Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations (2109.07557v3)

Published 15 Sep 2021 in cs.LG

Abstract: This work presents CounterNet, a novel end-to-end learning framework which integrates Machine Learning (ML) model training and the generation of corresponding counterfactual (CF) explanations into a single end-to-end pipeline. Counterfactual explanations offer a contrastive case, i.e., they attempt to find the smallest modification to the feature values of an instance that changes the prediction of the ML model on that instance to a predefined output. Prior techniques for generating CF explanations suffer from two major limitations: (i) all of them are post-hoc methods designed for use with proprietary ML models -- as a result, their procedure for generating CF explanations is uninformed by the training of the ML model, which leads to misalignment between model predictions and explanations; and (ii) most of them rely on solving separate time-intensive optimization problems to find CF explanations for each input data point (which negatively impacts their runtime). This work makes a novel departure from the prevalent post-hoc paradigm (of generating CF explanations) by presenting CounterNet, an end-to-end learning framework which integrates predictive model training and the generation of counterfactual (CF) explanations into a single pipeline. Unlike post-hoc methods, CounterNet enables the optimization of the CF explanation generation only once together with the predictive model. We adopt a block-wise coordinate descent procedure which helps in effectively training CounterNet's network. Our extensive experiments on multiple real-world datasets show that CounterNet generates high-quality predictions, and consistently achieves 100% CF validity and low proximity scores (thereby achieving a well-balanced cost-invalidity trade-off) for any new input instance, and runs 3X faster than existing state-of-the-art baselines.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (64)
  1. Sushant Agarwal. 2020. Trade-Offs between Fairness and Interpretability in Machine Learning. Proc. 3rd International Workshop on AI for Social Good (2020).
  2. Impact of Response Latency on User Behaviour in Mobile Web Search. In Proceedings of the 2021 Conference on Human Information Interaction and Retrieval. 279–283.
  3. Arthur Asuncion and David Newman. 2007. UCI machine learning repository.
  4. Preserving Fine-Grain Feature Information in Classification via Entropic Regularization. arXiv preprint arXiv:2208.03684 (2022).
  5. Explainable Machine Learning in Deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20). Association for Computing Machinery, New York, NY, USA, 648–657. https://doi.org/10.1145/3351095.3375624
  6. ’It’s Reducing a Human Being to a Percentage’ Perceptions of Justice in Algorithmic Decisions. In Proceedings of the 2018 Chi conference on human factors in computing systems. 1–14.
  7. Jock Blackard. 1998. Covertype. UCI Machine Learning Repository.
  8. Catherine Blake. 1998. UCI repository of machine learning databases. http://www. ics. uci. edu/~ mlearn/MLRepository. html (1998).
  9. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 1721–1730.
  10. This looks like that: deep learning for interpretable image recognition. In Advances in neural information processing systems. 8930–8941.
  11. Paulo Cortez and Alice Maria Gonçalves Silva. 2008. Using data mining to predict secondary school student performance. (2008).
  12. Explanations Based on the Missing: Towards Contrastive Explanations with Pertinent Negatives. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (Montréal, Canada) (NIPS’18). Curran Associates Inc., Red Hook, NY, USA, 590–601.
  13. FICO. 2018. Explainable Machine Learning Challenge. https://community.fico.com/s/explainable-machine-learning-challenge.
  14. Robust loss functions under label noise for deep neural networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 31.
  15. Explaining and Harnessing Adversarial Examples. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6572
  16. A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51, 5 (2018), 1–42.
  17. VCNet: A self-explaining model for realistic counterfactual generation. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).
  18. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
  19. Matthias Hein and Maksym Andriushchenko. 2017. Formal guarantees on the robustness of a classifier against adversarial manipulation. Advances in neural information processing systems 30 (2017).
  20. Like Hui and Mikhail Belkin. 2021. Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks. In International Conference on Learning Representations. https://openreview.net/forum?id=hsFN92eQEla
  21. Towards realistic individual recourse and actionable explanations in black-box decision making systems. arXiv preprint arXiv:1907.09615 (2019).
  22. Kaggle. 2018. Titanic - Machine Learning from Disaster. https://www.kaggle.com/c/titanic/overview.
  23. A survey of algorithmic recourse: definitions, formulations, solutions, and prospects. arXiv preprint arXiv:2010.04050 (2020).
  24. Algorithmic recourse: from counterfactual explanations to interventions. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 353–362.
  25. Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In International Conference on Machine Learning. PMLR, 1885–1894.
  26. R Kohavi and B Becker. 1996. UCI Machine Learning Repository: Adult Data Set.
  27. Open university learning analytics dataset. Scientific data 4 (2017), 170171.
  28. 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. 1675–1684.
  29. Accurate intelligible models with pairwise interactions. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 623–631.
  30. Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in neural information processing systems. 4765–4774.
  31. Towards Deep Learning Models Resistant to Adversarial Attacks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=rJzIBfZAb
  32. Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277 (2019).
  33. Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267 (2019), 1–38.
  34. Interpretable machine learning–a brief history, state-of-the-art and challenges. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 417–431.
  35. Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 607–617.
  36. Interpretable machine learning: definitions, methods, and applications. arXiv preprint arXiv:1901.04592 (2019).
  37. CounteRGAN: Generating counterfactuals for real-time recourse and interpretability using residual GANs. In Uncertainty in Artificial Intelligence. PMLR, 1488–1497.
  38. Learning model-agnostic counterfactual explanations for tabular data. In Proceedings of The Web Conference 2020. 3126–3132.
  39. Manipulating and Measuring Model Interpretability. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3411764.3445315
  40. Can I Still Trust You?: Understanding the Impact of Distribution Shifts on Algorithmic Recourses. arXiv preprint arXiv:2012.11788 (2020).
  41. ” 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. 1135–1144.
  42. Beyond trivial counterfactual explanations with diverse valuable explanations. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1056–1065.
  43. Learning Models for Actionable Recourse. Advances in Neural Information Processing Systems 34 (2021).
  44. Cynthia Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence 1, 5 (2019), 206–215.
  45. Generating interpretable counterfactual explanations by implicit minimisation of epistemic and aleatoric uncertainties. In International Conference on Artificial Intelligence and Statistics. PMLR, 1756–1764.
  46. Hydra: Pruning adversarially robust neural networks. Advances in Neural Information Processing Systems 33 (2020), 19655–19666.
  47. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision. 618–626.
  48. Explanation by Progressive Exaggeration. In International Conference on Learning Representations. https://openreview.net/forum?id=H1xFWgrFPS
  49. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017).
  50. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929–1958.
  51. A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence. IEEE Access 9 (2021), 11974–12001.
  52. Axiomatic attribution for deep networks. In International Conference on Machine Learning. PMLR, 3319–3328.
  53. Towards Robust and Reliable Algorithmic Recourse. Advances in Neural Information Processing Systems 34 (2021).
  54. Actionable recourse in linear classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency. 10–19.
  55. Arnaud Van Looveren and Janis Klaise. 2019. Interpretable counterfactual explanations guided by prototypes. arXiv preprint arXiv:1907.02584 (2019).
  56. Counterfactual Explanations for Machine Learning: A Review. arXiv preprint arXiv:2010.10596 (2020).
  57. Amortized generation of sequential algorithmic recourses for black-box models. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 8512–8519.
  58. On the fairness of causal algorithmic recourse. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 9584–9594.
  59. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech. 31 (2017), 841.
  60. Do Wider Neural Networks Really Help Adversarial Robustness? Advances in Neural Information Processing Systems 34 (2021).
  61. Empirical evaluation of rectified activations in convolutional network. arXiv preprint arXiv:1505.00853 (2015).
  62. Model-Based Counterfactual Synthesizer for Interpretation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Virtual Event, Singapore) (KDD ’21). Association for Computing Machinery, New York, NY, USA, 1964–1974. https://doi.org/10.1145/3447548.3467333
  63. I-Cheng Yeh and Che-hui Lien. 2009. The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications 36, 2 (2009), 2473–2480.
  64. Leveraging program analysis to reduce user-perceived latency in mobile applications. In Proceedings of the 40th International Conference on Software Engineering. 176–186.
Citations (12)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now