Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

How to address monotonicity for model risk management? (2305.00799v2)

Published 28 Apr 2023 in cs.LG and q-fin.CP

Abstract: In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity. Although there have been numerous studies on individual monotonicity, pairwise monotonicity is often overlooked in the existing literature. This paper studies transparent neural networks in the presence of three types of monotonicity: individual monotonicity, weak pairwise monotonicity, and strong pairwise monotonicity. As a means of achieving monotonicity while maintaining transparency, we propose the monotonic groves of neural additive models. As a result of empirical examples, we demonstrate that monotonicity is often violated in practice and that monotonic groves of neural additive models are transparent, accountable, and fair.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. Propublica. compas data and analysis for “machine bias”., 2016. URL https://github.com/propublica/compas-analysis.
  2. European union’s artificial intelligence act, 2021. URL https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206.
  3. Model risk management, 2021. URL https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/index-model-risk-management.html.
  4. Neural additive models: Interpretable machine learning with neural nets. Advances in Neural Information Processing Systems, 34, 2021.
  5. Survival analysis of heart failure patients: A case study. PloS one, 12(7):e0181001, 2017.
  6. Machine bias. In Ethics of Data and Analytics, pp.  254–264. Auerbach Publications, 2016.
  7. Conjoint measurement tools for mcdm: A brief introduction. Multiple criteria decision analysis: State of the art surveys, pp.  97–151, 2016.
  8. Ai regulation is coming. Harvard Business Review, 2021. URL https://hbr.org/2021/09/ai-regulation-is-coming.
  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, pp.  1721–1730, 2015.
  10. Monotonic neural additive models: Pursuing regulated machine learning models for credit scoring. In Proceedings of the Third ACM International Conference on AI in Finance, pp.  70–78, 2022.
  11. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp.  785–794, 2016.
  12. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC medical informatics and decision making, 20(1):1–16, 2020.
  13. The accuracy, fairness, and limits of predicting recidivism. Science advances, 4(1):eaao5580, 2018.
  14. Nearest neighbour classification with monotonicity constraints. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I 19, pp.  301–316. Springer, 2008.
  15. An intersectional definition of fairness. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp.  1918–1921. IEEE, 2020.
  16. Monotone decomposition of 2-additive generalized additive independence models. Mathematical Social Sciences, 92:64–73, 2018.
  17. Multidimensional shape constraints. In International Conference on Machine Learning, pp. 3918–3928. PMLR, 2020.
  18. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29, 2016.
  19. Hastie, T. J. Generalized additive models. In Statistical models in S, pp.  249–307. Routledge, 2017.
  20. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.  770–778, 2016.
  21. Explaining explanations: Axiomatic feature interactions for deep networks. Journal of Machine Learning Research, 22(104):1–54, 2021.
  22. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International Conference on Machine Learning, pp. 2564–2572. PMLR, 2018.
  23. An empirical study of rich subgroup fairness for machine learning. In Proceedings of the conference on fairness, accountability, and transparency, pp.  100–109, 2019.
  24. Certified monotonic neural networks. Advances in Neural Information Processing Systems, 33:15427–15438, 2020.
  25. Intelligible models for classification and regression. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  150–158, 2012.
  26. Accurate intelligible models with pairwise interactions. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  623–631, 2013.
  27. Fast and flexible monotonic functions with ensembles of lattices. Advances in neural information processing systems, 29, 2016.
  28. Classification trees for problems with monotonicity constraints. ACM SIGKDD Explorations Newsletter, 4(1):1–10, 2002.
  29. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.
  30. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206–215, 2019.
  31. Detecting statistical interactions with additive groves of trees. In Proceedings of the 25th international conference on Machine learning, pp.  1000–1007, 2008.
  32. Distill-and-compare: Auditing black-box models using transparent model distillation. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp.  303–310, 2018.
  33. Detecting statistical interactions from neural network weights. In International Conference on Learning Representations, 2018a. URL https://openreview.net/forum?id=ByOfBggRZ.
  34. Neural interaction transparency (nit): Disentangling learned interactions for improved interpretability. Advances in Neural Information Processing Systems, 31, 2018b.
  35. How does this interaction affect me? interpretable attribution for feature interactions. Advances in neural information processing systems, 33:6147–6159, 2020.
  36. Tshitoyan, V. Simple neural network, 2023. URL https://github.com/vtshitoyan/simpleNN.
  37. Hierarchical lattice layer for partially monotone neural networks. In Advances in Neural Information Processing Systems, 2022.
  38. Gami-net: An explainable neural network based on generalized additive models with structured interactions. Pattern Recognition, 120:108192, 2021.
  39. Deep lattice networks and partial monotonic functions. Advances in neural information processing systems, 30, 2017.
Citations (4)
View on Semantic Scholar

Summary

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

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