{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T04:04:26Z","timestamp":1742789066399,"version":"3.40.2"},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031858482","type":"print"},{"value":"9783031858499","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-85849-9_28","type":"book-chapter","created":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T04:27:02Z","timestamp":1742704022000},"page":"351-365","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["How Do ML Students Explain Their Models and\u00a0What Can We Learn from\u00a0This?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2017-7914","authenticated-orcid":false,"given":"Ulrik","family":"Franke","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,23]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","unstructured":"Abdul, A., Vermeulen, J., Wang, D., Lim, B.Y., Kankanhalli, M.: Trends and trajectories for explainable, accountable and intelligible systems: an HCI research agenda. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1\u201318 (2018). https:\/\/doi.org\/10.1145\/3173574.3174156","DOI":"10.1145\/3173574.3174156"},{"issue":"2","key":"28_CR2","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10506-020-09270-4","volume":"29","author":"A Bibal","year":"2021","unstructured":"Bibal, A., Lognoul, M., de Streel, A., Fr\u00e9nay, B.: Legal requirements on explainability in machine learning. Artif. Intell. Law 29(2), 149\u2013169 (2021). https:\/\/doi.org\/10.1007\/s10506-020-09270-4","journal-title":"Artif. Intell. Law"},{"key":"28_CR3","doi-asserted-by":"publisher","unstructured":"Binns, R., Van\u00a0Kleek, M., Veale, M., Lyngs, U., Zhao, J., Shadbolt, N.: \u2018It\u2019s reducing a human being to a percentage\u2019: perceptions of justice in algorithmic decisions. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1\u201314. CHI \u201918, ACM (2018). https:\/\/doi.org\/10.1145\/3173574.3173951","DOI":"10.1145\/3173574.3173951"},{"key":"28_CR4","doi-asserted-by":"publisher","unstructured":"Brennen, A.: What do people really want when they say they want \u201cExplainable AI?\u201d We asked 60 stakeholders. In: Extended abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp.\u00a01\u20137 (2020). https:\/\/doi.org\/10.1145\/3334480.3383047","DOI":"10.1145\/3334480.3383047"},{"key":"28_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/978-3-642-16178-0_5","volume-title":"Multiagent System Technologies","author":"J Broekens","year":"2010","unstructured":"Broekens, J., Harbers, M., Hindriks, K., van den Bosch, K., Jonker, C., Meyer, J.-J.: Do you get it? User-evaluated explainable BDI agents. In: Dix, J., Witteveen, C. (eds.) MATES 2010. LNCS (LNAI), vol. 6251, pp. 28\u201339. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-16178-0_5"},{"issue":"2","key":"28_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.giq.2021.101666","volume":"39","author":"H de Bruijn","year":"2022","unstructured":"de Bruijn, H., Warnier, M., Janssen, M.: The perils and pitfalls of explainable AI: strategies for explaining algorithmic decision-making. Gov. Inf. Q. 39(2), 101666 (2022). https:\/\/doi.org\/10.1016\/j.giq.2021.101666","journal-title":"Gov. Inf. Q."},{"issue":"7623","key":"28_CR7","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/538020a","volume":"538","author":"D Castelvecchi","year":"2016","unstructured":"Castelvecchi, D.: Can we open the black box of AI? Nature News 538(7623), 20 (2016). https:\/\/doi.org\/10.1038\/538020a","journal-title":"Nature News"},{"key":"28_CR8","doi-asserted-by":"publisher","unstructured":"Cavazos, J.G., Phillips, P.J., Castillo, C.D., O\u2019Toole, A.J.: Accuracy comparison across face recognition algorithms: where are we on measuring race bias? IEEE Transactions on Biometrics, Behavior, and Identity Science (2020). https:\/\/doi.org\/10.1109\/TBIOM.2020.3027269","DOI":"10.1109\/TBIOM.2020.3027269"},{"key":"28_CR9","doi-asserted-by":"publisher","unstructured":"Chowdhary, K.: Natural language processing. Fundam. Artif. Intell. 603\u2013649 (2020). https:\/\/doi.org\/10.1007\/978-81-322-3972-7_19","DOI":"10.1007\/978-81-322-3972-7_19"},{"key":"28_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1007\/978-3-030-85616-8_37","volume-title":"Human-Computer Interaction \u2013 INTERACT 2021","author":"M Chromik","year":"2021","unstructured":"Chromik, M.: Making SHAP rap: bridging local and global insights through interaction and narratives. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12933, pp. 641\u2013651. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85616-8_37"},{"key":"28_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1007\/978-3-030-85616-8_36","volume-title":"Human-Computer Interaction \u2013 INTERACT 2021","author":"M Chromik","year":"2021","unstructured":"Chromik, M., Butz, A.: Human-XAI interaction: a review and design principles for explanation user interfaces. In: Ardito, C., et al. (eds.) INTERACT 2021. LNCS, vol. 12933, pp. 619\u2013640. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85616-8_36"},{"key":"28_CR12","doi-asserted-by":"publisher","unstructured":"Dexe, J., Franke, U., Rad, A.: Transparency and insurance professionals: a study of Swedish insurance practice attitudes and future development. Geneva Pap. Risk Insur. Issues Pract. 46, 547\u2013572 (2021). https:\/\/doi.org\/10.1057\/s41288-021-00207-9","DOI":"10.1057\/s41288-021-00207-9"},{"key":"28_CR13","doi-asserted-by":"publisher","unstructured":"Dexe, J., et al.: Explaining automated decision-making\u2014a multinational study of the GDPR right to meaningful information. Geneva Pap. Risk Insur. Issues Pract. 47, 669\u2013697 (2022). https:\/\/doi.org\/10.1057\/s41288-022-00271-9","DOI":"10.1057\/s41288-022-00271-9"},{"key":"28_CR14","doi-asserted-by":"publisher","unstructured":"Dhanorkar, S., Wolf, C.T., Qian, K., Xu, A., Popa, L., Li, Y.: Who needs to know what, when?: Broadening the Explainable AI (XAI) design space by looking at explanations across the AI lifecycle. In: Proceedings of the 2021 ACM Designing Interactive Systems Conference, pp. 1591\u20131602 (2021). https:\/\/doi.org\/10.1145\/3461778.3462131","DOI":"10.1145\/3461778.3462131"},{"issue":"4","key":"28_CR15","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1109\/TTS.2021.3111764","volume":"2","author":"LMB Dor","year":"2021","unstructured":"Dor, L.M.B., Coglianese, C.: Procurement as AI governance. IEEE Trans. Technol. Soc. 2(4), 192\u2013199 (2021). https:\/\/doi.org\/10.1109\/TTS.2021.3111764","journal-title":"IEEE Trans. Technol. Soc."},{"key":"28_CR16","doi-asserted-by":"publisher","unstructured":"Druckman, J.N., Kam, C.D.: Students as experimental participants: a defense of the \u201cnarrow data base\u201d. In: Druckman, J.N., Greene, D.P., Kuklinski, J.H., Lupia, A. (eds.) Cambridge Handbook of Experimental Political Science, pp. 41\u201357. Cambridge University Press (2011). https:\/\/doi.org\/10.1017\/CBO9780511921452.004","DOI":"10.1017\/CBO9780511921452.004"},{"issue":"1","key":"28_CR17","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1145\/3359786","volume":"63","author":"M Du","year":"2019","unstructured":"Du, M., Liu, N., Hu, X.: Techniques for interpretable machine learning. Commun. ACM 63(1), 68\u201377 (2019). https:\/\/doi.org\/10.1145\/3359786","journal-title":"Commun. ACM"},{"key":"28_CR18","doi-asserted-by":"publisher","unstructured":"Ehsan, U., et al.: The who in XAI: how AI background shapes perceptions of AI explanations. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1\u201332 (2024). https:\/\/doi.org\/10.1145\/3613904.3642474","DOI":"10.1145\/3613904.3642474"},{"key":"28_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/978-3-030-60117-1_33","volume-title":"HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence","author":"U Ehsan","year":"2020","unstructured":"Ehsan, U., Riedl, M.O.: Human-centered explainable AI: towards a reflective sociotechnical approach. In: Stephanidis, C., Kurosu, M., Degen, H., Reinerman-Jones, L. (eds.) HCII 2020. LNCS, vol. 12424, pp. 449\u2013466. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60117-1_33"},{"key":"28_CR20","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":"28_CR21","doi-asserted-by":"publisher","unstructured":"Ehsan, U., et al.: Human-Centered Explainable AI (HCXAI): beyond opening the black-box of AI. In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, pp.\u00a01\u20137 (2022). https:\/\/doi.org\/10.1145\/3491101.3503727","DOI":"10.1145\/3491101.3503727"},{"key":"28_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-030-49760-6_4","volume-title":"Design, User Experience, and Usability. Design for Contemporary Interactive Environments","author":"JJ Ferreira","year":"2020","unstructured":"Ferreira, J.J., Monteiro, M.S.: What are people doing about XAI user experience? A survey on AI explainability research and practice. In: Marcus, A., Rosenzweig, E. (eds.) HCII 2020. LNCS, vol. 12201, pp. 56\u201373. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49760-6_4"},{"issue":"5","key":"28_CR23","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/1060710.1060715","volume":"48","author":"KR Fleischmann","year":"2005","unstructured":"Fleischmann, K.R., Wallace, W.A.: A covenant with transparency: opening the black box of models. Commun. ACM 48(5), 93\u201397 (2005). https:\/\/doi.org\/10.1145\/1060710.1060715","journal-title":"Commun. ACM"},{"key":"28_CR24","doi-asserted-by":"publisher","unstructured":"Franke, U., Helgesson Hallstr\u00f6m, C., Artman, H., Dexe, J.: Requirements on and procurement of explainable algorithms\u2014a systematic review of the literature. In: de la Iglesia, D.H., de Paz Santana, J.F., L\u00f3pez Rivero, A.J. (eds.) New Trends in Disruptive Technologies, Tech Ethics, and Artificial Intelligence. DiTTEt 2024. AISC, vol. 1459. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-66635-3_4","DOI":"10.1007\/978-3-031-66635-3_4"},{"key":"28_CR25","doi-asserted-by":"publisher","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 1\u201342 (2018). https:\/\/doi.org\/10.1145\/3236009","DOI":"10.1145\/3236009"},{"key":"28_CR26","doi-asserted-by":"publisher","unstructured":"Hacker, P., Passoth, J.H.: Varieties of AI explanations under the law. From the GDPR to the AIA, and beyond. In: Holzinger, A., Goebel, R., Fong, R., Moon, T., M\u00fcller, KR., Samek, W. (eds.) xxAI - Beyond Explainable AI. xxAI 2020. LNCS, vol. 13200, pp. 343\u2013373. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-031-04083-2_17","DOI":"10.1007\/978-3-031-04083-2_17"},{"key":"28_CR27","doi-asserted-by":"publisher","unstructured":"Hechler, E., Oberhofer, M., Schaeck, T.: Deploying AI in the Enterprise. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-1-4842-6206-1, see especially the chapter AI and Governance, pp.\u00a0165\u2013211","DOI":"10.1007\/978-1-4842-6206-1"},{"issue":"6435","key":"28_CR28","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1126\/science.aax0162","volume":"364","author":"EA Holm","year":"2019","unstructured":"Holm, E.A.: In defense of the black box. Science 364(6435), 26\u201327 (2019). https:\/\/doi.org\/10.1126\/science.aax0162","journal-title":"Science"},{"key":"28_CR29","doi-asserted-by":"publisher","unstructured":"Jonk, E., Iren, D.: Governance and communication of algorithmic decision making: a case study on public sector. In: 2021 IEEE 23rd Conference on Business Informatics (CBI), vol.\u00a01, pp. 151\u2013160. IEEE (2021). https:\/\/doi.org\/10.1109\/CBI52690.2021.00026","DOI":"10.1109\/CBI52690.2021.00026"},{"key":"28_CR30","doi-asserted-by":"publisher","unstructured":"Keil, F.C.: Explanation and understanding. Annu. Rev. Psychol. 57, 227\u2013254 (2006). https:\/\/doi.org\/10.1146\/annurev.psych.57.102904.190100","DOI":"10.1146\/annurev.psych.57.102904.190100"},{"key":"28_CR31","doi-asserted-by":"publisher","unstructured":"Kenny, E.M., Ford, C., Quinn, M., Keane, M.T.: Explaining black-box classifiers using post-hoc explanations-by-example: the effect of explanations and error-rates in XAI user studies. Artif. Intell. 294, 103459 (2021). https:\/\/doi.org\/10.1016\/j.artint.2021.103459","DOI":"10.1016\/j.artint.2021.103459"},{"issue":"3","key":"28_CR32","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/s40685-020-00134-w","volume":"13","author":"A K\u00f6chling","year":"2020","unstructured":"K\u00f6chling, A., Wehner, M.C.: Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Bus. Res. 13(3), 795\u2013848 (2020). https:\/\/doi.org\/10.1007\/s40685-020-00134-w","journal-title":"Bus. Res."},{"issue":"1","key":"28_CR33","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1108\/01140580710754647","volume":"19","author":"GA Liyanarachchi","year":"2007","unstructured":"Liyanarachchi, G.A.: Feasibility of using student subjects in accounting experiments: a review. Pac. Account. Rev. 19(1), 47\u201367 (2007). https:\/\/doi.org\/10.1108\/01140580710754647","journal-title":"Pac. Account. Rev."},{"key":"28_CR34","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html"},{"issue":"4","key":"28_CR35","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/s43681-022-00143-x","volume":"2","author":"M M\u00e4ntym\u00e4ki","year":"2022","unstructured":"M\u00e4ntym\u00e4ki, M., Minkkinen, M., Birkstedt, T., Viljanen, M.: Defining organizational AI governance. AI Ethics 2(4), 603\u2013609 (2022). https:\/\/doi.org\/10.1007\/s43681-022-00143-x","journal-title":"AI Ethics"},{"key":"28_CR36","doi-asserted-by":"publisher","unstructured":"More accountability for big-data algorithms: Nature 537(7621), 449 (2016). https:\/\/doi.org\/10.1038\/537449a","DOI":"10.1038\/537449a"},{"issue":"13s","key":"28_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3583558","volume":"55","author":"M Nauta","year":"2023","unstructured":"Nauta, M., et al.: From anecdotal evidence to quantitative evaluation methods: a systematic review on evaluating explainable AI. ACM Comput. Surv. 55(13s), 1\u201342 (2023). https:\/\/doi.org\/10.1145\/3583558","journal-title":"ACM Comput. Surv."},{"key":"28_CR38","doi-asserted-by":"publisher","unstructured":"Noll, A., Salzmann, R., Wuthrich, M.V.: Case study: French motor third-party liability claims (2020). https:\/\/doi.org\/10.2139\/ssrn.3164764","DOI":"10.2139\/ssrn.3164764"},{"issue":"6464","key":"28_CR39","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S.: Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464), 447\u2013453 (2019). https:\/\/doi.org\/10.1126\/science.aax2342","journal-title":"Science"},{"issue":"1","key":"28_CR40","doi-asserted-by":"publisher","first-page":"3923","DOI":"10.1038\/s41467-020-17419-7","volume":"11","author":"JG Richens","year":"2020","unstructured":"Richens, J.G., Lee, C.M., Johri, S.: Improving the accuracy of medical diagnosis with causal machine learning. Nat. Commun. 11(1), 3923 (2020). https:\/\/doi.org\/10.1038\/s41467-020-17419-7","journal-title":"Nat. Commun."},{"key":"28_CR41","doi-asserted-by":"publisher","unstructured":"Sandkuhl, K.: Putting AI into context \u2013 method support for the introduction of artificial intelligence into organizations. In: 2019 IEEE 21st Conference on Business Informatics (CBI), vol.\u00a01, pp. 157\u2013164. IEEE (2019). https:\/\/doi.org\/10.1109\/CBI.2019.00025","DOI":"10.1109\/CBI.2019.00025"},{"key":"28_CR42","doi-asserted-by":"publisher","unstructured":"Schneider, J., Abraham, R., Meske, C., vom Brocke, J.: Artificial intelligence governance for businesses. Inf. Syst. Manag. 40(3), 229\u2013249 (2023). https:\/\/doi.org\/10.1080\/10580530.2022.2085825","DOI":"10.1080\/10580530.2022.2085825"},{"key":"28_CR43","doi-asserted-by":"publisher","unstructured":"Schotman, E., Iren, D.: Algorithmic decision making and model explainability preferences in the insurance industry: A Delphi study. In: 2022 IEEE 24th Conference on Business Informatics (CBI), vol.\u00a01, pp. 235\u2013242. IEEE (2022). https:\/\/doi.org\/10.1109\/CBI52690.2021.10055","DOI":"10.1109\/CBI52690.2021.10055"},{"issue":"3","key":"28_CR44","doi-asserted-by":"publisher","first-page":"912","DOI":"10.1108\/ITP-08-2019-0433","volume":"34","author":"H S\u00f8rum","year":"2020","unstructured":"S\u00f8rum, H., Presthus, W.: Dude, where\u2019s my data? The GDPR in practice, from a consumer\u2019s point of view. Inf. Technol. People 34(3), 912\u2013929 (2020). https:\/\/doi.org\/10.1108\/ITP-08-2019-0433","journal-title":"Inf. Technol. People"},{"key":"28_CR45","unstructured":"Stecher, P., Pohl, M., Turowski, K.: Enterprise architecture\u2019s effects on organizations\u2019 ability to adopt artificial intelligence\u2013a resource-based perspective. In: Proceedings of the 28th European Conference on Information Systems (ECIS). Association for Information Systems (2020). https:\/\/aisel.aisnet.org\/ecis2020_rp\/173"},{"key":"28_CR46","volume-title":"The Visual Display of Quantitative Information","author":"ER Tufte","year":"2001","unstructured":"Tufte, E.R.: The Visual Display of Quantitative Information, 2nd edn. Graphics Press, Cheshire, CT (2001)","edition":"2"},{"key":"28_CR47","doi-asserted-by":"publisher","unstructured":"Vermeire, T., Laugel, T., Renard, X., Martens, D., Detyniecki, M.: How to choose an explainability method? Towards a methodical implementation of XAI in practice. In: Kamp, M., et al. (eds.) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. CCIS, vol. 1524, pp. 521\u2013533. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-93736-2_39","DOI":"10.1007\/978-3-030-93736-2_39"},{"key":"28_CR48","doi-asserted-by":"publisher","unstructured":"van\u00a0der Waa, J., Nieuwburg, E., Cremers, A., Neerincx, M.: Evaluating XAI: a comparison of rule-based and example-based explanations. Artif. Intell. 291, 103404 (2021). https:\/\/doi.org\/10.1016\/j.artint.2020.103404","DOI":"10.1016\/j.artint.2020.103404"},{"key":"28_CR49","first-page":"841","volume":"31","author":"S Wachter","year":"2017","unstructured":"Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv. J. Law Technol. 31, 841\u2013887 (2017)","journal-title":"Harv. J. Law Technol."},{"key":"28_CR50","doi-asserted-by":"publisher","unstructured":"Wang, X., Yin, M.: Are explanations helpful? A comparative study of the effects of explanations in AI-assisted decision-making. In: 26th International Conference on Intelligent User Interfaces, pp. 318\u2013328 (2021). https:\/\/doi.org\/10.1145\/3397481.3450650","DOI":"10.1145\/3397481.3450650"},{"key":"28_CR51","doi-asserted-by":"publisher","unstructured":"Weitz, K., Schiller, D., Schlagowski, R., Huber, T., Andr\u00e9, E.: \u201cDo you trust me?\u201d Increasing user-trust by integrating virtual agents in explainable AI interaction design. In: Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents, pp.\u00a07\u20139 (2019). https:\/\/doi.org\/10.1145\/3308532.3329441","DOI":"10.1145\/3308532.3329441"},{"key":"28_CR52","doi-asserted-by":"publisher","unstructured":"Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212\u20133232 (2019). https:\/\/doi.org\/10.1109\/TNNLS.2018.2876865","DOI":"10.1109\/TNNLS.2018.2876865"}],"container-title":["Lecture Notes in Business Information Processing","Software Business"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-85849-9_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T04:27:21Z","timestamp":1742704041000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-85849-9_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031858482","9783031858499"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-85849-9_28","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The author has 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":"ICSOB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Software Business","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Utrecht","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","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":"18 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsob2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icsob2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}