AI Accountability

Research Publications

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 (arXiv:2404.07461). arXiv. 

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 (arXiv:2407.14779). arXiv. 

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.  

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. 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. 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 (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.

Marquand House Collective. (2026). Auditing AI. MIT Press.

 

Shafik, W. (2026). The Dark Side of AI: A Human and Societal Perspective. Springer Nature.

 

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. 

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