Explaining Go: Challenges in Achieving Explainability in AI Go Programs

Zack Garrett /December 7, 2023

How to cite this article:
Garrett, Z. (2023). Explaining Go: Challenges in Achieving Explainability in AI Go Programs. Journal of Go Studies, 17(2), 29-60. doi: 10.62578/464862

Abstract
      There has been a push in recent years to provide better explanations for how AIs make their decisions. Most of this push has come from the ethical concerns that go hand in hand with AIs making decisions that affect humans. Outside of the strictly ethical concerns that have prompted the study of explainable AIs (XAIs), there has been research interest in the mere possibility of creating XAIs in various domains. In general, the more accurate we make our models the harder they are to explain. Go playing AIs like AlphaGo and KataGo provide fantastic examples of this phenomenon. In this paper, I discuss a non-exhaustive list of the leading theories of explanation and what each of these theories would say about the explainability of AI-played moves of Go. Finally, I consider the possibility of ever explaining AI-played Go moves in a way that meets the four principles of XAI. I conclude, somewhat pessimistically, that Go is not as imminently explainable as other domains. As such, the probability of having an XAI for Go that meets the four principles is low.

References

Achinstein, P. (1985). The nature of explanation. Oxford University Press. link

Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021). Explainable artificial intelligence: an analytical review. WIREs Data Mining and Knowledge Discovery, 11(5). Portico. doi: 10.1002/widm.1424

Arun, K., Ishan, G., & Sanmeet, K. (2016). Loan approval prediction based on machine learning approach. IOSR J. Comput. Eng, 18(3), 18-21. link

Baron, S. (2023). Explainable AI and Causal Understanding: Counterfactual Approaches Considered. Minds and Machines, 33(2), 347–377. doi: 10.1007/s11023-023-09637-x

Brandom, R. B. (1998). Insights and Blindspots of Reliabilism. Monist, 81(3), 371–392. doi: 10.5840/monist199881317

Hempel, C. G. (1965). Aspects of scientific explanation.

Hempel, C. G., & Oppenheim, P. (1948). Studies in the Logic of Explanation. Philosophy of Science, 15(2), 135–175. doi: 10.1086/286983

Ghasemi, M., Anvari, D., Atapour, M., Stephen wormith, J., Stockdale, K. C., & Spiteri, R. J. (2020). The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism. Criminal Justice and Behavior, 48(4), 518–538. doi: 10.1177/0093854820969753

Lewis, D. (1973). Causation. The Journal of Philosophy, 70(17), 556. doi: 10.2307/2025310

Maclure, J. (2021). AI, Explainability and Public Reason: The Argument from the Limitations of the Human Mind. Minds and Machines, 31(3), 421–438. doi: 10.1007/s11023-021-09570-x

Park, W. (2022). How to Make AlphaGo’s Children Explainable. Philosophies, 7(3), 55. doi: 10.3390/philosophies7030055

Phillips, P. J., Hahn, C. A., Fontana, P. C., Yates, A. N., Greene, K., Broniatowski, D. A., & Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. National Institute of Standards and Technology (U.S.). doi: 10.6028/nist.ir.8312

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " 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). doi: 10.1145/2939672.2939778
Salmon, W. C. (1971). Statistical Explanation and Statistical Relevance. doi: 10.2307/j.ctt6wrd9p

Selbst, A. & Powles, J.. (2018). “Meaningful Information” and the Right to Explanation. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, in Proceedings of Machine Learning Research 81:48-48. link

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359. doi: 10.1038/nature24270