{"id":1316,"date":"2026-04-09T08:47:45","date_gmt":"2026-04-09T08:47:45","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/1316\/"},"modified":"2026-04-09T08:47:45","modified_gmt":"2026-04-09T08:47:45","slug":"explainable-ai-needs-formalization-npj-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/1316\/","title":{"rendered":"Explainable AI needs formalization | npj Artificial Intelligence"},"content":{"rendered":"

European Commission. Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=celex%3A52021PC0206<\/a> (2021).<\/p>\n

Bishop, C. M. & Nasrabadi, N. M. Pattern recognition and machine learning, vol. 4 (Springer, 2006).<\/p>\n

Tjoa, E. & Guan, C. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Transactions on Neural Networks and Learning Systems Vol. 32 4793-4813 https:\/\/doi.org\/10.1109\/TNNLS.2020.3027314<\/a> (2021).<\/p>\n

Minh, D., Wang, H. X., Li, Y. F. & Nguyen, T. N. Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review 55, 3503\u20133568 https:\/\/doi.org\/10.1007\/s10462-021-10088-y<\/a> (2022).<\/p>\n

Miller, T. Explanation in artificial intelligence: Insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Saporta, A. et al. Benchmarking saliency methods for chest X-ray interpretation. Nat. Mach. Intell. 4, 867\u2013878 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ribeiro, M. T., Singh, S. & Guestrin, C. \u201c Why should I trust you?\u201d Explaining the predictions of any classifier. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135\u20131144 (ACM, 2016).<\/p>\n

Lapuschkin, S. et al. Unmasking Clever Hans predictors and assessing what machines really learn. Nat. Commun. 10, 1096 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Anders, C. J. et al. Finding and removing Clever Hans: using explanation methods to debug and improve deep models. Inf. Fusion 77, 261\u2013295 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, Z. J. et al. Interpretability, then what? editing machine learning models to reflect human knowledge and values. In Proc. of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 4132\u20134142 (2022).<\/p>\n

Samek, W. & M\u00fcller, K.-R. Towards explainable artificial intelligence. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Vol. 11700, 5\u201322 (Springer Nature, 2019).<\/p>\n

Jim\u00e9nez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573\u2013584 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tideman, L. E. et al. Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized shapley additive explanations. Anal. Chim. Acta 1177, 338522 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Watson, D. S. Interpretable machine learning for genomics. Hum. Genet. 141, 1499\u20131513 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wong, F. et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 626, 177\u2013185 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ustun, B., Spangher, A. & Liu, Y. Actionable recourse in linear classification. In Proc. Conference on Fairness, Accountability, and Transparency, 10\u201319 (ACM, 2019).<\/p>\n

Ates, E., Aksar, B., Leung, V. J. & Coskun, A. K. Counterfactual explanations for multivariate time series. In Proc. International Conference on Applied Artificial Intelligence (ICAPAI), 1\u20138 (IEEE, 2021).<\/p>\n

Wilming, R., Budding, C., M\u00fcller, K.-R. & Haufe, S. Scrutinizing XAI using linear ground-truth data with suppressor variables. Machine Learning, Special Issue of the ECML PKDD 2022 Journal Track, 1\u201321 (Springer Nature, 2022).<\/p>\n

Wilming, R. et al. GECOBench: a gender-controlled text dataset and benchmark for quantifying biases in explanations. Front. Artif. Intell. https:\/\/arxiv.org\/abs\/2406.11547<\/a> (in the press). (2024).<\/p>\n

Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96\u2013110 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kindermans, P.-J. et al. Learning how to explain neural networks: patternNet and patternAttribution. In 6th International Conference on Learning Representations (ICLR, 2018).<\/p>\n

Wilming, R., Kieslich, L., Clark, B. & Haufe, S. Theoretical behavior of XAI methods in the presence of suppressor variables. Proc. 40th Int. Conf. Mach. Learn. 202, 37091\u201337107 (2023).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Conger, A. J. A revised definition for suppressor variables: a guide to their identification and interpretation. Educ. Psychol. Meas. 34, 35\u201346 (1974).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pearl, J. Causality (Cambridge University Press, 2009).<\/p>\n

Clark, B., Wilming, R. & Haufe, S. XAI-TRIS: non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance. Mach. Learn. 113, 6871\u20136910 (2024).<\/p>\n

Baehrens, D. et al. How to explain individual classification decisions. J. Mach. Learn. Res. 11, 1803\u20131831 (2010).<\/p>\n

MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10, 1\u201346 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Montavon, G., Bach, S., Binder, A., Samek, W. & M\u00fcller, K.-R. Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. 65, 211\u2013222 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shapley, L. S. A value for n-person games. Contrib. Theory Games 2, 307\u2013317 (1953).<\/p>\n

MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lundberg, S. M. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. In Guyon, I. et al. (eds.) Advances in Neural Information Processing Systems 30, Vol. 30, 4765\u20134774 (Curran Associates, Inc., 2017).<\/p>\n

Aas, K., Jullum, M. & L\u00f8land, A. Explaining individual predictions when features are dependent: more accurate approximations to shapley values. Artif. Intell. 298, 103502 (2021).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In ICML, Vol. 70 of Proc. Machine Learning Research (eds. Precup, D. & Teh, Y. W.) 3319\u20133328 (PMLR, 2017).<\/p>\n

Wachter, S., Mittelstadt, B. & Russell, C. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL Tech. 31, 841 (2017).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Guidotti, R. et al. A survey of methods for explaining black box models. ACM Comput. Surv. 51, 1\u201342 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jacovi, A. & Goldberg, Y. Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness? in Proc. 58th Annual Meeting of the Association for Computational Linguistics, 4198\u20134205 (Association for Computational Linguistics, Online, 2020).<\/p>\n

Weichwald, S. et al. Causal interpretation rules for encoding and decoding models in neuroimaging. Neuroimage 110, 48\u201359 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Karimi, A.-H., Sch\u00f6lkopf, B. & Valera, I. Algorithmic recourse: from counterfactual explanations to interventions. In Proc. ACM Conference on Fairness, Accountability, and Transparency, 353\u2013362 (ACM, 2021).<\/p>\n

Caruana, R. et al. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proc. 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1721\u20131730 (ACM, 2015).<\/p>\n

Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206\u2013215 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rai, A. Explainable AI: Ffrom black box to glass box. J. Acad. Mark. Sci. 48, 137\u2013141 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Clark, B. et al. Correcting misinterpretations of additive models. in Proc. 39th Annual Conference on Neural Information Processing Systems (NeurIPS, 2025).<\/p>\n

Shmueli, G. To explain or to predict? Stat. Sci. 25, 289\u2013310 (2010).<\/p>\n

Del Giudice, M. The prediction-explanation fallacy: a pervasive problem in scientific applications of machine learning. Methodology 20, 22\u201346 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Doshi-Velez, F. & Kim, B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv: https:\/\/arxiv.org\/abs\/1702.08608<\/a> (2017).<\/p>\n

Hedstr\u00f6m, A. et al. Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond. J. Mach. Learn. Res. 24, 1\u201311 (2023).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Meinshausen, N. & B\u00fchlmann, P. Stability selection. J. R. Stat. Soc. Ser. B Stat. Methodol. 72, 417\u2013473 (2010).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Samek, W., Binder, A., Montavon, G., Lapuschkin, S. & M\u00fcller, K. Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28, 2660\u20132673 (2017).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hooker, S., Erhan, D., Kindermans, P.-J. & Kim, B. A benchmark for interpretability methods in deep neural networks. in Advances in Neural Information Processing Systems, Vol. 32, (eds. Wallach, H. et al.) 9737\u20139748 (Curran Associates, Inc., 2019).<\/p>\n

Rong, Y., Leemann, T., Borisov, V., Kasneci, G., & Kasneci, E. A Consistent and Efficient Evaluation Strategy for Attribution Methods. In International Conference on Machine Learning 18770\u201318795 (PMLR, 2022).<\/p>\n

Bl\u00fccher, S., Vielhaben, J. & Strodthoff, N. Preddiff: Explanations and interactions from conditional expectations. Artif. Intell. 312, 103774 (2022).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Adebayo, J. et al. Sanity checks for saliency maps. in Proc. 32nd International Conference on Neural Information Processing Systems, Vol. 31, 9525\u20139536 (Curran Associates Inc., 2018).<\/p>\n

Holzinger, A., Langs, G., Denk, H., Zatloukal, K. & M\u00fcller, H. Causability and explainability of artificial intelligence in medicine. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9, e1312 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Biessmann, F. & Refiano, D. Quality metrics for transparent machine learning with and without humans in the loop are not correlated. in Proc ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. https:\/\/arxiv.org\/abs\/2107.02033<\/a> (2021).<\/p>\n

Jesus, S. et al. How can i choose an explainer? An application-grounded evaluation of post-hoc explanations. in Proc. ACM conference on fairness, accountability, and transparency, 805\u2013815 (ACM, 2021).<\/p>\n

Bu\u00e7inca, Z., Lin, P., Gajos, K. Z. & Glassman, E. L. Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems. In Proc. 25th International Conference on Intelligent User Interfaces, 454\u2013464 (ACM, 2020).<\/p>\n

Bansal, G. et al. Does the whole exceed its parts? The effect of AI explanations on complementary team performance. in Proc. CHI Conference on Human Factors in Computing Systems, 1\u201316 (ACM, 2021).<\/p>\n

Trout, J. D. Scientific explanation and the sense of understanding. Philos. Sci. 69, 212\u2013233 (2002).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Oala, L. et al. Machine learning for health: algorithm auditing & quality control. J. Med. Syst. 45, 1\u20138 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

DIN SPEC 92001-3:2023-04. Artificial Intelligence\u2014Life Cycle Processes and Quality Requirements\u2014Part 3: Explainability (DIN Deutsches Institut f\u00fcr Normung e. V., 2023).<\/p>\n

Sokol, K. & Flach, P. Explainability fact sheets: a framework for systematic assessment of explainable approaches. in Proc. Conference on Fairness, Accountability, and Transparency, 56\u201367 (ACM, 2020).<\/p>\n

Amann, J. et al. To explain or not to explain?\u2013Artificial intelligence explainability in clinical decision support systems. PLoS Digit. Health 1, e0000016 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vetter, D. et al. Lessons learned from assessing trustworthy AI in practice. Digit. Soc. 2, 35 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ghassemi, M., Oakden-Rayner, L. & Beam, A. L. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3, e745\u2013e750 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sokol, K. & Flach, P. One explanation does not fit all: the promise of interactive explanations for machine learning transparency. KI-K.\u00fcnstliche Intell. 34, 235\u2013250 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Weber, R. O., Johs, A. J., Goel, P. & Silva, J. M. XAI is in trouble. AI Mag. 45, 300\u2013316. (2024).<\/p>\n

Freiesleben, T. & K\u00f6nig, G. Dear XAI community, we need to talk! fundamental misconceptions in current XAI research. in World Conference on Explainable Artificial Intelligence, 48\u201365 (Springer, 2023).<\/p>\n

Afroogh, S. et al. Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions. arXiv preprint arXiv:2602.24176 (2026).<\/p>\n

Babic, B., Gerke, S., Evgeniou, T. & Cohen, I. G. Beware explanations from AI in health care. Science 373, 284\u2013286 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bordt, S., Finck, M., Raidl, E. & von Luxburg, U. Post-hoc explanations fail to achieve their purpose in adversarial contexts. in Proc. ACM Conference on Fairness, Accountability, and Transparency, 891\u2013905 (ACM, 2022).<\/p>\n

Hedstr\u00f6m, A. et al. The meta-evaluation problem in explainable AI: identifying reliable estimators with MetaQuantus. Trans. Mach. Learn. Res. https:\/\/openreview.net\/forum?id=j3FK00HyfU<\/a> (2023).<\/p>\n

Bluecher, S., Vielhaben, J. & Strodthoff, N. Decoupling pixel flipping and occlusion strategy for consistent XAI benchmarks. Trans. Mach. Learn. Res. https:\/\/openreview.net\/forum?id=bIiLXdtUVM<\/a> (2024).<\/p>\n

Dombrowski, A.-K. et al. Explanations can be manipulated and geometry is to blame. In Proc. Advances in Neural Information Processing Systems, Vol. 32 (NeurIPS, 2019).<\/p>\n

Xin, X., Huang, F. & Hooker, G. Why you should not trust interpretations in machine learning: adversarial attacks on partial dependence plots. arXiv preprint arXiv: https:\/\/arxiv.org\/abs\/2404.18702<\/a> (2024).<\/p>\n

Kauffmann, J. et al. From clustering to cluster explanations via neural networks. IEEE Trans. Neural Netw. Learn. Syst. 35, 1926\u20131940 (2022).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Clark, B., Oliveira, M., Wilming, R. & Haufe, S. Feature salience–not task-informativeness–drives machine learning model explanations. arXiv preprint arXiv:2602.09238 (2026).<\/p>\n

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R. & Yu, B. Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. 116, 22071\u201322080 (2019).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zicari, R. V. et al. Z-Inspection\u00ae: a process to assess trustworthy AI. IEEE Transactions on Technology and Society, 2, 83\u201397 (2021) .<\/p>\n

Borgonovo, E., Ghidini, V., Hahn, R. & Plischke, E. Explaining classifiers with measures of statistical association. Comput. Stat. Data Anal. 182, 107701 (2023).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Karimi, A.-H., Von K\u00fcgelgen, J., Sch\u00f6lkopf, B. & Valera, I. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. Adv. Neural Inf. Process. Syst. 33, 265\u2013277 (2020).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sixt, L., Granz, M. & Landgraf, T. When explanations lie: why many modified BP attributions fail. in Proc. 37th International Conference on Machine Learning, 9046\u20139057 (PMLR, 2020).<\/p>\n

Bilodeau, B., Jaques, N., Koh, P. W. & Kim, B. Impossibility theorems for feature attribution. Proc. Natl. Acad. Sci. 121 e2304406120 (2024).<\/p>\n

Frye, C., Rowat, C. & Feige, I. Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability. Advances in neural information processing systems, 33, 1229\u20131239 (2020).<\/p>\n

Martin, J. & Haufe, S. cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context. arXiv preprint arXiv:2602.20396 (2026).<\/p>\n

Gj\u00f8lbye, A., Haufe, S. & Hansen, L. K. Minimizing false-positive attributions in explanations of non-linear models. In Proc. 39th Annual Conference on Neural Information Processing Systems. The Thirty-ninth Annual Conference on Neural Information Processing Systems, https:\/\/openreview.net\/forum?id=ORrCEtiiVX<\/a> (2025).<\/p>\n

Oberkampf, W. L. & Roy, C. J.Verification and Validation in Scientific Computing (Cambridge University Press, 2010).<\/p>\n

Imbert, C. & Ardourel, V. Formal verification, scientific code, and the epistemological heterogeneity of computational science. Philos. Sci. 90, 376\u2013394 (2023).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ismail, A. A., Gunady, M., Pessoa, L., Corrada Bravo, H. & Feizi, S. Input-cell attention reduces vanishing saliency of recurrent neural networks. in Proc. Advances in Neural Information Processing Systems, Vol. 32 (Curran Associates, Inc., 2019).<\/p>\n

Yalcin, O., Fan, X. & Liu, S. Evaluating the correctness of explainable AI algorithms for classification. arXiv preprint arXiv:https:\/\/arxiv.org\/abs\/2105.09740<\/a> (2021).<\/p>\n

Arras, L., Osman, A. & Samek, W. CLEVR-XAI: a benchmark dataset for the ground truth evaluation of neural network explanations. Inf. Fusion 81, 14\u201340 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhou, Y., Booth, S., Ribeiro, M. T. & Shah, J. Do feature attribution methods correctly attribute features? in Proc. AAAI Conference on Artificial Intelligence, Vol. 36 (AAAI, 2022).<\/p>\n

Budding, C., Eitel, F., Ritter, K. & Haufe, S. Evaluating saliency methods on artificial data with different background types. in Medical Imaging meets NeurIPS. An official NeurIPS Workshop. https:\/\/arxiv.org\/abs\/2112.04882<\/a> (2021).<\/p>\n

Oliveira, M. et al. Benchmarking the influence of pre-training on explanation performance in MR image classification. Front. Artif. Intell. 7, 1330919 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Oramas, J., Wang, K. & Tuytelaars, T. Visual explanation by interpretation: Improving visual feedback capabilities of deep neural networks. in International Conference on Learning Representations (2019).<\/p>\n

Fok, R. & Weld, D. S. In search of verifiability: Explanations rarely enable complementary performance in AI-advised decision making. AI Mag. 45, 317\u2013332 (2023).<\/p>\n

Janzing, D. & Sch\u00f6lkopf, B. Detecting non-causal artifacts in multivariate linear regression models. in International Conference on Machine Learning, 2245\u20132253 (PMLR, 2018).<\/p>\n

Hvilsh\u00f8j, F., Iosifidis, A. & Assent, I. ECINN: Efficient Counterfactuals from Invertible Neural Networks. In British Machine Vision Conference: Online 22nd-25th (2021).<\/p>\n

Scholkopf, B. et al. Toward Causal Representation Learning Proceedings of the IEEE. 109, 612\u2013634 https:\/\/doi.org\/10.1109\/JPROC.2021.3058954<\/a> (2021).<\/p>\n

Ahuja, K., Mahajan, D., Wang, Y. & Bengio, Y. Interventional causal representation learning. In International conference on machine learning pp. 372-407. (PMLR, 2023).<\/p>\n

Hastie, T. et al. The Elements of Statistical Learning (Springer, 2009).<\/p>\n","protected":false},"excerpt":{"rendered":"European Commission. Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules…\n","protected":false},"author":2,"featured_media":1317,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,1632,1633],"class_list":{"0":"post-1316","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-computational-science","11":"tag-computer-science"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/1316","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=1316"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/1316\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/1317"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=1316"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=1316"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=1316"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}