Psychological foundation of explanations

  1. Measuring individual differences in implicit cognition: the implicit association test. Anthony G. Greenwald, Debbie E. McGhee, Jordan L.K. Schwartz. Journal of personality and social psychology 1998. [paper]
  2. Conversational processes and causal explanation. Denis J. Hilton. Psychological Bulletin 1990. [paper]
  3. Explanation and understanding. Frank C. Keil. Annual Review of Psychology 2006. [paper]
  4. The structure and function of explanations. Tania Lombrozo. Trends in cognitive sciences 2006. [paper]
  5. A unified approach to interpreting model predictions. Scott M. Lundberg, Su-In Lee. NeurIPS 2017. [paper]
  6. Explanation in human-AI systems: A literature metareview, synopsis of key ideas and publications, and bibliography for explainable AI. Shane T. Mueller, Robert R. Hoffman, William Clancey, Abigail Emrey, Gary Klein. 2019. [paper]
  7. Telling more than we can know: verbal reports on mental processes. Richard E. Nisbett, Timothy D. Wilson. Psychological review 1977. [paper]
  8. Experiential Explanation. Aronowitz, S., Lombrozo, T.. Topics in Cognitive Science 2020. [paper]
  9. The Explanatory Effect of a Label: Its Influence on a Category Persists Even If We Forget the Label. Aslanov, I. A., Sudorgina, Y. V., & Kotov, A. A.. Frontiers in Psychology 2021 [paper].
  10. The explanatory effect of a label: Explanations with named categories are more satisfying. Giffin, C., Wilkenfeld, D., & Lombrozo, T.. Cognition 2017. [paper]
  11. Stability, breadth and guidance. Blanchard, T., Vasilyeva, N., & Lombrozo, T.. Philosophical Studies 2018. [paper]
  12. Community appeal: Explanation without information. Hemmatian, B., & Sloman, S. A.. Journal of Experimental Psychology: General 2018. [paper]
  13. Folkscience: Coarse interpretations of a complex reality. Keil, F. C.. Trends in cognitive sciences 2003. [paper]
  14. How do people know?. Kuhn, D.. Psychological science 2001. [paper]
  15. Contrastive explanation. Lipton, P.. Royal Institute of Philosophy Supplements 1990. [paper]
  16. Explanation and abductive inference.. Lombrozo, T.. Oxford handbook of thinking and reasoning 2012. [chapter]
  17. Explanatory preferences shape learning and inference. Lombrozo, T.. Trends in Cognitive Sciences 2016. [paper]
  18. Mechanistic versus functional understanding.. Lombrozo, T., & Wilkenfeld, D.. Varieties of Understanding: New Perspectives from Philosophy, Psychology, and Theology 2019. [chapter]
  19. The shadows and shallows of explanation. Wilson, R. A., & Keil, F.. Minds and machines 1998. [paper]
  20. “Scientific Explanation”. Woodward, J. & Ross, L.. The Stanford Encyclopedia of Philosophy 2021. [url]

Explanation methods

  1. Counterfactual visual explanations. Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee. ICML 2019. [paper]
  2. Efficient Data Representation by Selecting Prototypes with Importance Weights. Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi, Charu Aggarwal. ICDM 2019. [paper]

Human subject evaluations

  • Towards a rigorous science of interpretable machine learning. Doshi-Velez, F., & Kim, B.. 2017. [paper]
  • Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems. Buçinca, Z., Lin, P., Gajos, K. Z., & Glassman, E. L. IUI 2020. [paper]
  • Human-Centered Explainable AI (XAI): From Algorithms to User Experiences. Liao, Q. V., & Varshney, K. R.. 2021. [paper]
  • Questioning the AI: informing design practices for explainable AI user experiences. Liao, Q. V., Gruen, D., & Miller, S. CHI 2020. [paper]
  • Beyond expertise and roles: A framework to characterize the stakeholders of interpretable machine learning and their needs. Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A.. CHI 2021. [paper]
  • On human predictions with explanations and predictions of machine learning models: A case study on deception detection. Lai, V., & Tan, C. FaCCT 2019. [paper]
  • Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. Zhang, Y., Liao, Q. V., & Bellamy, R. K.. FaCCT 2020. [paper]
  • Are explanations helpful? a comparative study of the effects of explanations in AI-assisted decision-making. Wang, X., & Yin, M.. IUI 2021. [paper]
  • Developing and validating trust measures for e-commerce: An integrative typology. McKnight, D. H., Choudhury, V., & Kacmar, C.. Information systems research 2002. [paper]
  • Explaining decision-making algorithms through UI: Strategies to help non-expert stakeholders. Cheng, H. F., Wang, R., Zhang, Z., O’Connell, F., Gray, T., Harper, F. M., & Zhu, H.. CHI 2019. [paper]
  • Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies. Lai, V., Chen, C., Liao, Q. V., Smith-Renner, A., & Tan, C.. 2021. [paper]
  • ** Interpreting interpretability: understanding data scientists’ use of interpretability tools for machine learning*. *Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J.. CHI 2020. [paper]
  • Explaining models: an empirical study of how explanations impact fairness judgment. Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K., & Dugan, C.. IUI 2019. [paper]

Evaluation based on human-provided explanations

  1. Evaluating and Characterizing Human Rationales. Samuel Carton, Anirudh Rathore, Chenhao Tan. EMNLP 2020. [paper]
  2. ERASER: A Benchmark to Evaluate Rationalized NLP Models. Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace. ACL 2020. [paper]
  3. Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences. Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, Dan Roth. NAACL 2018. [paper]