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Most research efforts in machine learning focus on performance and are detached from an explanation of the behaviour of the model. We call for going back to basics of machine learning
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* Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support REFERENCES * Cooper, A. F., Moss, E., Laufer, B. & Nissenbaum, H. In _2022 ACM Conf.
Fairness, Accountability, and Transparency_ 864–876 (2022). * Molnar, C., Casalicchio, G. & Bischl, B. In _Joint European Conf. Machine Learning and Knowledge Discovery in Databases_
417–431 (2020). * Minh, D., Wang, H. X., Li, Y. F. & Nguyen, T. N. _Artif. Intell. Rev._ 55, 3503–3568 (2021). Article Google Scholar * Arrieta, A. B. et al. _Inform. Fusion_ 58,
82–115 (2020). Article Google Scholar * Sculley, D., Snoek, J., Wiltschko, A. B. & Rahimi, A. In _6th Int. Conf. Learning Representations_ (ICLR, 2018). * Vapnik, V. _The Nature of
Statistical Learning Theory_ (Springer, 2000). * Lindsey, J. K. _Applying Generalized Linear Models_ (Springer Science & Business Media, 2000). * Raissi, M., Perdikaris, P. &
Karniadakis, G. E. _J. Comput. Phys._ 378, 686–707 (2019). Article MathSciNet Google Scholar * Wu, Z. et al. _IEEE Trans. Neural Netw. Learn. Syst._ 32, 4–24 (2020). Article Google
Scholar Download references ACKNOWLEDGEMENTS D.M. was funded by grants 22/06211-2 and 23/00256-7, São Paulo Research Foundation (FAPESP). J.B. was funded by grants 14/50937-1 and
2020/06950-4, São Paulo Research Foundation (FAPESP). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Computer Science, Institute of Mathematics and Statistics, University of São
Paulo, São Paulo, Brazil Diego Marcondes & Junior Barrera * Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA Diego Marcondes *
Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil Adilson Simonis Authors * Diego Marcondes View author publications You can also
search for this author inPubMed Google Scholar * Adilson Simonis View author publications You can also search for this author inPubMed Google Scholar * Junior Barrera View author
publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Diego Marcondes. ETHICS DECLARATIONS COMPETING INTERESTS None of the authors
have conflict of interest or competing interests pertaining to this work. PEER REVIEW PEER REVIEW INFORMATION _Nature Machine Intelligence_ thanks Yiqun Chen for their contribution to the
peer review of this work. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Marcondes, D., Simonis, A. & Barrera, J. Back to basics to open the black
box. _Nat Mach Intell_ 6, 498–501 (2024). https://doi.org/10.1038/s42256-024-00842-6 Download citation * Published: 17 May 2024 * Issue Date: May 2024 * DOI:
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