Annexe 4: Further reading

PROV provenance standard

• Moreau, L. & Missier, P. (2013). PROV-DM: The PROV Data Model. W3C Recommendation.
• Huynh, T. D., Stalla-Bourdillon, S. & Moreau, L. (2019). Provenance-based Explanations for Automated Decisions : Final IAA Project Report. 

Resources for exploring algorithm types

General

• Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83-85. 
• Molnar, C. (2019). Interpretable machine learning: A guide for making black box models explainable. 
• Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206.

Regularised regression (LASSO and Ridge)

• Gaines, B. R., & Zhou, H. (2016). Algorithms for fitting the constrained lasso. Journal of Computational and Graphical Statistics, 27(4), 861-871. 
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288. 

Generalised linear model (GLM)

• glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models
• Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22. 
• Simon, N., Friedman, J., Hastie, T., & Tibshirani, R. (2011). Regularization paths for Cox's proportional hazards model via coordinate descent. Journal of Statistical Software, 39(5), 1-13. 

Generalised additive model (GAM)

• Lou, Y., Caruana, R., & Gehrke, J. (2012). Intelligible models for classification and regression. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 150-158). ACM. 
• Wood, S. N. (2006). Generalized additive models: An introduction with R. CRC Press.

Decision tree (DT)

Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and Regression Trees. CRC Press.

Rule/decision lists and sets

• Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., & Rudin, C. (2017). Learning certifiably optimal rule lists for categorical data. The Journal of Machine Learning Research, 18(1), 8753-8830. 
• Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016, August). Interpretable decision sets: A joint framework for description and prediction. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1675-1684). ACM. 
• Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015). Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model. The Annals of Applied Statistics, 9(3), 1350-1371.
• Wang, F., & Rudin, C. (2015). Falling rule lists. In Artificial Intelligence and Statistics (pp. 1013-1022). 

Case-based reasoning (CBR)/ Prototype and criticism

• Aamodt, A. (1991). A knowledge-intensive, integrated approach to problem solving and sustained learning. Knowledge Engineering and Image Processing Group. University of Trondheim, 27-85. 
• Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI communications, 7(1), 39-59.
• Bichindaritz, I., & Marling, C. (2006). Case-based reasoning in the health sciences: What's next?. Artificial intelligence in medicine, 36(2), 127-135.
• Bien, J., & Tibshirani, R. (2011). Prototype selection for interpretable classification. The Annals of Applied Statistics, 5(4), 2403-2424. 
• Kim, B., Khanna, R., & Koyejo, O. O. (2016). Examples are not enough, learn to criticize! criticism for interpretability  In Advances in Neural Information Processing Systems (pp. 2280-2288).
• MMD-critic in python.
• Kim, B., Rudin, C., & Shah, J. A. (2014). The bayesian case model: A generative approach for case-based reasoning and prototype classification  In Advances in Neural Information Processing Systems (pp. 1952-1960). 

Supersparse linear integer model (SLIM)

• Jung, J., Concannon, C., Shroff, R., Goel, S., & Goldstein, D. G. (2017). Simple rules for complex decisions. Available at SSRN 2919024. 
• Rudin, C., & Ustun, B. (2018). Optimized scoring systems: toward trust in machine learning for healthcare and criminal justice. Interfaces, 48(5), 449-466.
• Ustun, B., & Rudin, C. (2016). Supersparse linear integer models for optimized medical scoring systems. Machine Learning, 102(3), 349-391.
• Optimized scoring systems for classification problems in python: https://github.com/ustunb/slim-python
• Simple customizable risk scores in python.

Resources for exploring supplementary explanation strategies

Surrogate models (SM)

• Bastani, O., Kim, C., & Bastani, H. (2017). Interpretability via model extraction. arXiv preprint arXiv:1706.09773. 
• Craven, M., & Shavlik, J. W. (1996). Extracting tree-structured representations of trained networks  In Advances in Neural Information Processing Systems (pp. 24-30). 
• Van Assche, A., & Blockeel, H. (2007). Seeing the forest through the trees: Learning a comprehensible model from an ensemble. In European Conference on Machine Learning (pp. 418-429). Springer, Berlin, Heidelberg. 
• Valdes, G., Luna, J. M., Eaton, E., Simone II, C. B., Ungar, L. H., & Solberg, T. D. (2016). MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine. Scientific reports, 6, 37854. 

Partial Dependence Plot (PDP)

• Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232. 
• Greenwell, B. M. (2017). pdp: an R Package for constructing partial dependence plots. The R Journal, 9(1), 421-436. 
• For the software in R see Partial Dependence Plots

Individual Conditional Expectations Plot (ICE)

• Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015). Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics, 24(1), 44-65.
• For the software in R see:
  • ICEbox: Individual Conditional Expectation Plot Toolbox
  • Package ICEbox

Accumulated Local Effects Plots (ALE)

Apley, D. W., & Zhu, J. (2019). Visualizing the effects of predictor variables in black box supervised learning models. 

Global variable importance

• Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
• Casalicchio, G., Molnar, C., & Bischl, B. (2018, September). Visualizing the feature importance for black box models. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 655-670). Springer, Cham.
• Fisher, A., Rudin, C., & Dominici, F. (2018). All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously. 
• Fisher, A., Rudin, C., & Dominici, F. (2018). Model class reliance: Variable importance measures for any machine learning model class, from the “Rashomon” perspective. arXiv preprint arXiv:1801.01489. 
• Hooker, G., & Mentch, L. (2019). Please Stop Permuting Features: An Explanation and Alternatives. arXiv preprint arXiv:1905.03151. 
• Zhou, Z., & Hooker, G. (2019). Unbiased Measurement of Feature Importance in Tree-Based Methods. arXiv preprint arXiv:1903.05179. 

Global variable interaction

• Friedman, J. H., & Popescu, B. E. (2008). Predictive learning via rule ensembles. The Annals of Applied Statistics, 2(3), 916-954.
• Greenwell, B. M., Boehmke, B. C., & McCarthy, A. J. (2018). A simple and effective model-based variable importance measure. arXiv preprint arXiv:1805.04755. 
• Hooker, G. (2004, August). Discovering additive structure in black box functions. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 575-580). ACM. 

Local Interpretable Model-Agnostic Explanation (LIME)

• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 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). ACM. 
• LIME in python: https://github.com/marcotcr/lime
  LIME experiments in python: https://github.com/marcotcr/lime-experiments
• Ribeiro, M. T., Singh, S., & Guestrin, C. (2018). Anchors: High-precision model-agnostic explanations. In Thirty-Second AAAI Conference on Artificial Intelligence. 
• Anchors in python
• Anchors experiments in python

Shapley Additive ExPlanations (SHAP)

• Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (pp. 4765-4774).
• Software for SHAP and its extensions in python.
• R wrapper for SHAP.
• Shapley, L. S. (1953). A value for n-person games. Contributions to the Theory of Games, 2(28), 307-317. 

Counterfactual explanation

• Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech., 31, 841. 
• Ustun, B., Spangher, A., & Liu, Y. (2019). Actionable recourse in linear classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency(pp. 10-19). ACM. https://arxiv.org/pdf/1809.06514.pdf
• Evaluate recourse in linear classification models in python: https://github.com/ustunb/actionable-recourse

Secondary explainers and attention-based systems

• Li, O., Liu, H., Chen, C., & Rudin, C. (2018). Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions. In Thirty-Second AAAI Conference on Artificial Intelligence. 
• Park, D. H., Hendricks, L. A., Akata, Z., Schiele, B., Darrell, T., & Rohrbach, M. (2016). Attentive explanations: Justifying decisions and pointing to the evidence. arXiv preprint arXiv:1612.04757. 

Other resources for supplementary explanation

• IBM’s Explainability 360
• Biecek, B., & Burzykowski, T. (2019). Predictive Models: Explore, Explain, and Debug, Human-Centered Interpretable Machine Learning. 
• Accompanying software, Dalex, Descriptive mAchine Learning Explanations
• Przemysław Biecek, Interesting resources related to XAI
• Christoph Molnar, iml: Interpretable machine learning