Penn State CSRAI Research
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). Jang, E., & Plaisance, P. L. (2025). Textual and Comparative Analyses on AI Policies: How Big Tech and Their Global Watchdogs Frame Virtues and Responsibility . Journal of Media Ethics , 40 (4), 187-204. Chen, C., Jang, E., & Sundar, S. S. (2025). Racial Bias in AI Training Data: Do Laypersons Notice? Media Psychology , 1-28. Yamashita, M., Tran, T., Zhang, D. C., & Lee, D. (2025, November). Unmasking Fake Careers: Detecting Machine-Generated Career Trajectories via Multi-layer Heterogeneous Graphs . In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 20893-20908). Ramakrishnan, A. A., Saeedi, A., Hassanzadeh, H. R., Mohaghegh, F., & Lee, D. (2026). Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation . arXiv preprint arXiv:2603.19264 . Chang, A., Huang, L., Bhatia, P., Kass-Hout, T., Ma, F., & Xiao, C. (2025). Medheval: Benchmarking hallucinations and mitigation strategies in medical large vision-language models . arXiv preprint arXiv:2503.02157 . Zeng, Y., Wu, Y., Zhang, X., Wang, H., & Wu, Q. (2024). Autodefense: Multi-agent llm defense against jailbreak attacks . arXiv preprint arXiv:2403.04783 . Cao, B., Li, C., Cao, Y., Ge, Y., Wang, T., & Chen, J . (2025). You Can't Steal Nothing: Mitigating Prompt Leakages in LLMs via System Vectors . arXiv preprint arXiv:2509.21884 . Yamashita, M., Tran, T., Zhang, D. C., & Lee, D . (2025). Unmasking Fake Careers: Detecting Machine-Generated Career Trajectories via Multi-layer Heterogeneous Graphs . arXiv preprint arXiv:2509.19677 . 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 . arXiv preprint arXiv:2504.01153 . Chen, C., Jang, E., & Sundar, S. S . (2025). Racial Bias in AI Training Data: Do Laypersons Notice? . Media Psychology , 1-28. Wang, H., Cao, B., Cao, Y., & Chen, J. (2025). TruthFlow: Truthful LLM Generation via Representation Flow Correction. arXiv preprint arXiv:2502.04556. Yin, Z., Cao, Y., Liu, H., Wang, T., Chen, J. , & Ma, F. (2025). Towards Robust Multimodal Large Language Models Against Jailbreak Attacks. arXiv preprint arXiv:2502.00653. Fang, H., Qin, C., Xu, R., Liu, F., Liu, Y., Sun, L., ... & Yin, W. (2025). Could AI Trace and Explain the Origins of AI-Generated Images and Text?. arXiv preprint arXiv:2504.04279. Macko, D., Ramakrishnan, A. A., Lucas, J. S., Moro, R., Srba, I., Uchendu, A., & Lee, D. (2025). Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation. arXiv preprint arXiv:2503.23242. Chang, Y., Cao, B., & Lin, L. (2025). Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation. arXiv preprint arXiv:2503.03106. Zou, X., Kang, J., Kesidis, G., & Lin, L. (2025). Understanding and Rectifying Safety Perception Distortion in VLMs. arXiv preprint arXiv:2502.13095. Jang, E., Lee, H. M., Lee, S., Jung, Y., & Sundar, S. S. (2025, April). Too Good to be False: How Photorealism Promotes Susceptibility to Misinformation. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-8). Liu, J., Zeng, Y., Zhang, S., Zhang, C., Højmark-Bertelsen, M., Gadeberg, M. N., ... & Wu, Q. (2025). Divide, Optimize, Merge: Fine-Grained LLM Agent Optimization at Scale. arXiv preprint arXiv:2505.03973. Zhao, A., Wu, Y., Yue, Y., Wu, T., Xu, Q., Lin, M., ... & Huang, G. (2025). Absolute Zero: Reinforced self-play reasoning with zero data. arXiv preprint arXiv:2505.03335. Zhang, S., Yin, M., Zhang, J., Liu, J., Han, Z., Zhang, J., ... & Wu, Q. (2025). Which agent causes task failures and when? on automated failure attribution of llm multi-agent systems. arXiv preprint arXiv:2505.00212. Allik, S., Azafrani, R., Barnes, D., Bode, I., Conn, A., Lach M, E., ... & Wagner R, A. (2024). White paper - A Framework for Human Decision-Making through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications. A Framework for Human Decision-Making through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications , 1-63. Zhang, Z., Wang, F., Li, X., Wu, Z., Tang, X., Liu, H., ..., Yin, W. , & Wang, S. (2024). Does your LLM truly unlearn? An embarrassingly simple approach to recover unlearned knowledge. arXiv preprint arXiv:2410.16454. Suryanarayana, S., Sangwan, R. S., Srinivasan, S. M., & Badr, Y. (2024). Improving Safety of AI Systems. IEEE Reliability Magazine. Pang, Y., Xiong, A. , Zhang, Y., & Wang, T. (2024). Towards Understanding Unsafe Video Generation. arXiv preprint arXiv:2407.12581 . Venkit, P. N., Chakravorti, T., Gupta, V., Biggs, H., Srinath, M., Goswami, K., Rajtmajer, S. , & Wilson, S. ( 2024). "Confidently Nonsensical?’’: A Critical Survey on the Perspectives and Challenges of “Hallucinations” in NLP Sundar, S. S., & Liao, M. (2023). Calling BS on ChatGPT: Reflections on AI as a Communication Source. Journalism & Communication Monographs , 25 (2), 165–180. Ghosh, S., Venkit, P. N., Gautam, S., Wilson, S., & Caliskan, A. (2024). Do Generative AI Models Output Harm while Representing Non-Western Cultures: Evidence from A Community-Centered Approach 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. 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. 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.
Articles
Chen, Z. (2026). Operational responsibility in AI governance: a user-centric liability framework . AI and Ethics , 6 (3), 306. Sayyadi, M. (2026). The ethics of AI: Pursuing accountability for assured data to make right decisions. Business Information Review , 43 (1), 14-19. Chocron, R., Chayim, D. J. B., Lenga, E., Gressel, G., Oprea, A., & Mirsky, Y. (2026). Who Owns This Agent? Tracing AI Agents Back to Their Owners . arXiv preprint arXiv:2605.16035 . Tibebu, H., & Shemtaga, H. (2026). The accountability horizon: An impossibility theorem for governing human-agent collectives . arXiv preprint arXiv:2604.07778 . Solozobov, O. (2026). Decision Evidence Maturity Model for Agentic AI: A Property-Level Method Specification . arXiv preprint arXiv:2605.04093 . Lucaj, L., Bok, F., & van der Smagt, P. (2026, June). Auditing a Robotic System for the AI Act . In The 2026 ACM Conference on Fairness, Accountability, and Transparency (pp. 7893-7915). Cui, X., Zou, Y., Li, Z., Li, P., Xu, X., Liu, X., & Huang, H. (2026, March). T2Agent: A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search . In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 1, pp. 175-183). Pesaranghader, A., & Li, E. (2026). Hallucination detection and mitigation in large language models . arXiv preprint arXiv:2601.09929 . Sorrentino, S., Barbini, M., & Gatica-Perez, D. (2026). FAccT-Checked: A Narrative Review of Authority Reconfigurations and Retention in AI-Mediated Journalism . arXiv preprint arXiv:2604.21864 . Kolt, N., Caputo, N., Boeglin, J., O'keefe, C., Bommasani, R., Casper, S., ... & Zittrain, J. (2026). Legal alignment for safe and ethical ai . arXiv preprint arXiv:2601.04175 . Hardcastle, F., Henne, K., Harb, J. I., Lee, A., Viana, J. N., & Halford, S. (2026). Unblackboxing how sociotemporalities inform AI accountability: The case of targeted advertising . Social Science Computer Review , 44 (1), 131-149. Jang, M. (2026). Building Trust in AI: The Role of Technical Capacity, Social Risk, and Corporate Institutional Accountability . Information , 17 (2), 212. Law, J. (2026). The ethical imperative of algorithmic fairness in AI-enabled hiring: a critical analysis of bias, accountability, and justice . AI and Ethics , 6 (1), 65. Cong-Lem, N. (2026). “Is this Really your Work?”: A Qualitative Study of Teacher-Led Interviews and Student Accountability in the Age of Generative AI . Journal of Academic Ethics , 24 (1), 29. Ibitoye, A. O., Johnson, D. R., Sorinolu, B. G., Orji, R., & Christiana, A. O. (2026). A dynamic contextual responsibility framework for evaluating large language models in socio-technical contexts . AI and Ethics , 6 (2), 191. Grote, G., Parker, S. K., & Crowston, K. (2026). Taming artificial intelligence: A theory of control-accountability alignment among AI developers and users . Academy of Management Review , 51 (2), 278-299. Goldsmith, S. (2026). AI and the Transformation of Accountability and Discretion in Urban Governance . Urban Governance . Frimpong, V., & Botchey, O. K. (2026). Where Are the AI Governance Roles? An Early-Stage Empirical Mapping of Presence, Absence, and Structure in Organisational AI Oversight . Businesses , 6 (2), 18. McNealis, R. (2026). Shame in the machine: affective accountability and the ethics of AI. AI & SOCIETY , 41 (1), 403-413. Hadan, H., Hadi Mogavi, R., Zhang-Kennedy, L., & Nacke, L. E. (2026). Who is responsible when AI fails? Mapping causes, entities, and consequences of AI privacy and ethical incidents . International Journal of Human–Computer Interaction , 42 (8), 5933-5977. Tollon, F., & Vallor, S. (2026). Entangling ourselves with AI: Affirmative responsibility and the cultivation of responsible agency . Contemporary debates in the ethics of artificial intelligence , 161-181. Passlack, N., Hammerschmidt, T., & Posegga, O. (2026). With Great Power Comes Great Responsibility: What Shapes AI Literacy for Responsible Interactions of Knowledge Workers With AI?. Information Systems Frontiers , 28 (1), 11-46. Waltersdorfer, L., & Sabou, M. (2025). Leveraging knowledge graphs for AI system auditing and transparency . Journal of Web Semantics , 84 , 100849. Bartsch, S. C., Nguyen, L. H., Schmidt, J. H., Du, G., Adam, M., Benlian, A., & Sunyaev, A. (2025). The Present and Future of Accountability for AI Systems: A Bibliometric Analysis . Information Systems Frontiers , 1-22. Al-Dulaimi, A. O. M., & Mohammed, M. A. A. W. (2025). Legal responsibility for errors caused by artificial intelligence (AI) in the public sector . International Journal of Law and Management . Schmidt, J. H., Bartsch, S. C., Adam, M., & Benlian, A. (2025). Elevating developers’ accountability awareness in AI systems development . Business & Information Systems Engineering , 67 (1), 109-135. Yuan, Q., & Chen, T. (2025). Holding AI-based systems accountable in the public sector: A systematic review . Public Performance & Management Review , 1-34. Xia, B., Lu, Q., Zhu, L., Lee, S. U., Liu, Y., & Xing, Z. (2024, April). Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability. In Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering-Software Engineering for AI (pp. 100-111). Lo, S. K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H. Y., & Zhu, L. (2022). Toward trustworthy ai: Blockchain-based architecture design for accountability and fairness of federated learning systems. IEEE Internet of Things Journal, 10 (4), 3276-3284. Birhane, A., Steed, R., Ojewale, V., Vecchione, B., & Raji, I. D. (2024, April). AI auditing: The broken bus on the road to AI accountability. In 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) (pp. 612-643). Austin, D., Singh, S., Prakash, A., Venumuddala, V. R., & Ganeshan, S. (2024, October). Mapping Accountability in Human-AI Partnerships in Healthcare: Towards a Patient-centric Approach. In 2024 IEEE 12th Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1-8). 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. Novelli, C., Taddeo, M., & Floridi, L. (2024). Accountability in artificial intelligence: what it is and how it works. AI & Society, 39 (4), 1871-1882. Brown, S., Davidovic, J., & Hasan, A. (2021). The algorithm audit: Scoring the algorithms that score us. Big Data & Society, 8(1), 2053951720983865. Liu, H. W., Lin, C. F., & Chen, Y. J. (2019). Beyond State v Loomis: artificial intelligence, government algorithmization and accountability. International Journal of Law and Information Technology, 27 (2), 122-141. 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. Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way . 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 Lu, Q., Zhu, L., Xu, X., & Whittle, J. (2022). Responsible-AI-by-Design: A Pattern Collection for Designing Responsible AI Systems x 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 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 Schiff, D., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2020). Principles to Practices for Responsible AI: Closing the Gap 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
Bakiner, O. (2026). Governing AI: A Primer Quercia, D., & Constantinides, M. (Eds.). (2026). Operationalizing Responsible AI Marquand House Collective. (2026). Auditing AI . Shafik, W. (2026). The Dark Side of AI: A Human and Societal Perspective Marcella, A. J. (2025). Auditing Artificial Intelligence: A Handbook for Audit, Risk, and Security Professionals. CRC Press. Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., & Müller, K.-R. (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer Nature.
Blogs
