Akula , A. R., Wang , S. & Zhu , S.-C. 2020. CoCoX: Generating conceptual and counterfactual explanations via fault-lines. In AAAI, 2594–2601.

Angwin , J., Larson , J., Mattu , S. & Kirchner , L. 2016. Machine bias. ProPublica, May, 23.

Bromberger , S. 1966. Why–questions. In Mind and Cosmos: Essays in Contemporary Science and Philosophy, Colodny, R. G. (ed.). Pittsburgh University Press, 68–111.

Buchanan , B. & Shortliffe , E. 1984. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley.

Chandrasekaran , B., Tanner , M. C. & Josephson , J. R. 1989. Explaining control strategies in problem solving. IEEE Expert 4 (1), 9–15.

Chin-Parker , S. & Cantelon , J. 2017. Contrastive constraints guide explanation-based category learning. Cognitive Science 41 (6), 1645–1655.

Dhurandhar , A., Chen , P.-Y., Luss , R., Tu , C.-C., Ting , P., Shanmugam , K. & Das , P. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In Advances in Neural Information Processing Systems, 592–603.

Garfinkel , A. 1981. Forms of Explanation: Rethinking the Questions in Social Theory. Yale University Press.

Grice , H. P. 1975. Logic and conversation. In Syntax and Semantics 3: Speech Arts. Academic Press, 41–58.

Halpern , J. Y. 2015. A modification of the Halpern-Pearl definition of causality. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), 3022–3033.

Halpern , J. Y. & Pearl , J. 2005a. Causes and explanations: A structural-model approach. part i: Causes. The British Journal for the Philosophy of Science 56(4), 843–887.

Halpern , J. Y. & Pearl , J. 2005b. Causes and explanations: A structural-model approach. part ii: Explanations. The British Journal for the Philosophy of Science 56 (4), 889–911.

Haynes , S. R., Cohen , M. A. & Ritter , F. E. 2009. Designs for explaining intelligent agents. International Journal of Human-Computer Studies 67 (1), 90–110.

Hesslow , G. 1983. Explaining differences and weighting causes. Theoria 49 (2), 87–111.

Hesslow , G. 1988. The problem of causal selection. Contemporary Science and Natural Explanation: Commonsense Conceptions of Causality, 11–32.

Hilton , D. J. 1990. Conversational processes and causal explanation. Psychological Bulletin 107 (1), 65–81.

Kean , A. 1998. A characterization of contrastive explanations computation. In Pacific Rim International Conference on Artificial Intelligence. Springer, 599–610.

Krarup , B., Cashmore , M., Magazzeni , D. & Miller , T. 2019. Model-based contrastive explanations for explainable planning. In 2nd ICAPS Workshop on Explainable Planning (XAIP-2019). AAAI Press.

Lewis , D. 1986. Causal explanation. Philosophical Papers 2, 214–240.

Lim , B. Y. & Dey , A. K. 2009. Assessing demand for intelligibility in context-aware applications. In Proceedings of the 11th International Conference on Ubiquitous Computing. ACM, 195–204.

Linegang , M. P., Stoner , H. A., Patterson , M. J., Seppelt , B. D., Hoffman , J. D., Crittendon , Z. B. & Lee , J. D. 2006. Human-automation collaboration in dynamic mission planning: A challenge requiring an ecological approach. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 500 (23), 0 2482–2486.

Lipton , P. 1990. Contrastive explanation. Royal Institute of Philosophy Supplement 27, 247–266.

Madumal , P., Miller , T., Sonenberg , L. & Vetere , F. 2020. Explainable reinforcement learning through a causal lens. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2493–2500.

Mercado , J. E., Rupp , M. A., Chen , J. Y. C., Barnes , M. J., Barber , D. & Procci , K. 2016. Intelligent agent transparency in human–agent teaming for multi-UxV management. Human Factors 580 (3), 401–415.

Miller , T. 2018. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence. https://arxiv.org/abs/1706.07269.

Mothilal , R. K., Sharma , A. & Tan , C. 2020. Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 607–617.

Ruben , D.-H. 1987. Explaining contrastive facts. Analysis 470 (1), 35–37.

Slugoski , B. R., Lalljee , M., Lamb , R. & Ginsburg , G. P. 1993. Attribution in conversational context: Effect of mutual knowledge on explanation-giving. European Journal of Social Psychology 230 (3), 219–238.

Sreedharan , S., Srivastava , S. & Kambhampati , S. 2018. Hierarchical expertise level modeling for user specific contrastive explanations. In IJCAI, 4829–4836.

Stubbs , K., Hinds , P. & Wettergreen , D. 2007. Autonomy and common ground in human-robot interaction: A field study. IEEE Intell. Syst. 22 (2), 42–50.

Swartout , W. R. & Moore , J. D. 1993. Explanation in second generation expert systems. In Second Generation Expert Systems. Springer, 543–585.

Temple , D. 1988. The contrast theory of why–questions. Philosophy of Science 55(1), 141–151.

Van Bouwel , J. & Weber , E. 2002. Remote causes, bad explanations? Journal for the Theory of Social Behaviour 320 (4), 437–449.

Van Fraassen , B. C. 1980. The Scientific Image. Oxford University Press.

Waa , J., van Diggelen , J., Bosch , K. & Neerincx , M. 2018. Contrastive explanations for reinforcement learning in terms of expected consequences. In Proceedings of the Workshop on Explainable AI at IJCAI.

Wachter , S., Mittelstadt , B. & Russell , C. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology 31, 841.

Wang , D., Yang , Q., Abdul , A. & Lim , B. Y. 2019. Designing theory-driven user-centric explainable ai. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–15.

Winikoff , M. 2017. Debugging agent programs with Why?: Questions. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2017. IFAAMAS, 251–259.

Ylikoski , P. 2007. The idea of contrastive explanandum. In Rethinking Explanation. Springer, 27–42.