A Multi-layered Approach for Tailored Black-Box Explanations

1. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 1–42 (2018). Article no. 93

   Google Scholar 

2. Miller, T., Howe, P., Sonenberg, L.: Explainable AI: beware of inmates running the asylum. In: IJCAI 2017 Workshop on Explainable AI (XAI), vol. 36 (2017)

   Google Scholar 

3. Henin, C., Le Métayer, D.: Towards a generic framework for black-box explanations of algorithmic decision systems (Extended Version). Inria Research Report 9276. https://hal.inria.fr/hal-02131174

4. Tomsett, R., Braines, D., Harborne, D., Preece, A.D., Chakraborty, S.: Interpretable to whom? A role-based model for analyzing interpretable machine learning systems. CoRR abs/1806.07552 (2018)

   Google Scholar 

5. Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. arXiv:1910.10045 [cs]arXiv: 1910.10045

6. Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267. https://doi.org/10.1016/j.artint.2018.07.007

7. Weller, A.: Challenges for transparency. arXiv:1708.01870 [cs]arXiv: 1708.01870

8. Lipton, Z.C.: The mythos of model interpretability. arXiv:1606.03490 [cs, stat]arXiv: 1606.03490

9. Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harvard J. Law Technol. 31, 841–887 (2018)

   Google Scholar 

10. Stumpf, S., et al.: Toward harnessing user feedback for machine learning 10 (2007)

    Google Scholar 

11. Lim, B.Y., Dey, A.K., Avrahami, D.: Why and why not explanations improve the intelligibility of context-aware intelligent systems. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems - CHI 2009, p. 2119. ACM Press (2009). https://doi.org/10.1145/1518701.1519023

12. Lakkaraju, H., Kamar, E., Caruana, R., Leskovec, J.: Interpretable & explorable approximations of black box models. arXiv preprint arXiv:1707.01154

13. Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

14. Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., Detyniecki, M.: The dangers of post-hoc interpretability: unjustified counterfactual explanations. arXiv:1907.09294 [cs, stat]arXiv: 1907.09294

15. Henin, C., Le Métayer, D.: A multi-layered approach for interactive black-box explanations. Inria Research Report 9331. https://hal.inria.fr/hal-02498418

16. Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv e-prints arXiv:1702.08608 (2017)

17. Henin, C., Le Métayer, D.: A generic framework for black-box explanations. In: Proceedings of the International Workshop on Fair and Interpretable Learning Algorithms (FILA 2020). IEEE (2020)

    Google Scholar 

18. Ras, G., van Gerven, M., Haselager, P. Explanation methods in deep learning: users, values, concerns and challenges. CoRR abs/1803.07517. http://arxiv.org/abs/1803.07517

19. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018). https://doi.org/10.1109/ACCESS.2018.2870052

    Article  Google Scholar 

20. Wolf, C.T.: Explainability scenarios: towards scenario-based XAI design. In: Proceedings of the 24th International Conference on Intelligent User Interfaces - IUI 2019, pp. 252–257. ACM Press (2019). https://doi.org/10.1145/3301275.3302317

21. Poursabzi-Sangdeh, F., Goldstein, D.G., Hofman, J.M., Vaughan, J.W., Wallach, H.: Manipulating and measuring model interpretability. arXiv:1802.07810 [cs]arXiv: 1802.07810

22. Hall, M., et al.: A systematic method to understand requirements for explainable AI (XAI) systems 7 (2019)

    Google Scholar 

23. Arya, V., et al.: One explanation does not fit all: a toolkit and taxonomy of AI explainability techniques. arXiv:1909.03012 [cs, stat]arXiv: 1909.03012

24. Sokol, K., Flach, P.: One explanation does not fit all: the promise of interactive explanations for machine learning transparency. KI - Künstliche Intelligenz. http://dx.doi.org/10.1007/s13218-020-00637-y

25. Walton, D.: A dialogue system specification for explanation. Synthese 182(3), 349–374 (2011). https://doi.org/10.1007/s11229-010-9745-z

    Article  Google Scholar 

26. Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: A grounded interaction protocol for explainable artificial intelligence. arXiv:1903.02409 [cs]arXiv: 1903.02409

27. Nori, H., Jenkins, S., Koch, P., Caruana, R.: InterpretML: a unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223

28. Klaise, J., Van Looveren, A., Vacanti, G., Coca, A.: Alibi: Algorithms for monitoring and explaining machine learning models (2020)

    Google Scholar 

29. Biecek, P.: DALEX: explainers for complex predictive models in R. J. Mach. Learn. Res. 19(84), 1–5 (2018)

    MATH  Google Scholar 

30. Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should I trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

    Google Scholar 

31. Dhurandhar, A., Iyengar, V., Luss, R., Shanmugam, K.: A formal framework to characterize interpretability of procedures. arXiv:1707.03886 [cs]arXiv: 1707.03886

Download references