Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Lessons on interpretable machine learning from particle physics

Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods commonly used in particle physics.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

Fig. 1: 12-variable machine learning-assisted analysis for classifying five particle-production channels.


  1. Guest, D., Cranmer, K. & Whiteson, D. Deep Learning and its Application to LHC Physics. Ann. Rev. Nucl. Part. Sci. 68, 161–181 (2018).

    Article  ADS  Google Scholar 

  2. Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R. & Yu, B. Definitions, methods, and applications in interpretable machine learning. PNAS 116, 22071–22080 (2019).

    Article  MathSciNet  Google Scholar 

  3. Barredo Arrieta, A. et al. Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020).

    Article  Google Scholar 

  4. Hamon, R., Junklewitz, H. & Sanchez, I. Robustness and explainability of Artificial Intelligence. Publ. Off. Eur. Union, Luxembourg (2020).

  5. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article  Google Scholar 

  6. Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD ‘16) 785–794 (ACM, 2016).

  7. Fawagreh, K., Gaber, M. M. & Elyan, E. Random forests: from early developments to recent advancements. Syst. Sci. Control. Eng. 2, 602–609 (2014).

    Article  Google Scholar 

  8. Ribeiro, M. T., Singh, S. & Guestrin, S. “Why should I trust you?”: Explaining the predictions of any classifier. In Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD ‘16) 1135–1144 (ACM, 2016).

  9. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    Article  Google Scholar 

  10. Grojean, C., Paul, A. & Qian, Z. Resurrecting \(b\bar{b}h\) with kinematic shapes. J. High Energy Phys. 4, 139 (2021).

    Article  ADS  Google Scholar 

Download references


This work benefited from support by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy EXC 2121 “Quantum Universe”–390833306. The work of A.P. is funded by Volkswagen Foundation within the initiative “Corona Crisis and Beyond–Perspectives for Science, Scholarship and Society”. I.S. is grateful to the Norwegian Research Council for support through the EXAIGON project–Explainable AI systems for gradual industry adoption (grant no. 304843).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ayan Paul.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grojean, C., Paul, A., Qian, Z. et al. Lessons on interpretable machine learning from particle physics. Nat Rev Phys 4, 284–286 (2022).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing