{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T10:12:38Z","timestamp":1778407958239,"version":"3.51.4"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031703430","type":"print"},{"value":"9783031703447","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70344-7_8","type":"book-chapter","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T08:02:43Z","timestamp":1724918563000},"page":"125-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["On the\u00a0Robustness of\u00a0Global Feature Effect Explanations"],"prefix":"10.1007","author":[{"given":"Hubert","family":"Baniecki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giuseppe","family":"Casalicchio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernd","family":"Bischl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Przemyslaw","family":"Biecek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"8_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: NeurIPS (2018)"},{"key":"8_CR2","unstructured":"Agarwal, C., et al.: OpenXAI: towards a transparent evaluation of model explanations. In: NeurIPS (2022)"},{"issue":"4","key":"8_CR3","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1111\/rssb.12377","volume":"82","author":"DW Apley","year":"2020","unstructured":"Apley, D.W., Zhu, J.: Visualizing the effects of predictor variables in black box supervised learning models. J. Roy. Stat. Soc. Ser. B (Stat. Methodol.) 82(4), 1059\u20131086 (2020)","journal-title":"J. Roy. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"8_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102303","volume":"107","author":"H Baniecki","year":"2024","unstructured":"Baniecki, H., Biecek, P.: Adversarial attacks and defenses in explainable artificial intelligence: a survey. Inf. Fusion 107, 102303 (2024)","journal-title":"Inf. Fusion"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Baniecki, H., Kretowicz, W., Biecek, P.: Fooling partial dependence via data poisoning. In: ECML PKDD (2022)","DOI":"10.1007\/978-3-031-26409-2_8"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Baniecki, H., Parzych, D., Biecek, P.: The grammar of interactive explanatory model analysis. Data Min. Knowl. Discov. 1\u201337 (2023)","DOI":"10.1007\/s10618-023-00924-w"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Bodria, F., Giannotti, F., Guidotti, R., Naretto, F., Pedreschi, D., Rinzivillo, S.: Benchmarking and survey of explanation methods for black box models. Data Min. Knowl. Discov. 1\u201360 (2023)","DOI":"10.1007\/s10618-023-00933-9"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Casalicchio, G., Molnar, C., Bischl, B.: Visualizing the feature importance for black box models. In: ECML PKDD (2018)","DOI":"10.1007\/978-3-030-10925-7_40"},{"issue":"4","key":"8_CR9","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s11229-023-04334-9","volume":"202","author":"T Freiesleben","year":"2023","unstructured":"Freiesleben, T., Grote, T.: Beyond generalization: a theory of robustness in machine learning. Synthese 202(4), 109 (2023)","journal-title":"Synthese"},{"issue":"5","key":"8_CR10","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001)","journal-title":"Ann. Stat."},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Gan, Y., et al.: \u201cIs your explanation stable?\u201d: A robustness evaluation framework for feature attribution. In: ACM CCS (2022)","DOI":"10.1145\/3548606.3559392"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Ghorbani, A., Abid, A., Zou, J.: Interpretation of neural networks is fragile. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33013681"},{"key":"8_CR13","unstructured":"Gkolemis, V., Dalamagas, T., Diou, C.: DALE: differential accumulated local effects for efficient and accurate global explanations. In: ACML (2023)"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Gkolemis, V., Dalamagas, T., Ntoutsi, E., Diou, C.: RHALE: robust and heterogeneity-aware accumulated local effects. In: ECAI (2023)","DOI":"10.3233\/FAIA230354"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Greenwell, B.M., Boehmke, B.C., McCarthy, A.J.: A simple and effective model-based variable importance measure. arXiv preprint arXiv:1805.04755 (2018)","DOI":"10.32614\/CRAN.package.vip"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Guidotti, R., et al.: Stable and actionable explanations of black-box models through factual and counterfactual rules. Data Min. Knowl. Discov. 1\u201338 (2022)","DOI":"10.1007\/s10618-022-00878-5"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Guyomard, V., Fessant, F., Guyet, T., Bouadi, T., Termier, A.: Generating robust counterfactual explanations. In: ECML PKDD (2023)","DOI":"10.1007\/978-3-031-43418-1_24"},{"issue":"34","key":"8_CR18","first-page":"1","volume":"24","author":"A Hedstrom","year":"2023","unstructured":"Hedstrom, A., et al.: Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations and beyond. J. Mach. Learn. Res. 24(34), 1\u201311 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"8_CR19","unstructured":"Herbinger, J., Bischl, B., Casalicchio, G.: REPID: regional effect plots with implicit interaction detection. In: AISTATS (2022)"},{"issue":"3","key":"8_CR20","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1198\/106186007X237892","volume":"16","author":"G Hooker","year":"2007","unstructured":"Hooker, G.: Generalized functional ANOVA diagnostics for high-dimensional functions of dependent variables. J. Comput. Graph. Stat. 16(3), 709\u2013732 (2007)","journal-title":"J. Comput. Graph. Stat."},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Huang, W., Zhao, X., Jin, G., Huang, X.: SAFARI: versatile and efficient evaluations for robustness of interpretability. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00190"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Jia, Y., Frank, E., Pfahringer, B., Bifet, A., Lim, N.: Studying and exploiting the relationship between model accuracy and explanation quality. In: ECML PKDD (2021)","DOI":"10.1007\/978-3-030-86520-7_43"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Kemter, M., Marwan, N., Villarini, G., Merz, B.: Controls on flood trends across the united states. Water Resourc. Res. 59(2), e2021WR031673 (2023)","DOI":"10.1029\/2021WR031673"},{"key":"8_CR24","unstructured":"Laberge, G., A\u00efvodji, U., Hara, S., Marchand, M., Khomh, F.: Fooling SHAP with stealthily biased sampling. In: ICLR (2023)"},{"key":"8_CR25","unstructured":"Lakkaraju, H., Arsov, N., Bastani, O.: Robust and stable black box explanations. In: ICML (2020)"},{"key":"8_CR26","unstructured":"Lin, C., Covert, I., Lee, S.I.: On the robustness of removal-based feature attributions. In: NeurIPS (2023)"},{"key":"8_CR27","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: NeurIPS (2017)"},{"key":"8_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2021.112883","volume":"250","author":"S Mangalathu","year":"2022","unstructured":"Mangalathu, S., Karthikeyan, K., Feng, D.C., Jeon, J.S.: Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems. Eng. Struct. 250, 112883 (2022)","journal-title":"Eng. Struct."},{"key":"8_CR29","unstructured":"Meyer, A.P., Ley, D., Srinivas, S., Lakkaraju, H.: On Minimizing the impact of dataset shifts on actionable explanations. In: UAI (2023)"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Molnar, C., et\u00a0al.: Relating the partial dependence plot and permutation feature importance to the data generating process. In: XAI (2023)","DOI":"10.1007\/978-3-031-44064-9_24"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., H\u00fcllermeier, E.: iPDP: on partial dependence plots in dynamic modeling scenarios. In: XAI (2023)","DOI":"10.1007\/978-3-031-44064-9_11"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Noppel, M., Wressnegger, C.: SoK: explainable machine learning in adversarial environments. In: IEEE S &P (2024)","DOI":"10.1109\/SP54263.2024.00021"},{"issue":"2","key":"8_CR33","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.cjca.2021.09.004","volume":"38","author":"J Petch","year":"2022","unstructured":"Petch, J., Di, S., Nelson, W.: Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can. J. Cardiol. 38(2), 204\u2013213 (2022)","journal-title":"Can. J. Cardiol."},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should I trust you?\u201d: explaining the predictions of any classifier. In: KDD (2016)","DOI":"10.1145\/2939672.2939778"},{"issue":"1","key":"8_CR35","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-023-36135-6","volume":"14","author":"RC Robertson","year":"2023","unstructured":"Robertson, R.C., et al.: The gut microbiome and early-life growth in a population with high prevalence of stunting. Nat. Commun. 14(1), 654 (2023)","journal-title":"Nat. Commun."},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Schwalbe, G., Finzel, B.: A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts. Data Min. Knowl. Discov. 1\u201359 (2023)","DOI":"10.1007\/s10618-022-00867-8"},{"key":"8_CR37","doi-asserted-by":"crossref","unstructured":"Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: adversarial attacks on post hoc explanation methods. In: AIES (2020)","DOI":"10.1145\/3375627.3375830"},{"key":"8_CR38","unstructured":"Virmaux, A., Scaman, K.: Lipschitz regularity of deep neural networks: analysis and efficient estimation. In: NeurIPS (2018)"},{"key":"8_CR39","unstructured":"Wicker, M.R., Heo, J., Costabello, L., Weller, A.: Robust explanation constraints for neural networks. In: ICLR (2023)"},{"key":"8_CR40","doi-asserted-by":"crossref","unstructured":"Zien, A., Kr\u00e4mer, N., Sonnenburg, S., R\u00e4tsch, G.: The feature importance ranking measure. In: ECML PKDD (2009)","DOI":"10.1007\/978-3-642-04174-7_45"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70344-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T12:12:41Z","timestamp":1732709561000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70344-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703430","9783031703447"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70344-7_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}