Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Domain Knowledge Incorporated Counterfactual Example-Based Explanation for Bankruptcy Prediction Model

부도예측모형에서 도메인 지식을 통합한 반사실적 예시 기반 설명력 증진 방법

  • 조수현 (이화여자대학교 빅데이터분석학) ;
  • 신경식 (이화여자대학교 경영대학)
  • Received : 2022.05.20
  • Accepted : 2022.06.23
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

부도예측모형은 여러 금융기관의 신용평가모형의 지식기반(knowledge base)로 이용되고 있으며 최근 머신러닝 기법의 발전으로 이를 도입하여 고도화하려는 다양한 시도가 진행 중이다. 그러나 실제 이러한 모형이 도입되기 위해서는 모형을 이용하는 사용자와 설명제공 대상인 고객의 이해와 수용이 전제되어야 한다. 그러나 사용자에게 제공되는 설명이 현실적 타당성(feasibility)이 결여되어 있다면 모형의 신뢰성과 수용도에 부정적인 영향을 미친다. 이에 따라 본 연구는 도메인 지식을 설명 생성 알고리즘에 통합하여 현실적으로 타당한 설명을 사용자에게 제공하고자 한다. 본 연구에서는 머신러닝 기반의 부도예측 모형에 설명력을 더하는 방법으로 반사실적 예시(counterfactual example) 기반의 로컬영역에서의 설명을 제공하는 모델을 제안한다. 제안 모델은 모형에 이용된 재무변수의 특성을 설명력 생성 알고리즘에 통합하여 설명의 현실적 가능성을 확보하고 이를 통해 사용자의 이해와 수용을 도모하고자 한다. 또한 본 연구에서는 반사실적 예시기반 설명을 위해 유전알고리즘(GA)를 이용하며 다목적함수를 목적함수로 설정하여 반사실적 예시의 주요 기준이 되는 항목을 반영하고 있다. 본 연구는 대표적인 머신러닝 기법인 인공신경망을 이용해 부도예측모형을 학습시킨 뒤, 사후적 방법(post-hoc)으로 설명을 위한 알고리즘을 도입하여 기존의 모형 설명 알고리즘인 LIME과 현실적 가능성이 결여된 반사실적 예시 기반 알고리즘과 비교하였다. 더 나아가 제안방법의 금융/회계 분야의 종사자를 대상으로 서베이를 진행하여 제안 방법의 설명의 질을 정성적으로 평가하였다.

Keywords

References

  1. Adhikari, A., Tax, D. M. J., Satta, R., & Faeth, M. (2019). LEAFAGE: Example-based and Feature importance-based Explanations for Black-box ML models. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2019.8858846
  2. Bank of Korea. (2020). Financial Statement Analysis for 2019. In Bank of Korea.
  3. Belkoura, S., Zanin, M., & Latorre, A. (2019). Fostering interpretability of data mining models through data perturbation. Expert Systems With Applications, 137, 191-201. https://doi.org/10.1016/j.eswa.2019.07.001
  4. Binns, R., van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018). "It's reducing a human being to a percentage"; perceptions of justice in algorithmic decisions. Conference on Human Factors in Computing Systems - Proceedings, 2018-April. https://doi.org/10.1145/3173574.3173951
  5. Byrne, R. M. J. (2019). Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning. International Joint Conference on Artificial Intelligence (IJCAI-19), 6276-6282.
  6. Dandl, S., Molnar, C., Binder, M., & Bischl, B. (2020). Multi-Objective Counterfactual Explanations. Parallel Problem Solving from Nature - PPSN XVI. PPSN 2020. Lecture Notes in Computer Science, 448-469. https://doi.org/10.1007/978-3-030-58112-1
  7. Davidson, W. (2019). Financial Statement Analysis Basis For Management Advice. Association of International Certified Professional Accountants, Inc.
  8. Fernandez, R. R., Diego, I. M. de, Acena, V., Fernandez-isabel, A., & Moguerza, J. M. (2020). Random forest explainability using counterfactual sets. Information Fusion, 63, 196-207. https://doi.org/10.1016/j.inffus.2020.07.001
  9. Gadanecz, B., & Jayaram, K. (2008). Measures of financial stability - a review. IFC Conference on "Measuring Financial Innovation and Its Impact," 365-380.
  10. Gedikli, F., Jannach, D., & Ge, M. (2014). How should i explain? A comparison of different explanation types for recommender systems. International Journal of Human Computer Studies, 72(4), 367-382. https://doi.org/10.1016/j.ijhcs.2013.12.007
  11. Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., & Lee, S. (2019). Counterfactual visual explanations. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 4254-4262.
  12. Grath, R. M., Costabello, L., le Van, C., Sweeney, P., Kamiab, F., Shen, Z., & Lecue, F. (2018). Interpretable credit application predictions with counterfactual explanations. NIPS 2018 Workshop on Challenges and Opportunities for AI InFinancial Services: The Impact of Fairness, Explainability, Accuracy, and Privacy.
  13. Guidotti, R., Monreale, A., Ruggieri, S., Giannotti, F., Pedreschi, D., & Turini, F. (2019). Factual and Counterfactual Explanations for Black Box Decision Making. IEEE Intelligent Systems, November/December, 14-23.
  14. Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., & Giannotti, F. (2018). Local rule-based explanations of black box decision systems. In arXiv (Issue May).
  15. Hashemi, M., & Fathi, A. (2020). PermuteAttack: Counterfactual explanation of machine learning credit scorecards. ArXiv.
  16. Helfert, E. A. (2001). Financial Analysis Tools and Techniques: A Guide for Managers. McGrawHill Education.
  17. Islam, M. R., Ahmed, M. U., Barua, S., & Begum, S. (2022). A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks. Applied Sciences, 12(3). https://doi.org/10.3390/app12031353
  18. Kanamori, K., Takagi, T., Kobayashi, K., & Arimura, H. (2020). DACE: Distribution-aware counterfactual explanation by mixed-integer linear optimization. IJCAI International Joint Conference on Artificial Intelligence, 2855-2862. https://doi.org/10.24963/ijcai.2020/395
  19. Keane, M. T., & Smyth, B. (2020). Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12311 LNAI, 163-178. https://doi.org/10.1007/978-3-030-58342-2_11
  20. Kenny, E. M., Ford, C., Quinn, M., & Keane, M. T. (2021). explanations-by-example : The effect of explanations and error-rates in XAI user studies . Artificial Intelligence, 294, 103459. https://doi.org/10.1016/j.artint.2021.103459
  21. Kenny, E. M., & Keane, M. T. (2021a). Explaining Deep Learning using examples: Optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI. Knowledge-Based Systems, 233, 107530. https://doi.org/10.1016/j.knosys.2021.107530
  22. Kenny, E. M., & Keane, M. T. (2021b). On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 11575-11585. http://arxiv.org/abs/2009.06399
  23. Le, T., Wang, S., & Lee, D. (2020). GRACE : Generating Concise and Informative Contrastive Sample to Explain Neural Network Model ' s Prediction. KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 238-248. https://doi.org/https://doi.org/10.1145/3394486.3403066
  24. Lundberg, S. M., & Lee, S. (2017). A Unified Approach to Interpreting Model Predictions. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, Section 2, 1-10.
  25. Mahajan, D., Tan, C., & Sharma, A. (2019). Preserving causal constraints in counterfactual explanations for machine learning classifiers. 33rd Conferenceon Neural Information Processing Systems.
  26. Melanie, M. (1999). An Introduction to Genetic Algorithms. A Bradford Book The MIT Press.
  27. Miller, T. (2019). Explanation in artificial intelligence : Insights from the social sciences. Artificial Intelligence, 267, 1-38. https://doi.org/10.1016/j.artint.2018.07.007
  28. Mittelstadt, B., Russell, C., & Wachter, S. (2019). Explaining explanations in AI. FAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency, 279-288. https://doi.org/10.1145/3287560.3287574
  29. Mothilal, R. K., Sharma, A., & Tan, C. (2020). Explaining machine learning classifiers through diverse counterfactual explanations. Conference on Fairness, Accountability, and Transparency, 607-617. https://doi.org/10.1145/3351095.3372850
  30. Poyiadzi, R., Sokol, K., Santos-rodriguez, R., Bie, T. de, & Flach, P. (2020). FACE : Feasible and Actionable Counterfactual Explanations. AAAI/ACM Conference on AI, Ethics, and Society (AIES). https://doi.org/https://doi.org/10.1145/3375627.3375850 1
  31. Ribeiro, M. T., & Guestrin, C. (2016). " Why Should I Trust You ?" Explaining the Predictions of Any Classifier. KDD 2016 San Francisco, CA, USA. https://doi.org/http://dx.doi.org/10.1145/2939672.2939778
  32. Ribeiro, M. T., Singh, S., & Guestrin, C. (2018). Anchors : High-Precision Model-Agnostic Explanations. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 1527-1535.
  33. Russell, C. (2019). Efficient search for diverse coherent explanations. Conference on Fairness, Accountability, and Transparency, January, 20-28. https://doi.org/10.1145/3287560.3287569
  34. Schneider, C. Q., & Rohlfing, I. (2016). Case Studies Nested in Fuzzy-set QCA on Sufficiency: Formalizing Case Selection and Causal Inference. Sociological Methods and Research, 45(3), 526-568. https://doi.org/10.1177/0049124114532446
  35. Sharma, P., Wadhwa, A., & Komal, K. (2014). Analysis of Selection Schemes for Solving an Optimization Problem in Genetic Algorithm. International Journal of Computer Applications, 93(11), 1-3. https://doi.org/10.5120/16256-5714
  36. Stepin, I., Alonso, J. M., & Catala, A. (2021). A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence. IEEE Access, 9. https://doi.org/10.1109/ACCESS.2021.3051315
  37. Waa, J. van der, Nieuwburg, E., Cremers, A., & Neerincx, M. (2021). Evaluating XAI : A comparison of rule-based and example-based explanations. Artificial Intelligence, 291, 103404. https://doi.org/10.1016/j.artint.2020.103404
  38. Wachter, S., Mittelstadt, B., & Russell, C. (2018). COUNTERFACTUAL EXPLANATIONS WITHOUT OPENING THE BLACK BOX : AUTOMATED DECISIONS AND THE GDPR. Harvard Journal of Law & Technology, 31(2), 842-887.