{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T19:24:53Z","timestamp":1785439493198,"version":"3.56.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T00:00:00Z","timestamp":1687219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Minds &amp; Machines"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11023-023-09639-9","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T13:02:23Z","timestamp":1687266143000},"page":"429-450","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5745-9838","authenticated-orcid":false,"given":"Dan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,20]]},"reference":[{"key":"9639_CR1","unstructured":"Beucler, T., Pritchard, M., Gentine, P. and Stephan R (2020). Towards physically-consistent, data-driven models of convection.\u00a0ArXiv:2002.08525 [Physics], April. http:\/\/arxiv.org\/abs\/2002.08525."},{"key":"9639_CR2","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-030-37599-7_20","volume-title":"Machine learning optimization, and data science.\u00a0Lecture notes in computer science","author":"G Bontempi","year":"2019","unstructured":"Bontempi, G. (2019). The induction problem: A machine learning vindication argument. In N. Giuseppe, P. Panos, U. Renato, G. Giovanni, & S. Vincenzo (Eds.), Machine learning optimization, and data science.\u00a0Lecture notes in computer science (pp. 232\u201343). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-37599-7_20"},{"issue":"3","key":"9639_CR3","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1214\/ss\/1009213726","volume":"16","author":"L Breiman","year":"2001","unstructured":"Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199\u2013231.","journal-title":"Statistical Science"},{"key":"9639_CR4","volume-title":"The philosophy of Francis Bacon: An address delivered at Cambridge on the occasion of the Bacon Tercentenary","author":"CD Broad","year":"1926","unstructured":"Broad, C. D. (1926). The philosophy of Francis Bacon: An address delivered at Cambridge on the occasion of the Bacon Tercentenary. Cambridge University Press."},{"key":"9639_CR5","unstructured":"Buolamwini, J. (2018). and Timnit Gebru. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency, (pp. 77\u201391). PMLR. http:\/\/proceedings.mlr.press\/v81\/buolamwini18a.html."},{"key":"9639_CR6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2011.03395","author":"A D\u2019Amour","year":"2020","unstructured":"D\u2019Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., & Chen, C. (2020). Underspecification presents challenges for credibility in modern machine learning. The Journal of Machine Learning Research. https:\/\/doi.org\/10.48550\/arXiv.2011.03395","journal-title":"The Journal of Machine Learning Research"},{"issue":"10","key":"9639_CR30","doi-asserted-by":"publisher","first-page":"3999","DOI":"10.5194\/gmd-11-3999-2018","volume":"11","author":"PD Dueben","year":"2018","unstructured":"Dueben, P. D., & Bauer, P. (2018). Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development, 11(10), 3999\u20134009. https:\/\/doi.org\/10.5194\/gmd-11-3999-2018.","journal-title":"Geoscientific Model Development"},{"key":"9639_CR7","doi-asserted-by":"publisher","DOI":"10.7208\/chicago\/9780226292144.001.0001","volume-title":"Scientific perspectivism","author":"RN Giere","year":"2006","unstructured":"Giere, R. N. (2006). Scientific perspectivism. University of Chicago Press."},{"key":"9639_CR8","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-3-642-21329-8_4","volume-title":"Probability in physics.\u00a0The frontiers collection","author":"S Goldstein","year":"2012","unstructured":"Goldstein, S. (2012). Typicality and notions of probability in physics. In Yemima Ben-Menahem & Meir Hemmo (Eds.), Probability in physics.\u00a0The frontiers collection (pp. 59\u201371). Springer. https:\/\/doi.org\/10.1007\/978-3-642-21329-8_4"},{"key":"9639_CR9","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., & Courville, Aaron. (2016). Deep learning. MIT Press."},{"key":"9639_CR10","volume-title":"Fact, fiction, and forecast","author":"N Goodman","year":"1965","unstructured":"Goodman, N. (1955). Fact, fiction, and forecast. Harvard University Press."},{"issue":"2","key":"9639_CR11","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/BF00766728","volume":"26","author":"R Gottlob","year":"1995","unstructured":"Gottlob, R. (1995). Emeralds are no chameleons: Why \u2018Grue\u2019 is not projectible for induction. Journal for General Philosophy of Science \/ Zeitschrift F\u00fcr Allgemeine Wissenschaftstheorie, 26(2), 259\u2013268.","journal-title":"Journal for General Philosophy of Science \/ Zeitschrift F\u00fcr Allgemeine Wissenschaftstheorie"},{"key":"9639_CR12","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1906.01563","author":"S Greydanus","year":"2019","unstructured":"Greydanus, S., Dzamba, M., & Yosinski, Jason. (2019). Hamiltonian neural networks. Advances in Neural Information Processing Systems. https:\/\/doi.org\/10.48550\/arXiv.1906.01563","journal-title":"Advances in Neural Information Processing Systems"},{"key":"9639_CR13","unstructured":"Heaven, W. D. (2020). \u201cThe Way We Train AI Is Fundamentally Flawed.\u201d MIT Technology Review, November 18, 2020."},{"issue":"1","key":"9639_CR14","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1146\/annurev.energy.25.1.441","volume":"25","author":"IM Held","year":"2000","unstructured":"Held, I. M., Brian, J., & Soden (2000). Water Vapor Feedback and global warming. Annual Review of Energy and the Environment, 25(1), 441\u2013475. https:\/\/doi.org\/10.1146\/annurev.energy.25.1.441.","journal-title":"Annual Review of Energy and the Environment"},{"key":"9639_CR15","volume-title":"The stanford encyclopedia of philosophy.\u00a0Metaphysics research lab","author":"L Henderson","year":"2020","unstructured":"Henderson, L. (2020). The problem of induction. In N. Z. Edward (Ed.), The stanford encyclopedia of philosophy.\u00a0Metaphysics research lab. Metaphysics Research Lab: Stanford University."},{"issue":"6245","key":"9639_CR16","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255\u2013260. https:\/\/doi.org\/10.1126\/science.aaa8415.","journal-title":"Science"},{"issue":"10","key":"9639_CR32","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1109\/TKDE.2017.2720168","volume":"29","author":"A Karpatne","year":"2017","unstructured":"Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V.  (2017). Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on Knowledge and Data Engineering, 29(10), 2318\u20132331. https:\/\/doi.org\/10.1109\/TKDE.2017.2720168.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"9639_CR17","doi-asserted-by":"publisher","DOI":"10.1086\/714877","author":"S Kawamleh","year":"2021","unstructured":"Kawamleh, S. (2021). Can machines learn how clouds work?: The epistemic implications of machine learning methods in climate science. Philosophy of Science. https:\/\/doi.org\/10.1086\/714877","journal-title":"Philosophy of Science"},{"issue":"4","key":"9639_CR18","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1093\/bjps\/52.4.761","volume":"52","author":"IA Kiesepp\u00e4","year":"2001","unstructured":"Kiesepp\u00e4, I. A. (2001). Statistical model selection criteria and the philosophical problem of Underdetermination. The British Journal for the Philosophy of Science, 52(4), 761\u2013794. https:\/\/doi.org\/10.1093\/bjps\/52.4.761.","journal-title":"The British Journal for the Philosophy of Science"},{"key":"9639_CR19","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/978-3-030-37591-1_9","volume-title":"Guide to deep learning basics logical, historical and philosophical perspectives","author":"D Lauc","year":"2020","unstructured":"Lauc, D. (2020). Machine learning and the philosophical problems of induction. In Sandro Skansi (Ed.), Guide to deep learning basics logical, historical and philosophical perspectives (pp. 93\u2013106). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-030-37591-1_9"},{"issue":"1","key":"9639_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1175\/1520-0469(1975)032<0003:TEODTC>2.0.CO;2","volume":"32","author":"S Manabe","year":"1975","unstructured":"Manabe, S., & Wetherald, R. T. (1975). The Effects of doubling the CO2 concentration on the climate of a general circulation model. Journal of the Atmospheric Sciences, 32(1), 3\u201315.","journal-title":"Journal of the Atmospheric Sciences"},{"key":"9639_CR31","unstructured":"Mitchell, T. M. (1980). The need for biases in learning generalizations. Rutgers Computer Science Tech. Rept. (Computer Science Department, Rutgers University)."},{"key":"9639_CR21","volume-title":"Machine learning","author":"TM Mitchell","year":"1997","unstructured":"Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hill. McGraw-Hill Series in Computer Science."},{"issue":"4","key":"9639_CR22","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1086\/378858","volume":"70","author":"JD Norton","year":"2003","unstructured":"Norton, J. D. (2003). A material theory of induction. Philosophy of Science, 70(4), 647\u2013670. https:\/\/doi.org\/10.1086\/378858.","journal-title":"Philosophy of Science"},{"issue":"2","key":"9639_CR23","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s11229-004-6261-z","volume":"150","author":"John D Norton","year":"2006","unstructured":"Norton, John D. (2006). How the formal equivalence of grue and green defeats what is new in the new riddle of induction. Synthese, 150(2), 185\u2013207. https:\/\/doi.org\/10.1007\/s11229-004-6261-z","journal-title":"Synthese"},{"issue":"39","key":"9639_CR24","doi-asserted-by":"publisher","first-page":"9684","DOI":"10.1073\/pnas.1810286115","volume":"115","author":"S Rasp","year":"2018","unstructured":"Rasp, S., Pritchard, M. S., & Pierre, G. (2018). Deep learning to represent subgrid processes in climate models. Proceedings of the National Academy of Sciences, 115(39), 9684\u201389. https:\/\/doi.org\/10.1073\/pnas.1810286115","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"9639_CR25","unstructured":"Reichenbach, H. (1938). \u201cExperience and prediction: An analysis of the foundations and the structure of knowledge.\u201d 1938. https:\/\/philarchive.org\/rec\/REIEAP-2."},{"issue":"5","key":"9639_CR33","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"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\u2013215. https:\/\/doi.org\/10.1038\/s42256-019-0048-x","journal-title":"Nature Machine Intelligence"},{"issue":"1","key":"9639_CR26","doi-asserted-by":"publisher","first-page":"39","DOI":"10.2307\/2104014","volume":"14","author":"WC Salmon","year":"1953","unstructured":"Salmon, W. C. (1953). The uniformity of Nature. Philosophy and Phenomenological Research, 14(1), 39\u201348. https:\/\/doi.org\/10.2307\/2104014.","journal-title":"Philosophy and Phenomenological Research"},{"key":"9639_CR27","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/11964.001.0001","volume-title":"Hume\u2019s problem solved: The optimality of meta-induction","author":"G Schurz","year":"2019","unstructured":"Schurz, G. (2019). Hume\u2019s problem solved: The optimality of meta-induction. MIT Press."},{"key":"9639_CR28","volume-title":"The stanford encyclopedia of philosophy.\u00a0Metaphysics research lab","author":"K Stanford","year":"2017","unstructured":"Stanford, K. (2017). Underdetermination of scientific theory. In N. Z. Edward (Ed.), The stanford encyclopedia of philosophy.\u00a0Metaphysics research lab. Stanford University."},{"issue":"7","key":"9639_CR29","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1162\/neco.1996.8.7.1341","volume":"8","author":"DH Wolpert","year":"1996","unstructured":"Wolpert, D. H. (1996). The lack of a priori distinctions between learning algorithms. Neural Computation, 8(7), 1341\u20131390. https:\/\/doi.org\/10.1162\/neco.1996.8.7.1341","journal-title":"Neural Computation"}],"container-title":["Minds and Machines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11023-023-09639-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11023-023-09639-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11023-023-09639-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T19:02:34Z","timestamp":1694977354000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11023-023-09639-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,20]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["9639"],"URL":"https:\/\/doi.org\/10.1007\/s11023-023-09639-9","relation":{},"ISSN":["0924-6495","1572-8641"],"issn-type":[{"value":"0924-6495","type":"print"},{"value":"1572-8641","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,20]]},"assertion":[{"value":"16 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}]}}