Research Publications
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.
Beyond State v Loomis: Artificial intelligence, government algorithmization and accountability | International Journal of Law and Information Technology | Oxford Academic. (n.d.).
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.
Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., & Müller, K.-R. (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer Nature.