Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability
Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often based on more complex algorithms and therefore la...
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| Published in: | International journal of information management Vol. 69; p. 102538 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.04.2023
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| ISSN: | 0268-4012, 1873-4707 |
| Online Access: | Get full text |
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| Abstract | Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often based on more complex algorithms and therefore lack explainability and vice versa. However, there is little to no empirical evidence of this tradeoff from an end user perspective. We aim to provide empirical evidence by conducting two user experiments. Using two distinct datasets, we first measure the tradeoff for five common classes of machine learning algorithms. Second, we address the problem of end user perceptions of explainable artificial intelligence augmentations aimed at increasing the understanding of the decision logic of high-performing complex models. Our results diverge from the widespread assumption of a tradeoff curve and indicate that the tradeoff between model performance and explainability is much less gradual in the end user’s perception. This is a stark contrast to assumed inherent model interpretability. Further, we found the tradeoff to be situational for example due to data complexity. Results of our second experiment show that while explainable artificial intelligence augmentations can be used to increase explainability, the type of explanation plays an essential role in end user perception.
•Theoretical algorithm interpretability does not entail perceived explainability.•Tradeoff can be characterized by a group structure rather than a curve.•Tree-based machine learning algorithms achieve best explainability results.•While performance distance increases for complex datasets, explainability distance decreases.•Local XAI augmentations requiring low cognitive effort fare better with end users. |
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| AbstractList | Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often based on more complex algorithms and therefore lack explainability and vice versa. However, there is little to no empirical evidence of this tradeoff from an end user perspective. We aim to provide empirical evidence by conducting two user experiments. Using two distinct datasets, we first measure the tradeoff for five common classes of machine learning algorithms. Second, we address the problem of end user perceptions of explainable artificial intelligence augmentations aimed at increasing the understanding of the decision logic of high-performing complex models. Our results diverge from the widespread assumption of a tradeoff curve and indicate that the tradeoff between model performance and explainability is much less gradual in the end user’s perception. This is a stark contrast to assumed inherent model interpretability. Further, we found the tradeoff to be situational for example due to data complexity. Results of our second experiment show that while explainable artificial intelligence augmentations can be used to increase explainability, the type of explanation plays an essential role in end user perception.
•Theoretical algorithm interpretability does not entail perceived explainability.•Tradeoff can be characterized by a group structure rather than a curve.•Tree-based machine learning algorithms achieve best explainability results.•While performance distance increases for complex datasets, explainability distance decreases.•Local XAI augmentations requiring low cognitive effort fare better with end users. |
| ArticleNumber | 102538 |
| Author | Herm, Lukas-Valentin Wanner, Jonas Heinrich, Kai Janiesch, Christian |
| Author_xml | – sequence: 1 givenname: Lukas-Valentin surname: Herm fullname: Herm, Lukas-Valentin email: lukas-valentin.herm@uni-wuerzburg.de organization: Julius-Maximilians-Universität Würzburg, Würzburg, Germany – sequence: 2 givenname: Kai surname: Heinrich fullname: Heinrich, Kai email: kai.heinrich@ovgu.de organization: Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany – sequence: 3 givenname: Jonas surname: Wanner fullname: Wanner, Jonas email: jonas.wanner@uni-wuerzburg.de organization: Julius-Maximilians-Universität Würzburg, Würzburg, Germany – sequence: 4 givenname: Christian surname: Janiesch fullname: Janiesch, Christian email: christian.janiesch@tu-dortmund.de organization: TU Dortmund University, Otto-Hahn-Str. 14, 44227 Dortmund, Germany |
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| Cites_doi | 10.1109/ACCESS.2019.2949286 10.3390/electronics10050593 10.1145/3387166 10.1007/s12525-020-00441-4 10.1007/978-3-030-20521-8_1 10.1016/j.jjimei.2021.100050 10.1038/s41586-018-0438-y 10.1016/j.patrec.2020.07.042 10.1016/j.jmsy.2018.01.003 10.1016/j.ejmp.2021.02.006 10.1007/978-3-030-50334-5_4 10.1145/3236009 10.1609/aimag.v40i2.2850 10.1038/s41586-019-1799-6 10.1016/j.obhdp.2018.12.005 10.1007/s41870-017-0080-1 10.1007/s10994-019-05856-5 10.1057/ejis.2016.2 10.1016/j.artint.2018.07.007 10.1109/ACCESS.2018.2870052 10.1214/lnms/1196794933 10.1016/j.ijinfomgt.2019.102061 10.1145/3290605.3300233 10.1007/s00330-020-06946-y 10.21275/ART20203995 10.1016/j.inffus.2019.12.012 10.1016/j.ijinfomgt.2019.08.002 10.1016/j.techfore.2021.121390 10.1016/j.ijforecast.2019.03.015 10.1038/s41524-018-0081-z 10.1145/3183399.3183424 10.1145/129617.129621 10.1038/s42256-019-0048-x 10.25300/MISQ/2021/15882 10.1080/135467896394447 10.34068/joe.50.02.48 10.1016/j.ijinfomgt.2021.102379 10.1016/j.ijinfomgt.2022.102497 10.1038/nature14539 10.1007/s12525-021-00475-2 10.1016/j.neunet.2014.09.003 10.3748/wjg.v25.i14.1666 10.1007/s12599-020-00678-5 10.1162/99608f92.5a8a3a3d 10.17705/1jais.00124 10.1016/j.ijinfomgt.2021.102383 10.1007/s13347-021-00495-y 10.1145/3457607 10.17705/1jais.00664 10.1016/j.ijhcs.2020.102551 10.1007/s13347-021-00477-0 10.3389/fpsyg.2017.02239 10.1016/j.artint.2020.103404 10.25300/MISQ/2021/16564 10.1145/2939672.2939778 10.1080/2573234X.2021.1952913 10.1109/MIS.2013.24 10.1016/j.artint.2021.103459 10.1016/j.artint.2021.103573 10.1609/aimag.v38i3.2741 10.1371/journal.pmed.1002709 |
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| Keywords | XAI Tradeoff Performance Explainability Machine learning |
| Language | English |
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| References | Ebers (bib18) 2020 Goodman, Flaxman (bib22) 2017; 38 Boone, Boone (bib10) 2012; 50 Collins, Dennehy, Conboy, Mikalef (bib14) 2021; 60 James, Witten, Hastie, Tibshirani (bib35) 2013 Berger, Adam, Rühr, Benlian (bib7) 2021; 63 LeCun, Bengio, Hinton (bib43) 2015; 521 Adadi, Berrada (bib1) 2018; 6 Wang, Ma, Zhang, Gao, Wu (bib76) 2018; 48 Angelov, Soares (bib3) 2019; 1912 Janiesch, Zschech, Heinrich (bib36) 2021; 31 European Conference on Information Systems, Virtual Conference. Jauernig, Uhl, Walkowitz (bib38) 2022; 35 Mignan, A., & Broccardo, M. (2019). A deeper look into ‘deep learning of aftershock patterns following large earthquakes’: Illustrating first principles in neural network physical interpretability. International Work-Conference on Artificial Neural Networks, Cham. Chandra, Bedi (bib12) 2021; 13 Janosi, A., Steinbrunn, W., Pfisterer, M., & Detrano, R. (1988). Dwivedi, Hughes, Ismagilova, Aarts, Coombs, Crick, Eirug (bib17) 2021; 57 Wanner, J., Heinrich, K., Janiesch, C., & Zschech, P. (2020). How Much AI Do You Require? Decision Factors for Adopting AI Technology. 41st International Conference on Information Systems (ICIS), India. Guidotti, Monreale, Ruggieri, Turini, Giannotti, Pedreschi (bib23) 2018; 51 Rudin, Radin (bib64) 2019; 1 Hilton (bib30) 1996; 2 La Cava, W., Williams, H., Fu, W., & Moore, J. H. (2019). Evaluating recommender systems for AI-driven data science. Zhang, Ling (bib83) 2018; 4 Chiu, Zhu, Corbett (bib13) 2021; 60 Heinrich, K., Janiesch, C., Möller, B., & Zschech, P. (2019). Is bigger always better? Lessons learnt from the evolution of deep learning architectures for image classification. Pre-ICIS SIGDSA Symposium, Munich, Germany. Logg, Minson, Moore (bib44) 2019; 151 Meske, Bunde, Schneider, Gersch (bib53) 2022 Fürnkranz, Kliegr, Paulheim (bib20) 2020; 109 Thiebes, Lins, Sunyaev (bib73) 2021; 31 Zhou, Gandomi, Chen, Holzinger (bib84) 2021; 10 Yang, Bang (bib82) 2019; 25 Jussupow, E., Benbasat, I., & Heinzl, A. (2020). van der Waa, Nieuwburg, Cremers, Neerincx (bib74) 2021; 291 . 28th European Conference on Information Systems, Virtual Conference. Sharma, Kumar, Chuah (bib67) 2021; 1 Castiglioni, Rundo, Codari, Di Leo, Salvatore, Interlenghi, Sardanelli (bib11) 2021; 83 Arrieta, Díaz-Rodríguez, Del Ser, Bennetot, Tabik, Barbado, Benjamins (bib4) 2020; 58 Straub, Burton-Jones (bib70) 2007; 8 Subramanian, Nosek, Raghunathan, Kanitkar (bib72) 1992; 35 Nanayakkara, Fogarty, Tremeer, Ross, Richards, Bergmeir, Tacey (bib59) 2018; 15 Herm, L.-V., Wanner, J., Seubert, F., & Janiesch, C. (2021). Mualla, Tchappi, Kampik, Najjar, Calvaresi, Abbas-Turki, Nicolle (bib57) 2022; 302 Mohseni, Zarei, Ragan (bib56) 2021; 11 Russell, Norvig (bib65) 2021 Baird, Maruping (bib6) 2021; 45 Hradecky, Kennell, Cai, Davidson (bib33) 2022; 65 Meske, Bunde (bib52) 2022 Liu, R., Strawderman, W., & Zhang, C.-H. (2007). Complex Datasets and Inverse Problems. Tomography, Networks and Beyond. Asatiani, Malo, Nagbøl, Penttinen, Rinta-Kahila, Salovaara (bib5) 2021; 22 Miller (bib55) 2019; 267 Schmidhuber (bib66) 2015; 61 Nguyen (bib60) 2018 Das, A., & Rad, P. (2020). Opportunities and challenges in explainable artificial intelligence (xai): A survey. Dam, H. K., Tran, T., & Ghose, A. (2018). Explainable software analytics. 40th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), Gothenburg. Mehrabi, Morstatter, Saxena, Lerman, Galstyan (bib49) 2021; 54 Strohm, Hehakaya, Ranschaert, Boon, Moors (bib71) 2020; 30 DeVries, Viégas, Wattenberg, Meade (bib16) 2018; 560 Kenny, Ford, Quinn, Keane (bib39) 2021; 294 Goodfellow, Bengio, Courville (bib21) 2016 Hyndman (bib34) 2020; 36 Wang, Fan, Wang (bib77) 2021; 141 von Eschenbach (bib19) 2021 Shin (bib68) 2021; 146 Vempala, Russo (bib75) 2018; 8 Mahmud, Islam, Ahmed, Smolander (bib47) 2022; 175 McKinney, Sieniek, Godbole, Godwin, Antropova, Ashrafian, Darzi (bib48) 2020; 577 Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018). Metrics for Explainable AI: Challenges and Prospects. Rudin (bib63) 2019; 1 Wanner, J., Popp, L., Fuchs, K., Heinrich, K., Herm, L.-V., & Janiesch, C. (2021b). Adoption Barriers Of AI: A Context-specific Acceptance Model For Industrial Maintenance. 29th European Conference on Information Systems, Virtual Conference. Bishop (bib8) 2006 Bohaju (bib9) 2020 Guo, M., Zhang, Q., Liao, X., & Chen, Y. (2019). An interpretable machine learning framework for modelling human decision behavior. Hoffman, Johnson, Bradshaw, Underbrink (bib32) 2013; 28 Gunning (bib24) 2019; 40 Loyola-Gonzalez (bib45) 2019; 7 Preece, A., Harborne, D., Braines, D., Tomsett, R., & Chakraborty, S. (2018). Stakeholders in explainable AI. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. Mahesh (bib46) 2020; 9 Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should i trust you?" Explaining the predictions of any classifier. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA. Meske, C., & Bunde, E. (2020). Transparency and trust in human-AI-interaction: The role of model-agnostic explanations in computer vision-based decision support. International Conference on Human-Computer Interaction, Virtual Conference. Lebovitz, Levina, Lifshitz-Assaf (bib42) 2021; 45 Shin, Zhong, Biocca (bib69) 2020; 52 Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., & Inkpen, K. (2019). Guidelines for human-AI interaction. 2019 CHI Conference on Human Factors in Computing Systems, Glasgow. Wanner, Herm, Heinrich, Janiesch (bib79) 2021 Wanner, Herm, Heinrich, Janiesch (bib80) 2022; 5 UCI Machine Learning Library. Retrieved 10.10.2021 from Müller, Junglas, Brocke, Debortoli (bib58) 2017; 25 Rudin (10.1016/j.ijinfomgt.2022.102538_bib64) 2019; 1 10.1016/j.ijinfomgt.2022.102538_bib25 10.1016/j.ijinfomgt.2022.102538_bib26 10.1016/j.ijinfomgt.2022.102538_bib2 10.1016/j.ijinfomgt.2022.102538_bib27 Hyndman (10.1016/j.ijinfomgt.2022.102538_bib34) 2020; 36 Strohm (10.1016/j.ijinfomgt.2022.102538_bib71) 2020; 30 10.1016/j.ijinfomgt.2022.102538_bib28 10.1016/j.ijinfomgt.2022.102538_bib29 Mahmud (10.1016/j.ijinfomgt.2022.102538_bib47) 2022; 175 Zhou (10.1016/j.ijinfomgt.2022.102538_bib84) 2021; 10 Wanner (10.1016/j.ijinfomgt.2022.102538_bib79) 2021 Thiebes (10.1016/j.ijinfomgt.2022.102538_bib73) 2021; 31 Müller (10.1016/j.ijinfomgt.2022.102538_bib58) 2017; 25 Wanner (10.1016/j.ijinfomgt.2022.102538_bib80) 2022; 5 Castiglioni (10.1016/j.ijinfomgt.2022.102538_bib11) 2021; 83 Miller (10.1016/j.ijinfomgt.2022.102538_bib55) 2019; 267 Chiu (10.1016/j.ijinfomgt.2022.102538_bib13) 2021; 60 Nguyen (10.1016/j.ijinfomgt.2022.102538_bib60) 2018 10.1016/j.ijinfomgt.2022.102538_bib61 Straub (10.1016/j.ijinfomgt.2022.102538_bib70) 2007; 8 10.1016/j.ijinfomgt.2022.102538_bib62 von Eschenbach (10.1016/j.ijinfomgt.2022.102538_bib19) 2021 10.1016/j.ijinfomgt.2022.102538_bib78 Nanayakkara (10.1016/j.ijinfomgt.2022.102538_bib59) 2018; 15 Shin (10.1016/j.ijinfomgt.2022.102538_bib69) 2020; 52 Subramanian (10.1016/j.ijinfomgt.2022.102538_bib72) 1992; 35 10.1016/j.ijinfomgt.2022.102538_bib37 Meske (10.1016/j.ijinfomgt.2022.102538_bib52) 2022 James (10.1016/j.ijinfomgt.2022.102538_bib35) 2013 Goodfellow (10.1016/j.ijinfomgt.2022.102538_bib21) 2016 Sharma (10.1016/j.ijinfomgt.2022.102538_bib67) 2021; 1 Baird (10.1016/j.ijinfomgt.2022.102538_bib6) 2021; 45 Mualla (10.1016/j.ijinfomgt.2022.102538_bib57) 2022; 302 Dwivedi (10.1016/j.ijinfomgt.2022.102538_bib17) 2021; 57 Lebovitz (10.1016/j.ijinfomgt.2022.102538_bib42) 2021; 45 Berger (10.1016/j.ijinfomgt.2022.102538_bib7) 2021; 63 Mahesh (10.1016/j.ijinfomgt.2022.102538_bib46) 2020; 9 Chandra (10.1016/j.ijinfomgt.2022.102538_bib12) 2021; 13 DeVries (10.1016/j.ijinfomgt.2022.102538_bib16) 2018; 560 Ebers (10.1016/j.ijinfomgt.2022.102538_bib18) 2020 Hilton (10.1016/j.ijinfomgt.2022.102538_bib30) 1996; 2 Janiesch (10.1016/j.ijinfomgt.2022.102538_bib36) 2021; 31 Logg (10.1016/j.ijinfomgt.2022.102538_bib44) 2019; 151 McKinney (10.1016/j.ijinfomgt.2022.102538_bib48) 2020; 577 Collins (10.1016/j.ijinfomgt.2022.102538_bib14) 2021; 60 10.1016/j.ijinfomgt.2022.102538_bib31 Wang (10.1016/j.ijinfomgt.2022.102538_bib76) 2018; 48 Jauernig (10.1016/j.ijinfomgt.2022.102538_bib38) 2022; 35 Angelov (10.1016/j.ijinfomgt.2022.102538_bib3) 2019; 1912 Meske (10.1016/j.ijinfomgt.2022.102538_bib53) 2022 Loyola-Gonzalez (10.1016/j.ijinfomgt.2022.102538_bib45) 2019; 7 Bishop (10.1016/j.ijinfomgt.2022.102538_bib8) 2006 Vempala (10.1016/j.ijinfomgt.2022.102538_bib75) 2018; 8 Schmidhuber (10.1016/j.ijinfomgt.2022.102538_bib66) 2015; 61 Wang (10.1016/j.ijinfomgt.2022.102538_bib77) 2021; 141 Russell (10.1016/j.ijinfomgt.2022.102538_bib65) 2021 Guidotti (10.1016/j.ijinfomgt.2022.102538_bib23) 2018; 51 Zhang (10.1016/j.ijinfomgt.2022.102538_bib83) 2018; 4 Mehrabi (10.1016/j.ijinfomgt.2022.102538_bib49) 2021; 54 Arrieta (10.1016/j.ijinfomgt.2022.102538_bib4) 2020; 58 Hoffman (10.1016/j.ijinfomgt.2022.102538_bib32) 2013; 28 van der Waa (10.1016/j.ijinfomgt.2022.102538_bib74) 2021; 291 10.1016/j.ijinfomgt.2022.102538_bib81 10.1016/j.ijinfomgt.2022.102538_bib40 Boone (10.1016/j.ijinfomgt.2022.102538_bib10) 2012; 50 10.1016/j.ijinfomgt.2022.102538_bib41 Adadi (10.1016/j.ijinfomgt.2022.102538_bib1) 2018; 6 10.1016/j.ijinfomgt.2022.102538_bib15 Shin (10.1016/j.ijinfomgt.2022.102538_bib68) 2021; 146 Fürnkranz (10.1016/j.ijinfomgt.2022.102538_bib20) 2020; 109 Gunning (10.1016/j.ijinfomgt.2022.102538_bib24) 2019; 40 Goodman (10.1016/j.ijinfomgt.2022.102538_bib22) 2017; 38 LeCun (10.1016/j.ijinfomgt.2022.102538_bib43) 2015; 521 Hradecky (10.1016/j.ijinfomgt.2022.102538_bib33) 2022; 65 Rudin (10.1016/j.ijinfomgt.2022.102538_bib63) 2019; 1 Kenny (10.1016/j.ijinfomgt.2022.102538_bib39) 2021; 294 Mohseni (10.1016/j.ijinfomgt.2022.102538_bib56) 2021; 11 Bohaju (10.1016/j.ijinfomgt.2022.102538_bib9) 2020 Asatiani (10.1016/j.ijinfomgt.2022.102538_bib5) 2021; 22 10.1016/j.ijinfomgt.2022.102538_bib50 10.1016/j.ijinfomgt.2022.102538_bib51 10.1016/j.ijinfomgt.2022.102538_bib54 Yang (10.1016/j.ijinfomgt.2022.102538_bib82) 2019; 25 |
| References_xml | – volume: 22 start-page: 325 year: 2021 end-page: 352 ident: bib5 article-title: Sociotechnical envelopment of artificial intelligence: An approach to organizational deployment of inscrutable artificial intelligence systems publication-title: Journal of the Association for Information Systems – start-page: 1607 year: 2021 end-page: 1622 ident: bib19 article-title: Transparency and the black box problem: Why we do not trust AI publication-title: Philosophy & Technology – volume: 25 start-page: 289 year: 2017 end-page: 302 ident: bib58 article-title: Utilizing big data analytics for information systems research: Challenges, promises and guidelines publication-title: European Journal of Information Systems – volume: 10 start-page: 593 year: 2021 ident: bib84 article-title: Evaluating the quality of machine learning explanations: A survey on methods and metrics publication-title: Electronics – reference: Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. – volume: 294 year: 2021 ident: bib39 article-title: Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies publication-title: Artificial Intelligence – year: 2016 ident: bib21 article-title: Deep learning – volume: 60 year: 2021 ident: bib14 article-title: Artificial intelligence in information systems research: A systematic literature review and research agenda publication-title: International Journal of Information Management – volume: 38 start-page: 50 year: 2017 end-page: 57 ident: bib22 article-title: European union regulations on algorithmic decision-making and a “right to explanation” publication-title: AI Magazine – volume: 45 start-page: 1501 year: 2021 end-page: 1525 ident: bib42 article-title: Is AI ground truth really “true”? The dangers of training and evaluating AI tools based on experts’ know-what publication-title: Management Information Systems Quarterly – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: bib66 article-title: Deep learning in neural networks: An overview publication-title: Neural Networks – volume: 63 start-page: 55 year: 2021 end-page: 68 ident: bib7 article-title: Watch me improve—Algorithm aversion and demonstrating the ability to learn publication-title: Business & Information Systems Engineering – volume: 57 year: 2021 ident: bib17 article-title: Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy publication-title: International Journal of Information Management – volume: 30 start-page: 5525 year: 2020 end-page: 5532 ident: bib71 article-title: Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors publication-title: European radiology – volume: 8 start-page: 2239 year: 2018 ident: bib75 article-title: Modeling music emotion judgments using machine learning methods publication-title: Frontiers in Psychology – reference: European Conference on Information Systems, Virtual Conference. – volume: 4 start-page: 25 year: 2018 ident: bib83 article-title: A strategy to apply machine learning to small datasets in materials science publication-title: npj Computational Materials – volume: 6 start-page: 52138 year: 2018 end-page: 52160 ident: bib1 article-title: Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) publication-title: IEEE Access – volume: 5 start-page: 29 year: 2022 end-page: 50 ident: bib80 article-title: A social evaluation of the perceived goodness of explainability in machine learning publication-title: Journal of Business Analytics – reference: Liu, R., Strawderman, W., & Zhang, C.-H. (2007). Complex Datasets and Inverse Problems. Tomography, Networks and Beyond. – volume: 83 start-page: 9 year: 2021 end-page: 24 ident: bib11 article-title: AI applications to medical images: From machine learning to deep learning publication-title: Physica Medica – volume: 31 start-page: 685 year: 2021 end-page: 695 ident: bib36 article-title: Machine learning and deep learning publication-title: Electronic Markets – volume: 577 start-page: 89 year: 2020 end-page: 94 ident: bib48 article-title: International evaluation of an AI system for breast cancer screening publication-title: Nature – reference: . 28th European Conference on Information Systems, Virtual Conference. – reference: Wanner, J., Popp, L., Fuchs, K., Heinrich, K., Herm, L.-V., & Janiesch, C. (2021b). Adoption Barriers Of AI: A Context-specific Acceptance Model For Industrial Maintenance. 29th European Conference on Information Systems, Virtual Conference. – volume: 51 start-page: 1 year: 2018 end-page: 42 ident: bib23 article-title: A survey of methods for explaining black box models publication-title: ACM Computing surveys (CSUR) – volume: 11 start-page: 1 year: 2021 end-page: 45 ident: bib56 article-title: A multidisciplinary survey and framework for design and evaluation of explainable Ai systems publication-title: ACM Transactions on Interactive Intelligent Systems – volume: 28 start-page: 84 year: 2013 end-page: 88 ident: bib32 article-title: Trust in automation publication-title: IEEE Intelligent Systems – reference: Preece, A., Harborne, D., Braines, D., Tomsett, R., & Chakraborty, S. (2018). Stakeholders in explainable AI. – reference: Heinrich, K., Janiesch, C., Möller, B., & Zschech, P. (2019). Is bigger always better? Lessons learnt from the evolution of deep learning architectures for image classification. Pre-ICIS SIGDSA Symposium, Munich, Germany. – reference: Wanner, J., Heinrich, K., Janiesch, C., & Zschech, P. (2020). How Much AI Do You Require? Decision Factors for Adopting AI Technology. 41st International Conference on Information Systems (ICIS), India. – year: 2013 ident: bib35 article-title: An introduction to statistical learning – volume: 1 year: 2019 ident: bib64 article-title: Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition publication-title: Harvard Data Science Review – year: 2021 ident: bib65 publication-title: Artificial intelligence: A modern approach – volume: 58 start-page: 82 year: 2020 end-page: 115 ident: bib4 article-title: Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI publication-title: Information Fusion – reference: Dam, H. K., Tran, T., & Ghose, A. (2018). Explainable software analytics. 40th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), Gothenburg. – volume: 65 year: 2022 ident: bib33 article-title: Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe publication-title: International Journal of Information Management – volume: 151 start-page: 90 year: 2019 end-page: 103 ident: bib44 article-title: Algorithm appreciation: People prefer algorithmic to human judgment publication-title: Organizational Behavior and Human Decision Processes – reference: Jussupow, E., Benbasat, I., & Heinzl, A. (2020). – volume: 7 start-page: 154096 year: 2019 end-page: 154113 ident: bib45 article-title: Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view publication-title: IEEE Access – volume: 40 start-page: 44 year: 2019 end-page: 58 ident: bib24 article-title: DARPA’s explainable artificial intelligence (XAI) program publication-title: AI Magazine – volume: 13 start-page: 1 year: 2021 end-page: 11 ident: bib12 article-title: Survey on SVM and their application in image classification publication-title: International Journal of Information Technology – volume: 291 year: 2021 ident: bib74 article-title: Evaluating XAI: A comparison of rule-based and example-based explanations publication-title: Artificial Intelligence – volume: 45 start-page: 315 year: 2021 end-page: 341 ident: bib6 article-title: The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts publication-title: MIS Quarterly – reference: Hoffman, R. R., Mueller, S. T., Klein, G., & Litman, J. (2018). Metrics for Explainable AI: Challenges and Prospects. – reference: Herm, L.-V., Wanner, J., Seubert, F., & Janiesch, C. (2021). – volume: 15 year: 2018 ident: bib59 article-title: Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study publication-title: PLoS Medicine – volume: 175 year: 2022 ident: bib47 article-title: What influences algorithmic decision-making? A systematic literature review on algorithm aversion publication-title: Technological Forecasting and Social Change – reference: La Cava, W., Williams, H., Fu, W., & Moore, J. H. (2019). Evaluating recommender systems for AI-driven data science. – volume: 9 start-page: 381 year: 2020 end-page: 386 ident: bib46 article-title: Machine learning algorithms-a review publication-title: International Journal of Science and Research (IJSR) – start-page: 1 year: 2022 end-page: 11 ident: bib53 article-title: Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities publication-title: Information Systems Management – volume: 60 year: 2021 ident: bib13 article-title: In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations publication-title: International Journal of Information Management – reference: Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should i trust you?" Explaining the predictions of any classifier. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA. – volume: 302 year: 2022 ident: bib57 article-title: The quest of parsimonious XAI: A human-agent architecture for explanation formulation publication-title: Artificial Intelligence – volume: 8 start-page: 223 year: 2007 end-page: 229 ident: bib70 article-title: Veni, vidi, vici: Breaking the TAM logjam publication-title: Journal of the Association for Information Systems – reference: UCI Machine Learning Library. Retrieved 10.10.2021 from – reference: Mignan, A., & Broccardo, M. (2019). A deeper look into ‘deep learning of aftershock patterns following large earthquakes’: Illustrating first principles in neural network physical interpretability. International Work-Conference on Artificial Neural Networks, Cham. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib43 article-title: Deep learning publication-title: Nature – reference: Das, A., & Rad, P. (2020). Opportunities and challenges in explainable artificial intelligence (xai): A survey. – volume: 109 start-page: 853 year: 2020 end-page: 898 ident: bib20 article-title: On cognitive preferences and the plausibility of rule-based models publication-title: Machine Learning – volume: 35 start-page: 2 year: 2022 ident: bib38 article-title: People prefer moral discretion to algorithms: Algorithm aversion beyond intransparency publication-title: Philosophy & Technology – reference: Meske, C., & Bunde, E. (2020). Transparency and trust in human-AI-interaction: The role of model-agnostic explanations in computer vision-based decision support. International Conference on Human-Computer Interaction, Virtual Conference. – reference: Janosi, A., Steinbrunn, W., Pfisterer, M., & Detrano, R. (1988). – volume: 48 start-page: 144 year: 2018 end-page: 156 ident: bib76 article-title: Deep learning for smart manufacturing: Methods and applications publication-title: Journal of Manufacturing Systems – volume: 1912 start-page: 02523 year: 2019 ident: bib3 article-title: Towards explainable deep neural networks (xDNN) publication-title: arXiv – volume: 36 start-page: 7 year: 2020 end-page: 14 ident: bib34 article-title: A brief history of forecasting competitions publication-title: International Journal of Forecasting – volume: 1 year: 2021 ident: bib67 article-title: Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer publication-title: International Journal of Information Management Data Insights – volume: 560 start-page: 632 year: 2018 end-page: 634 ident: bib16 article-title: Deep learning of aftershock patterns following large earthquakes publication-title: Nature – start-page: 1 year: 2022 end-page: 31 ident: bib52 article-title: Design principles for user interfaces in Ai-based decision support systems: The case of explainable hate speech detection publication-title: Information Systems Frontiers – volume: 31 start-page: 447 year: 2021 end-page: 464 ident: bib73 article-title: Trustworthy artificial intelligence publication-title: Electronic Markets – volume: 35 start-page: 89 year: 1992 end-page: 94 ident: bib72 article-title: A comparison of the decision table and tree publication-title: Communications of the ACM – volume: 54 start-page: 1 year: 2021 end-page: 35 ident: bib49 article-title: A survey on bias and fairness in machine learning publication-title: ACM Computing surveys (CSUR) – reference: Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., & Inkpen, K. (2019). Guidelines for human-AI interaction. 2019 CHI Conference on Human Factors in Computing Systems, Glasgow. – start-page: 245 year: 2021 end-page: 258 ident: bib79 article-title: Stop Ordering Machine Learning Algorithms by Their Explainability! An Empirical Investigation of the Tradeoff Between Performance and Explainability publication-title: 20th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society (I3E) – volume: 25 start-page: 1666 year: 2019 end-page: 1683 ident: bib82 article-title: Application of artificial intelligence in gastroenterology publication-title: World Journal of gastroenterology – volume: 146 year: 2021 ident: bib68 article-title: The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI publication-title: International Journal of Human-Computer Studies – volume: 267 start-page: 1 year: 2019 end-page: 38 ident: bib55 article-title: Explanation in artificial intelligence: Insights from the social sciences publication-title: Artificial Intelligence – reference: . – volume: 141 start-page: 61 year: 2021 end-page: 67 ident: bib77 article-title: Comparative analysis of image classification algorithms based on traditional machine learning and deep learning publication-title: Pattern Recognition Letters – year: 2006 ident: bib8 article-title: Pattern recognition and machine learning – volume: 50 start-page: 1 year: 2012 end-page: 5 ident: bib10 article-title: Analyzing likert data publication-title: Journal of Extension – volume: 52 year: 2020 ident: bib69 article-title: Beyond user experience: What constitutes algorithmic experiences? publication-title: International Journal of Information Management – reference: Guo, M., Zhang, Q., Liao, X., & Chen, Y. (2019). An interpretable machine learning framework for modelling human decision behavior. – year: 2018 ident: bib60 article-title: Comparing automatic and human evaluation of local explanations for text classification publication-title: 2018 Conference of the North American Chapter of the Association for Computational Linguistics – year: 2020 ident: bib9 article-title: Brain tumor publication-title: Kaggle – year: 2020 ident: bib18 article-title: Regulating Explainable AI in the European Union. An Overview of the Current Legal Framework(s) publication-title: Law and Informatics 2020: Law in the Era of Artificial Intelligence – volume: 1 start-page: 206 year: 2019 end-page: 215 ident: bib63 article-title: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead publication-title: Nature Machine Intelligence – volume: 2 start-page: 273 year: 1996 end-page: 308 ident: bib30 article-title: Mental models and causal explanation: Judgements of probable cause and explanatory relevance publication-title: Thinking & Reasoning – volume: 7 start-page: 154096 year: 2019 ident: 10.1016/j.ijinfomgt.2022.102538_bib45 article-title: Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2949286 – volume: 10 start-page: 593 issue: 5 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib84 article-title: Evaluating the quality of machine learning explanations: A survey on methods and metrics publication-title: Electronics doi: 10.3390/electronics10050593 – start-page: 1 year: 2022 ident: 10.1016/j.ijinfomgt.2022.102538_bib53 article-title: Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities publication-title: Information Systems Management – year: 2018 ident: 10.1016/j.ijinfomgt.2022.102538_bib60 article-title: Comparing automatic and human evaluation of local explanations for text classification – ident: 10.1016/j.ijinfomgt.2022.102538_bib81 – ident: 10.1016/j.ijinfomgt.2022.102538_bib37 – year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib9 article-title: Brain tumor publication-title: Kaggle – volume: 11 start-page: 1 issue: 3–4 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib56 article-title: A multidisciplinary survey and framework for design and evaluation of explainable Ai systems publication-title: ACM Transactions on Interactive Intelligent Systems doi: 10.1145/3387166 – volume: 31 start-page: 447 issue: 2 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib73 article-title: Trustworthy artificial intelligence publication-title: Electronic Markets doi: 10.1007/s12525-020-00441-4 – ident: 10.1016/j.ijinfomgt.2022.102538_bib54 doi: 10.1007/978-3-030-20521-8_1 – volume: 1 issue: 2 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib67 article-title: Turning the blackbox into a glassbox: An explainable machine learning approach for understanding hospitality customer publication-title: International Journal of Information Management Data Insights doi: 10.1016/j.jjimei.2021.100050 – volume: 560 start-page: 632 issue: 7720 year: 2018 ident: 10.1016/j.ijinfomgt.2022.102538_bib16 article-title: Deep learning of aftershock patterns following large earthquakes publication-title: Nature doi: 10.1038/s41586-018-0438-y – volume: 141 start-page: 61 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib77 article-title: Comparative analysis of image classification algorithms based on traditional machine learning and deep learning publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2020.07.042 – volume: 48 start-page: 144 year: 2018 ident: 10.1016/j.ijinfomgt.2022.102538_bib76 article-title: Deep learning for smart manufacturing: Methods and applications publication-title: Journal of Manufacturing Systems doi: 10.1016/j.jmsy.2018.01.003 – year: 2013 ident: 10.1016/j.ijinfomgt.2022.102538_bib35 – volume: 83 start-page: 9 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib11 article-title: AI applications to medical images: From machine learning to deep learning publication-title: Physica Medica doi: 10.1016/j.ejmp.2021.02.006 – ident: 10.1016/j.ijinfomgt.2022.102538_bib51 doi: 10.1007/978-3-030-50334-5_4 – volume: 1912 start-page: 02523 year: 2019 ident: 10.1016/j.ijinfomgt.2022.102538_bib3 article-title: Towards explainable deep neural networks (xDNN) publication-title: arXiv – volume: 51 start-page: 1 issue: 5 year: 2018 ident: 10.1016/j.ijinfomgt.2022.102538_bib23 article-title: A survey of methods for explaining black box models publication-title: ACM Computing surveys (CSUR) doi: 10.1145/3236009 – volume: 40 start-page: 44 issue: 2 year: 2019 ident: 10.1016/j.ijinfomgt.2022.102538_bib24 article-title: DARPA’s explainable artificial intelligence (XAI) program publication-title: AI Magazine doi: 10.1609/aimag.v40i2.2850 – volume: 577 start-page: 89 issue: 7788 year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib48 article-title: International evaluation of an AI system for breast cancer screening publication-title: Nature doi: 10.1038/s41586-019-1799-6 – volume: 151 start-page: 90 year: 2019 ident: 10.1016/j.ijinfomgt.2022.102538_bib44 article-title: Algorithm appreciation: People prefer algorithmic to human judgment publication-title: Organizational Behavior and Human Decision Processes doi: 10.1016/j.obhdp.2018.12.005 – volume: 13 start-page: 1 issue: 5 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib12 article-title: Survey on SVM and their application in image classification publication-title: International Journal of Information Technology doi: 10.1007/s41870-017-0080-1 – volume: 109 start-page: 853 issue: 4 year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib20 article-title: On cognitive preferences and the plausibility of rule-based models publication-title: Machine Learning doi: 10.1007/s10994-019-05856-5 – volume: 25 start-page: 289 issue: 4 year: 2017 ident: 10.1016/j.ijinfomgt.2022.102538_bib58 article-title: Utilizing big data analytics for information systems research: Challenges, promises and guidelines publication-title: European Journal of Information Systems doi: 10.1057/ejis.2016.2 – volume: 267 start-page: 1 year: 2019 ident: 10.1016/j.ijinfomgt.2022.102538_bib55 article-title: Explanation in artificial intelligence: Insights from the social sciences publication-title: Artificial Intelligence doi: 10.1016/j.artint.2018.07.007 – year: 2006 ident: 10.1016/j.ijinfomgt.2022.102538_bib8 – volume: 6 start-page: 52138 year: 2018 ident: 10.1016/j.ijinfomgt.2022.102538_bib1 article-title: Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI) publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2870052 – ident: 10.1016/j.ijinfomgt.2022.102538_bib28 – ident: 10.1016/j.ijinfomgt.2022.102538_bib50 doi: 10.1214/lnms/1196794933 – volume: 52 year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib69 article-title: Beyond user experience: What constitutes algorithmic experiences? publication-title: International Journal of Information Management doi: 10.1016/j.ijinfomgt.2019.102061 – ident: 10.1016/j.ijinfomgt.2022.102538_bib2 doi: 10.1145/3290605.3300233 – volume: 30 start-page: 5525 year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib71 article-title: Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors publication-title: European radiology doi: 10.1007/s00330-020-06946-y – year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib18 article-title: Regulating Explainable AI in the European Union. An Overview of the Current Legal Framework(s) – ident: 10.1016/j.ijinfomgt.2022.102538_bib25 – volume: 9 start-page: 381 issue: 1 year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib46 article-title: Machine learning algorithms-a review publication-title: International Journal of Science and Research (IJSR) doi: 10.21275/ART20203995 – volume: 58 start-page: 82 year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib4 article-title: Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI publication-title: Information Fusion doi: 10.1016/j.inffus.2019.12.012 – start-page: 245 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib79 article-title: Stop Ordering Machine Learning Algorithms by Their Explainability! An Empirical Investigation of the Tradeoff Between Performance and Explainability – volume: 57 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib17 article-title: Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy publication-title: International Journal of Information Management doi: 10.1016/j.ijinfomgt.2019.08.002 – volume: 175 year: 2022 ident: 10.1016/j.ijinfomgt.2022.102538_bib47 article-title: What influences algorithmic decision-making? A systematic literature review on algorithm aversion publication-title: Technological Forecasting and Social Change doi: 10.1016/j.techfore.2021.121390 – volume: 36 start-page: 7 issue: 1 year: 2020 ident: 10.1016/j.ijinfomgt.2022.102538_bib34 article-title: A brief history of forecasting competitions publication-title: International Journal of Forecasting doi: 10.1016/j.ijforecast.2019.03.015 – volume: 4 start-page: 25 issue: 1 year: 2018 ident: 10.1016/j.ijinfomgt.2022.102538_bib83 article-title: A strategy to apply machine learning to small datasets in materials science publication-title: npj Computational Materials doi: 10.1038/s41524-018-0081-z – ident: 10.1016/j.ijinfomgt.2022.102538_bib31 – ident: 10.1016/j.ijinfomgt.2022.102538_bib15 doi: 10.1145/3183399.3183424 – year: 2016 ident: 10.1016/j.ijinfomgt.2022.102538_bib21 – ident: 10.1016/j.ijinfomgt.2022.102538_bib29 – start-page: 1 year: 2022 ident: 10.1016/j.ijinfomgt.2022.102538_bib52 article-title: Design principles for user interfaces in Ai-based decision support systems: The case of explainable hate speech detection publication-title: Information Systems Frontiers – volume: 35 start-page: 89 issue: 1 year: 1992 ident: 10.1016/j.ijinfomgt.2022.102538_bib72 article-title: A comparison of the decision table and tree publication-title: Communications of the ACM doi: 10.1145/129617.129621 – volume: 1 start-page: 206 issue: 5 year: 2019 ident: 10.1016/j.ijinfomgt.2022.102538_bib63 article-title: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead publication-title: Nature Machine Intelligence doi: 10.1038/s42256-019-0048-x – volume: 45 start-page: 315 issue: 1 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib6 article-title: The next generation of research on IS use: A theoretical framework of delegation to and from agentic IS artifacts publication-title: MIS Quarterly doi: 10.25300/MISQ/2021/15882 – volume: 2 start-page: 273 issue: 4 year: 1996 ident: 10.1016/j.ijinfomgt.2022.102538_bib30 article-title: Mental models and causal explanation: Judgements of probable cause and explanatory relevance publication-title: Thinking & Reasoning doi: 10.1080/135467896394447 – volume: 50 start-page: 1 issue: 2 year: 2012 ident: 10.1016/j.ijinfomgt.2022.102538_bib10 article-title: Analyzing likert data publication-title: Journal of Extension doi: 10.34068/joe.50.02.48 – volume: 60 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib13 article-title: In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations publication-title: International Journal of Information Management doi: 10.1016/j.ijinfomgt.2021.102379 – volume: 65 year: 2022 ident: 10.1016/j.ijinfomgt.2022.102538_bib33 article-title: Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe publication-title: International Journal of Information Management doi: 10.1016/j.ijinfomgt.2022.102497 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.ijinfomgt.2022.102538_bib43 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 31 start-page: 685 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib36 article-title: Machine learning and deep learning publication-title: Electronic Markets doi: 10.1007/s12525-021-00475-2 – ident: 10.1016/j.ijinfomgt.2022.102538_bib26 – ident: 10.1016/j.ijinfomgt.2022.102538_bib41 – volume: 61 start-page: 85 year: 2015 ident: 10.1016/j.ijinfomgt.2022.102538_bib66 article-title: Deep learning in neural networks: An overview publication-title: Neural Networks doi: 10.1016/j.neunet.2014.09.003 – volume: 25 start-page: 1666 issue: 14 year: 2019 ident: 10.1016/j.ijinfomgt.2022.102538_bib82 article-title: Application of artificial intelligence in gastroenterology publication-title: World Journal of gastroenterology doi: 10.3748/wjg.v25.i14.1666 – ident: 10.1016/j.ijinfomgt.2022.102538_bib78 – volume: 63 start-page: 55 issue: 1 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib7 article-title: Watch me improve—Algorithm aversion and demonstrating the ability to learn publication-title: Business & Information Systems Engineering doi: 10.1007/s12599-020-00678-5 – volume: 1 issue: 2 year: 2019 ident: 10.1016/j.ijinfomgt.2022.102538_bib64 article-title: Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition publication-title: Harvard Data Science Review doi: 10.1162/99608f92.5a8a3a3d – volume: 8 start-page: 223 issue: 4 year: 2007 ident: 10.1016/j.ijinfomgt.2022.102538_bib70 article-title: Veni, vidi, vici: Breaking the TAM logjam publication-title: Journal of the Association for Information Systems doi: 10.17705/1jais.00124 – volume: 60 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib14 article-title: Artificial intelligence in information systems research: A systematic literature review and research agenda publication-title: International Journal of Information Management doi: 10.1016/j.ijinfomgt.2021.102383 – ident: 10.1016/j.ijinfomgt.2022.102538_bib61 – volume: 35 start-page: 2 issue: 1 year: 2022 ident: 10.1016/j.ijinfomgt.2022.102538_bib38 article-title: People prefer moral discretion to algorithms: Algorithm aversion beyond intransparency publication-title: Philosophy & Technology doi: 10.1007/s13347-021-00495-y – volume: 54 start-page: 1 issue: 6 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib49 article-title: A survey on bias and fairness in machine learning publication-title: ACM Computing surveys (CSUR) doi: 10.1145/3457607 – volume: 22 start-page: 325 issue: 2 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib5 article-title: Sociotechnical envelopment of artificial intelligence: An approach to organizational deployment of inscrutable artificial intelligence systems publication-title: Journal of the Association for Information Systems doi: 10.17705/1jais.00664 – volume: 146 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib68 article-title: The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI publication-title: International Journal of Human-Computer Studies doi: 10.1016/j.ijhcs.2020.102551 – start-page: 1607 issue: 34 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib19 article-title: Transparency and the black box problem: Why we do not trust AI publication-title: Philosophy & Technology doi: 10.1007/s13347-021-00477-0 – volume: 8 start-page: 2239 year: 2018 ident: 10.1016/j.ijinfomgt.2022.102538_bib75 article-title: Modeling music emotion judgments using machine learning methods publication-title: Frontiers in Psychology doi: 10.3389/fpsyg.2017.02239 – volume: 291 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib74 article-title: Evaluating XAI: A comparison of rule-based and example-based explanations publication-title: Artificial Intelligence doi: 10.1016/j.artint.2020.103404 – volume: 45 start-page: 1501 issue: 3b year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib42 article-title: Is AI ground truth really “true”? The dangers of training and evaluating AI tools based on experts’ know-what publication-title: Management Information Systems Quarterly doi: 10.25300/MISQ/2021/16564 – ident: 10.1016/j.ijinfomgt.2022.102538_bib62 doi: 10.1145/2939672.2939778 – volume: 5 start-page: 29 issue: 1 year: 2022 ident: 10.1016/j.ijinfomgt.2022.102538_bib80 article-title: A social evaluation of the perceived goodness of explainability in machine learning publication-title: Journal of Business Analytics doi: 10.1080/2573234X.2021.1952913 – year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib65 – ident: 10.1016/j.ijinfomgt.2022.102538_bib27 – volume: 28 start-page: 84 issue: 1 year: 2013 ident: 10.1016/j.ijinfomgt.2022.102538_bib32 article-title: Trust in automation publication-title: IEEE Intelligent Systems doi: 10.1109/MIS.2013.24 – volume: 294 year: 2021 ident: 10.1016/j.ijinfomgt.2022.102538_bib39 article-title: Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies publication-title: Artificial Intelligence doi: 10.1016/j.artint.2021.103459 – ident: 10.1016/j.ijinfomgt.2022.102538_bib40 – volume: 302 year: 2022 ident: 10.1016/j.ijinfomgt.2022.102538_bib57 article-title: The quest of parsimonious XAI: A human-agent architecture for explanation formulation publication-title: Artificial Intelligence doi: 10.1016/j.artint.2021.103573 – volume: 38 start-page: 50 issue: 3 year: 2017 ident: 10.1016/j.ijinfomgt.2022.102538_bib22 article-title: European union regulations on algorithmic decision-making and a “right to explanation” publication-title: AI Magazine doi: 10.1609/aimag.v38i3.2741 – volume: 15 issue: 11 year: 2018 ident: 10.1016/j.ijinfomgt.2022.102538_bib59 article-title: Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study publication-title: PLoS Medicine doi: 10.1371/journal.pmed.1002709 |
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