{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T01:06:56Z","timestamp":1779325616421,"version":"3.51.4"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032025142","type":"print"},{"value":"9783032025159","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-02515-9_8","type":"book-chapter","created":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T14:51:38Z","timestamp":1755960698000},"page":"118-136","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Understanding the Affordances and Constraints of Explainable AI in Safety-Critical Contexts: A Case Study in Dutch Social Welfare"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1219-855X","authenticated-orcid":false,"given":"Aleksander","family":"Buszydlik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4726-8613","authenticated-orcid":false,"given":"Patrick","family":"Altmeyer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4633-7023","authenticated-orcid":false,"given":"Roel","family":"Dobbe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5385-7695","authenticated-orcid":false,"given":"Cynthia C. S.","family":"Liem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"8_CR1","unstructured":"Alder, M.: DOJ seeks public input on AI use in criminal justice system (2024). https:\/\/fedscoop.com\/doj-seeks-input-on-criminal-justice-ai\/. Accessed 20 Jan 2025"},{"key":"8_CR2","unstructured":"Algorithm Audit: Risk Profiling for Social Welfare Re-examination. Advice document. Technical report AA:2023:02:A, Algorithm Audit (2023)"},{"key":"8_CR3","unstructured":"Algorithm Audit: Risk Profiling for Social Welfare Re-examination. Problem statement. Technical report AA:2023:02:P, Algorithm Audit (2023)"},{"key":"8_CR4","unstructured":"AlgorithmWatch: how Dutch activists got an invasive fraud detection algorithm banned (2020). https:\/\/algorithmwatch.org\/en\/syri-netherlands-algorithm\/. Accessed 20 Jan 2025"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Altmeyer, P., Farmanbar, M., van Deursen, A., Liem, C.C.S.: Faithful model explanations through energy-constrained conformal counterfactuals. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 10 (2024). https:\/\/doi.org\/10.1609\/aaai.v38i10.28956","DOI":"10.1609\/aaai.v38i10.28956"},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Araujo, T., Helberger, N., Kruikemeier, S., de\u00a0Vreese, C.H.: In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI Soc. 35(3) (2020). https:\/\/doi.org\/10.1007\/s00146-019-00931-w","DOI":"10.1007\/s00146-019-00931-w"},{"key":"8_CR7","unstructured":"Braun, J.C., Constantaras, E., Aung, H., Geiger, G., Mehrotra, D., Howden, D.: Suspicion Mach. Methodol. (2023). https:\/\/www.lighthousereports.com\/methodology\/suspicion-machine\/. Accessed 20 Jan 2025"},{"key":"8_CR8","unstructured":"Burgess, M., Schot, E., Geiger, G.: This Algorithm Could Ruin Your Life (March 2023). https:\/\/www.wired.com\/story\/welfare-algorithms-discrimination\/. Accessed 20 Jan 2025"},{"key":"8_CR9","unstructured":"Castelluccia, C., Le\u00a0M\u00e9tayer, D.: Understanding algorithmic decision-making: opportunities and challenges. Technical report, European Parliament (2019)"},{"key":"8_CR10","unstructured":"Centraal Bureau voor de Statistiek: Centraal Bureau voor de Statistiek (2024). subpages therein. https:\/\/www.cbs.nl\/. Accessed 20 Jan 2025"},{"key":"8_CR11","unstructured":"Chouldechova, A., Benavides-Prado, D., Fialko, O., Vaithianathan, R.: A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In: Proceedings of the 1st Conference on Fairness, Accountability and Transparency. PMLR, vol.\u00a081. PMLR (2018)"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Crampton, J.W.: Maps as social constructions: power, communication and visualization. Prog. Hum. Geograph. 25(2) (2001). https:\/\/doi.org\/10.1191\/030913201678580494","DOI":"10.1191\/030913201678580494"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"De\u00a0Bruijn, H., Warnier, M., Janssen, M.: The perils and pitfalls of explainable AI: strategies for explaining algorithmic decision-making. Gov. Inf. Quart. 39(2) (2022). https:\/\/doi.org\/10.1016\/j.giq.2021.101666","DOI":"10.1016\/j.giq.2021.101666"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Dobbe, R.: System Safety and Artificial Intelligence (2022). https:\/\/arxiv.org\/abs\/2202.09292","DOI":"10.1093\/oxfordhb\/9780197579329.013.67"},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Dobbe, R., Wolters, A.: Toward Sociotechnical AI: mapping vulnerabilities for machine learning in context. Mind. Mach. 34(12) (2024). https:\/\/doi.org\/10.1007\/s11023-024-09668-y","DOI":"10.1007\/s11023-024-09668-y"},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Ehsan, U., Riedl, M.O.: Explainability pitfalls: beyond dark patterns in explainable AI. Patterns 5(6) (2024). https:\/\/doi.org\/10.1016\/j.patter.2024.100971","DOI":"10.1016\/j.patter.2024.100971"},{"key":"8_CR17","unstructured":"Eubanks, V.: Want to Predict the Future of Surveillance? Ask Poor Communities (2014). The American Prospect. https:\/\/prospect.org\/power\/want-predict-future-surveillance-ask-poor-communities.\/. Accessed 20 Jan 2025"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Statist. 29(5) (2001). https:\/\/doi.org\/10.1214\/aos\/1013203451","DOI":"10.1214\/aos\/1013203451"},{"key":"8_CR19","unstructured":"Geiger, G., et al.: Suspicion Machines (2023). https:\/\/www.lighthousereports.com\/investigation\/suspicion-machines\/. Accessed 20 Jan 2025"},{"key":"8_CR20","doi-asserted-by":"publisher","unstructured":"Gomes de Sousa, W., et al.: How and where is artificial intelligence in the public sector going? A literature review and research agenda. Gov. Inf. Quart. 36(4) (2019). https:\/\/doi.org\/10.1016\/j.giq.2019.07.004","DOI":"10.1016\/j.giq.2019.07.004"},{"key":"8_CR21","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and Harnessing Adversarial Examples (2015). https:\/\/arxiv.org\/abs\/1412.6572"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Grimmelikhuijsen, S., Meijer, A.: Legitimacy of algorithmic decision-making: six threats and the need for a calibrated institutional response. Perspect. Public Manag. Gov. 5(3) (2022). https:\/\/doi.org\/10.1093\/ppmgov\/gvac008","DOI":"10.1093\/ppmgov\/gvac008"},{"key":"8_CR23","doi-asserted-by":"publisher","unstructured":"Hasan, S.: Governance and public administration. In: Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-31816-5_1820-1","DOI":"10.1007\/978-3-319-31816-5_1820-1"},{"key":"8_CR24","unstructured":"Heikkil\u00e4, M.: Dutch scandal serves as a warning for Europe over risks of using algorithms (2022). https:\/\/www.politico.eu\/article\/dutch-scandal-serves-as-a-warning-for-europe-over-risks-of-using-algorithms\/. Accessed 20 Jan 2025"},{"key":"8_CR25","unstructured":"Hoger Onderwijs Persbureau: Government apologises for discrimination by DUO in fraud detection (2024). https:\/\/www.cursor.tue.nl\/en\/news\/2024\/maart\/week-1\/government-apologises-for-discrimination-by-duo-in-fraud-detection\/. Accessed 20 Jan 2025"},{"key":"8_CR26","unstructured":"Kim, B., Khanna, R., Koyejo, O.: Examples are not enough, learn to criticize! criticism for interpretability. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA (2016)"},{"key":"8_CR27","doi-asserted-by":"publisher","unstructured":"Kudina, O., van\u00a0de Poel, I.: A sociotechnical system perspective on AI. Mind. Mach. 34(3) (2024). https:\/\/doi.org\/10.1007\/s11023-024-09680-2","DOI":"10.1007\/s11023-024-09680-2"},{"key":"8_CR28","doi-asserted-by":"publisher","unstructured":"Leveson, N.G.: Engineering a Safer World: Systems Thinking Applied to Safety. The MIT Press, Cambridge, MA, USA (2011). https:\/\/doi.org\/10.7551\/mitpress\/8179.001.0001","DOI":"10.7551\/mitpress\/8179.001.0001"},{"key":"8_CR29","unstructured":"Leveson, N.G., Thomas, J.P.: STPA Handbook (2018). https:\/\/psas.scripts.mit.edu\/home\/books-and-handbooks\/. Accessed 20 Jan 2025"},{"key":"8_CR30","doi-asserted-by":"publisher","unstructured":"Levy, K., Chasalow, K.E., Riley, S.: Algorithms and decision-making in the public sector. Annu. Rev. Law Soc. Sci. 17(1) (2021). https:\/\/doi.org\/10.1146\/annurev-lawsocsci-041221-023808","DOI":"10.1146\/annurev-lawsocsci-041221-023808"},{"key":"8_CR31","doi-asserted-by":"publisher","unstructured":"Maas, J.: Machine learning and power relations. AI Soc. 38(4) (2023). https:\/\/doi.org\/10.1007\/s00146-022-01400-7","DOI":"10.1007\/s00146-022-01400-7"},{"key":"8_CR32","doi-asserted-by":"publisher","unstructured":"Marcinkevi\u010ds, R., Vogt, J.E.: Interpretable and explainable machine learning: a methods-centric overview with concrete examples. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 13(3) (2023). https:\/\/doi.org\/10.1002\/widm.1493","DOI":"10.1002\/widm.1493"},{"key":"8_CR33","doi-asserted-by":"publisher","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. J. Artif. Intell. 267 (2019). https:\/\/doi.org\/10.1016\/j.artint.2018.07.007","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"8_CR34","unstructured":"Ministerie van Binnenlandse Zaken en Koninkrijksrelaties: Handreiking Algoritmeregister. Technical Report 1.0, Ministerie BZK (2023)"},{"key":"8_CR35","doi-asserted-by":"publisher","unstructured":"Mittelstadt, B.: Principles alone cannot guarantee ethical AI. Nat. Mach. Intell. 1(11) (2019). https:\/\/doi.org\/10.1038\/s42256-019-0114-4","DOI":"10.1038\/s42256-019-0114-4"},{"key":"8_CR36","unstructured":"Murgia, M.: Algorithms are deciding who gets organ transplants. Are their decisions fair? (2023). https:\/\/www.ft.com\/content\/5125c83a-b82b-40c5-8b35-99579e087951. Accessed 20 Jan 2025"},{"key":"8_CR37","doi-asserted-by":"publisher","unstructured":"Netten, N., Shoae-Bargh, M., Choenni, S.: Exploiting data analytics for social services: on searching for profiles of unlawful use of social benefits. In: Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance. ICEGOV \u201918, Association for Computing Machinery, New York, (2018). https:\/\/doi.org\/10.1145\/3209415.3209481","DOI":"10.1145\/3209415.3209481"},{"key":"8_CR38","volume-title":"Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy","author":"C O\u2019Neil","year":"2016","unstructured":"O\u2019Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group, USA (2016)"},{"key":"8_CR39","unstructured":"Overheid.nl: Lokale wet- en regelgeving (2024)., subpages therein. https:\/\/lokaleregelgeving.overheid.nl\/. Accessed 20 Jan 2025"},{"key":"8_CR40","unstructured":"Rathenau Instituut: governing algorithmic decision-making in government. The role of the Senate. Technical report Rathenau Instituut (2021)"},{"key":"8_CR41","unstructured":"Rechtspraak.nl: SyRI legislation in breach of European Convention on Human Rights (2020). https:\/\/www.rechtspraak.nl\/Organisatie-en-contact\/Organisatie\/Rechtbanken\/Rechtbank-Den-Haag\/Nieuws\/Paginas\/SyRI-legislation-in-breach-of-European-Convention-on-Human-Rights.aspx. Accessed 20 Jan 2025"},{"key":"8_CR42","unstructured":"Rekenkamer Rotterdam: gekleurde technologie. verkenning ethisch gebruik algoritmes. Technical report, Rekenkamer Rotterdam (2021)"},{"key":"8_CR43","doi-asserted-by":"publisher","unstructured":"Rieder, B., Hofmann, J.: Towards platform observability. Internet Policy Rev. 9(4) (2020). https:\/\/doi.org\/10.14763\/2020.4.1535","DOI":"10.14763\/2020.4.1535"},{"key":"8_CR44","unstructured":"Rismani, S., Dobbe, R., Moon, A.: From Silos to Systems: Process-Oriented Hazard Analysis for AI Systems (2024). https:\/\/arxiv.org\/abs\/2410.22526"},{"key":"8_CR45","doi-asserted-by":"publisher","unstructured":"Rizk, A., Lindgren, I.: Automated decision-making in the public sector: a multidisciplinary literature review. In: Electronic Government. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-70274-7_15","DOI":"10.1007\/978-3-031-70274-7_15"},{"key":"8_CR46","doi-asserted-by":"publisher","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5) (2019). https:\/\/doi.org\/10.1038\/s42256-019-0048-x","DOI":"10.1038\/s42256-019-0048-x"},{"key":"8_CR47","doi-asserted-by":"publisher","unstructured":"Selbst, A.D., Barocas, S.: The intuitive appeal of explainable machines. Fordham L. Rev. 87 (2018). https:\/\/doi.org\/10.2139\/ssrn.3126971","DOI":"10.2139\/ssrn.3126971"},{"key":"8_CR48","doi-asserted-by":"publisher","unstructured":"Selbst, A.D., boyd, d., Friedler, S.A., Venkatasubramanian, S., Vertesi, J.: Fairness and abstraction in sociotechnical systems. In: Proceedings of the Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3287560.3287598","DOI":"10.1145\/3287560.3287598"},{"key":"8_CR49","doi-asserted-by":"publisher","unstructured":"Speith, T.: A review of taxonomies of explainable artificial intelligence (XAI) Methods. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. FAccT \u201922, Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3531146.3534639","DOI":"10.1145\/3531146.3534639"},{"key":"8_CR50","doi-asserted-by":"publisher","unstructured":"Ustun, B., Spangher, A., Liu, Y.: Actionable recourse in linear classification. In: Proceedings of the Conference on Fairness, Accountability, and Transparency. FAT* \u201919, Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3287560.3287566","DOI":"10.1145\/3287560.3287566"},{"key":"8_CR51","doi-asserted-by":"publisher","unstructured":"Van den Goorbergh, R., van Smeden, M., Timmerman, D., Van\u00a0Calster, B.: The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J. Am. Med. Inform. Assoc. 29(9) (2022). https:\/\/doi.org\/10.1093\/jamia\/ocac093","DOI":"10.1093\/jamia\/ocac093"},{"key":"8_CR52","doi-asserted-by":"publisher","unstructured":"Veale, M., Binns, R.: Fairer machine learning in the real world: mitigating discrimination without collecting sensitive data. Big Data Soc. 4(2) (2017). https:\/\/doi.org\/10.1177\/2053951717743530","DOI":"10.1177\/2053951717743530"},{"key":"8_CR53","doi-asserted-by":"publisher","unstructured":"Verma, S., et al.: Counterfactual explanations and algorithmic recourses for machine learning: a review. ACM Comput. Surv. 56(12) (2024). https:\/\/doi.org\/10.1145\/3677119","DOI":"10.1145\/3677119"},{"key":"8_CR54","doi-asserted-by":"publisher","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harvard J. Law Technol. 31 (2017). https:\/\/doi.org\/10.2139\/ssrn.3063289","DOI":"10.2139\/ssrn.3063289"},{"key":"8_CR55","unstructured":"Weidinger, L., et al.: Sociotechnical Safety Evaluation of Generative AI Systems (2023). https:\/\/arxiv.org\/abs\/2310.11986"},{"key":"8_CR56","doi-asserted-by":"publisher","unstructured":"Wieringa, M.: Municipalities Enacting Algorithms: A Typology of Dutch Municipal Strategies for Leveraging Algorithmic Systems, chap.\u00a01, pp. 19\u201341. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-84748-6_2","DOI":"10.1007\/978-3-031-84748-6_2"},{"key":"8_CR57","doi-asserted-by":"publisher","unstructured":"Zacharias, J., von Zahn, M., Chen, J., Hinz, O.: Designing a feature selection method based on explainable artificial intelligence. Electron. Markets 32(4) (2022). https:\/\/doi.org\/10.1007\/s12525-022-00608-1","DOI":"10.1007\/s12525-022-00608-1"}],"container-title":["Lecture Notes in Computer Science","Electronic Participation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02515-9_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:38:20Z","timestamp":1779323900000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02515-9_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"ISBN":["9783032025142","9783032025159"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02515-9_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,21]]},"assertion":[{"value":"21 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ePart","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Electronic Participation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Krems","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epart2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dgsociety.org\/egov-2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}