan, Q. (2026). When transparency backfires: how AI identity disclosure fuels consumer information distortion in service recovery . Journal of Hospitality Marketing & Management , 1-27. Buttaboni, C., & Floridi, L. (2026). A regulatory taxonomy of AI opacity in the EU: rethinking transparency, traceability, interpretability, and explainability . AI and Ethics , 6 (1), 100. Cetinkaya, N. E., & Krämer, N. (2026). Between transparency and trust: Identifying key factors in AI system perception . Behaviour & Information Technology , 45 (5), 840-854. Yhee, Y., & Koo, C. (2026). Seeing AI as human or machine? Effects of transparency, valence, and readability on review summary helpfulness . Tourism Management , 113 , 105305. Ou, M., Zheng, H., Zeng, Y., & Hansen, P. (2026). Trust it or not: Understanding users’ motivations and strategies for assessing the credibility of AI-generated information . New media & society , 28 (2), 507-529. Gilbert, S., Adler, R., Holoyad, T., & Weicken, E. (2025). Could transparent model cards with layered accessible information drive trust and safety in health AI?. npj Digital Medicine , 8 (1), 124. Lee, C., & Cha, K. (2025). Toward the dynamic relationship between AI transparency and trust in AI: a case study on ChatGPT . International Journal of Human–Computer Interaction , 41 (13), 8086-8103. Sapkota, R., Raza, S., & Karkee, M. (2025). Comprehensive analysis of transparency and accessibility of chatgpt, deepseek, and other sota large language models . arXiv preprint arXiv:2502.18505 . Hayes, P., & Fitzpatrick, N. (2025). Narrativity and responsible and transparent ai practices. AI & SOCIETY , 40 (2), 605-625. Sloane, M., & Wüllhorst, E. (2025). A systematic review of regulatory strategies and transparency mandates in AI regulation in Europe, the United States, and Canada. Data & Policy , 7 , e11. Kumar, A., Hora, H., Rohilla, A., Kumar, P., & Gautam, R. (2025). Explainable Artificial Intelligence (XAI) for Healthcare: Enhancing Transparency and Trust. In International Conference on Cognitive Computing and Cyber Physical Systems (pp. 295-308) . Springer, Singapore. https://doi.org/10.1007/978-981-97-7371-8_23 Bilal, A., Ebert, D., & Lin, B. (2025). LLMs for Explainable AI: A Comprehensive Survey. arXiv preprint arXiv:2504.00125. https://doi.org/10.48550/arXiv.2504.00125 Xu, X., Yu, R., Yuan, M., & Zheng, J. (2025). Bidirectional transparency in human-agent communications: effects of direction and level of transparency. Ergonomics, 1-19. https://doi.org/10.1080/00140139.2025.2456535 Spitzer, P., Holstein, J., Morrison, K., Holstein, K., Satzger, G., & Kühl, N. (2024). Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration. arXiv preprint arXiv:2409.12809. Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I. H., Muller, M., & Riedl, M. O. (2024, May). The Who in XAI: How AI Background Shapes Perceptions of AI Explanations. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-32). Kumar, V., Yadav, V., & Singh, A. P. (2024, August). Demystifying Predictive Maintenance: Achieving Transparency through Explainable AI. In 2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET) (pp. 1-7). Bo, J. Y., Hao, P., & Lim, B. Y. (2024, May). Incremental XAI: Memorable Understanding of AI with Incremental Explanations. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-17). Nimmo, R., Constantinides, M., Zhou, K., Quercia, D., & Stumpf, S. (2024, May). User Characteristics in Explainable AI: The Rabbit Hole of Personalization?. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-13). Shen, J., DiPaola, D., Ali, S., Sap, M., Park, H. W., & Breazeal, C. (2024). Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study. JMIR Mental Health, 11 (1), e62679. Xuan, Y., Small, E., Sokol, K., Hettiachchi, D., & Sanderson, M. (2024). Comprehension is a double-edged sword: Over-interpreting unspecified information in intelligible machine learning explanations. International Journal of Human-Computer Studies , 103376. Palazzolo, P., Stahl, B., & Webb, H. (2024, September). Measurable Trust: The Key to Unlocking User Confidence in Black-Box AI. In Proceedings of the Second International Symposium on Trustworthy Autonomous Systems (pp. 1-7). Coeckelbergh, M. (2020). Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability. Science and Engineering Ethics, 26(4), 2051–2068. Adebayo, J., Muelly, M., Abelson, H., & Kim, B. (2022, January 28). Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation. International Conference on Learning Representations. Anjomshoae, S., Najjar, A., Calvaresi, D., & Främling, K. (2019). Explainable Agents and Robots: Results from a Systematic Literature Review. 1078–1088. Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY, 35(3), 611–623. Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. Beyond State v Loomis: Artificial intelligence, government algorithmization and accountability | International Journal of Law and Information Technology | Oxford Academic. (n.d.). Retrieved May 23, 2022 Constantinescu, M., Voinea, C., Uszkai, R., & Vică, C. (2021). Understanding responsibility in Responsible AI. Dianoetic virtues and the hard problem of context. Ethics and Information Technology, 23(4), 803–814. Gunning, D., Vorm, E., Wang, Y., & Turek, M. (2021). DARPA’s explainable AI (XAI) program: A retrospective. Authorea Preprints. Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer International Publishing. Ghallab, M. (2019). Responsible AI: Requirements and challenges. AI Perspectives, 1(1), 3. Givens, A. R., & Morris, M. R. (2020). Centering disability perspectives in algorithmic fairness, accountability, & transparency. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 684–684. Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: Accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256. Kang, S. S. (2020). Algorithmic accountability in public administration: The GDPR paradox. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 32–32. Lima, G., & Cha, M. (2020). Responsible AI and Its Stakeholders (arXiv:2004.11434). arXiv. Lu, Q., Zhu, L., Xu, X., & Whittle, J. (2022). Responsible-AI-by-Design: A Pattern Collection for Designing Responsible AI Systems (arXiv:2203.00905). arXiv. Martin, K. (2019). Ethical Implications and Accountability of Algorithms. Journal of Business Ethics: JBE, 160(4), 835–850. Phillips, P. J., Hahn, C. A., Fontana, P. C., Broniatowski, D. A., & Przybocki, M. A. (2020). Four Principles of Explainable Artificial Intelligence [Preprint]. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing (arXiv:2001.00973). arXiv. Rakova, B., Yang, J., Cramer, H., & Chowdhury, R. (2021). Where Responsible AI meets Reality: Practitioner Perspectives on Enablers for Shifting Organizational Practices. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1–23. Ratti, E., & Graves, M. (2022). Explainable machine learning practices: Opening another black box for reliable medical AI. AI and Ethics. Scantamburlo, T., Cortés, A., & Schacht, M. (2020). Progressing Towards Responsible AI (arXiv:2008.07326). arXiv. Schiff, D., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2020). Principles to Practices for Responsible AI: Closing the Gap (arXiv:2006.04707). arXiv. Shneiderman, B. (2021). Responsible AI: Bridging from ethics to practice. Communications of the ACM, 64(8), 32–35. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. California Management Review, 61(4), 15–42. Tigard, D. W. (2021). Responsible AI and moral responsibility: A common appreciation. AI and Ethics, 1(2), 113–117. Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14. Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 1–18.
Books
Zerilli, J. (2021). A citizen's guide to artificial intelligence Agüera y Arcas, B. (2025). What is intelligence? Lessons from AI about evolution, computing, and minds Tripathy, B. K., & Seetha, M. (2024). Explainable, interpretable, and transparent AI systems. Explainable Artificial Intelligence for Cyber Security —Google Books. (n.d.)Molnar, C. (2020). Interpretable Machine Learning. Lulu.com. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
Blogs
