short-survey

Explainable Artificial Intelligence (XAI) 2.0: : A manifesto of open challenges and interdisciplinary research directions

Volume 106, Issue C

https://doi.org/10.1016/j.inffus.2024.102301

Published: 25 June 2024 Publication History

Abstract

Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. We aim to develop a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 28 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders.

References

[1]

Swartout W., Paris C., Moore J., Explanations in knowledge systems: Design for explainable expert systems, IEEE Expert 6 (3) (1991) 58–64.

[2]

Paris C.L., Generation and explanation: Building an explanation facility for the explainable expert systems framework, in: Natural Language Generation in Artificial Intelligence and Computational Linguistics, Springer, 1991, pp. 49–82.

[3]

Confalonieri R., Coba L., Wagner B., Besold T.R., A historical perspective of explainable Artificial Intelligence, WIREs Data Min. Knowl. Discov. 11 (1) (2021),. URL https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1391.

[4]

Speith T., A review of taxonomies of explainable artificial intelligence (XAI) methods, in: Isbell C., Lazar S., Oh A., Xiang A. (Eds.), Proceedings of the 5th ACM Conference on Fairness, Accountability, and Transparency, Association for Computing Machinery, New York, NY, USA, 2022, pp. 2239–2250,.

Digital Library

[5]

Schwalbe G., Finzel B., A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts, Data Min. Knowl. Discov. (2023) 1–59.

[6]

Langer M., Oster D., Speith T., Hermanns H., Kästner L., Schmidt E., Sesing A., Baum K., What do we want from Explainable Artificial Intelligence (XAI)?–A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research, Artificial Intelligence 296 (2021).

[7]

Langer M., Baum K., Hartmann K., Hessel S., Speith T., Wahl J., Explainability auditing for intelligent systems: A rationale for multi-disciplinary perspectives, in: Yue T., Mirakhorli M. (Eds.), 29th IEEE International Requirements Engineering Conference Workshops, in: REW 2021, IEEE, Piscataway, NJ, USA, 2021, pp. 164–168,.

[8]

Ali S., Abuhmed T., El-Sappagh S., Muhammad K., Alonso-Moral J.M., Confalonieri R., Guidotti R., Del Ser J., Díaz-Rodríguez N., Herrera F., Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence, Inf. Fusion (2023).

[9]

Cao L., Ai in finance: challenges, techniques, and opportunities, ACM Comput. Surv. 55 (3) (2022) 1–38.

[10]

Caruana R., Lou Y., Gehrke J., Koch P., Sturm M., Elhadad N., Intelligible models for HealthCare: Predicting pneumonia risk and hospital 30-day readmission, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, Association for Computing Machinery, New York, NY, USA, 2015, pp. 1721–1730,.

Digital Library

[11]

AI High-Level Expert Group R., Ethics guidelines for trustworthy AI, 2019, B-1049 Brussels. URL https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.

[12]

Freiesleben T., König G., Dear XAI community, we need to talk!, in: Longo L. (Ed.), Explainable Artificial Intelligence, Springer Nature Switzerland, Cham, 2023, pp. 48–65.

[13]

Kästner L., Langer M., Lazar V., Schomäcker A., Speith T., Sterz S., On the relation of trust and explainability: Why to engineer for trustworthiness, in: Yue T., Mirakhorli M. (Eds.), 29th IEEE International Requirements Engineering Conference Workshops, in: REW 2021, IEEE, Piscataway, NJ, USA, 2021, pp. 169–175,.

[14]

Papenmeier A., Englebienne G., Seifert C., How model accuracy and explanation fidelity influence user trust, 2019, arXiv preprint arXiv:1907.12652.

[15]

Huang X., Marques-Silva J., From robustness to explainability and back again, 2023, arXiv preprint arXiv:2306.03048.

[16]

Marques-Silva J., Huang X., Explainability is NOT a game, 2023, arXiv preprint arXiv:2307.07514.

[17]

Miller T., Howe P., Sonenberg L., Explainable AI: Beware of inmates running the asylum. or: How I learnt to stop worrying and love the social and behavioural sciences, in: Aha D.W., Darrell T., Pazzani M., Reid D., Sammut C., Stone P. (Eds.), Proceedings of the IJCAI 2017 Workshop on Explainable Artificial Intelligence, IJCAI, Santa Clara County, CA, USA, 2017, pp. 36–42. arXiv:1712.00547.

[18]

Barredo Arrieta A., Díaz-Rodríguez N., Del Ser J., Bennetot A., Tabik S., Barbado A., Garcia S., Gil-Lopez S., Molina D., Benjamins R., Chatila R., Herrera F., Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI, Inf. Fusion 58 (2020) 82–115,. URL https://www.sciencedirect.com/science/article/pii/S1566253519308103.

Digital Library

[19]

Haresamudram K., Larsson S., Heintz F., Three levels of AI transparency, Computer 56 (2) (2023) 93–100,.

[20]

Zerilli J., Explaining machine learning decisions, Philos. Sci. 89 (1) (2022) 1–19.

[21]

Chazette L., Schneider K., Explainability as a non-functional requirement: challenges and recommendations, Requir. Eng. 25 (4) (2020) 493–514,.

Digital Library

[22]

Köhl M.A., Baum K., Bohlender D., Langer M., Oster D., Speith T., Explainability as a non-functional requirement, in: Damian D.E., Perini A., Lee S. (Eds.), IEEE 27th International Requirements Engineering Conference, in: RE 2019, IEEE, Piscataway, NJ, USA, 2019, pp. 363–368,.

[23]

Miller T., Explanation in artificial intelligence: Insights from the social sciences, Artificial Intelligence 267 (2019) 1–38,.

[24]

Páez A., The pragmatic turn in explainable artificial intelligence (XAI), Minds Mach. 29 (3) (2019) 441–459,.

Digital Library

[25]

Bruckert S., Finzel B., Schmid U., The next generation of medical decision support: A roadmap toward transparent expert companions, Frontiers Artificial Intelligence 3 (2020).

[26]

Arya V., Bellamy R.K.E., Chen P.-Y., Dhurandhar A., Hind M., Hoffman S.C., Houde S., Liao Q.V., Luss R., Mojsilović A., Mourad S., Pedemonte P., Raghavendra R., Richards J., Sattigeri P., Shanmugam K., Singh M., Varshney K.R., Wei D., Zhang Y., One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques, 2021, arXiv:1909.03012.

[27]

Vilone G., Longo L., Notions of explainability and evaluation approaches for explainable artificial intelligence, Inf. Fusion 76 (2021) 89–106.

Digital Library

[28]

Sokol K., Flach P., Explainability fact sheets: A framework for systematic assessment of explainable approaches, in: Hildebrandt M., Castillo C., Celis L.E., Ruggieri S., Taylor L., Zanfir-Fortuna G. (Eds.), Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, in: FAT* 2020, Association for Computing Machinery, New York, NY, USA, 2020, pp. 56–67,.

Digital Library

[29]

Chazette L., Brunotte W., Speith T., Exploring explainability: A definition, a model, and a knowledge catalogue, in: Cleland-Huang J., Moreira A., Schneider K., Vierhauser M. (Eds.), IEEE 29th International Requirements Engineering Conference, in: RE 2021, IEEE, Piscataway, NJ, USA, 2021, pp. 197–208,.

[30]

Weber L., Lapuschkin S., Binder A., Samek W., Beyond explaining: Opportunities and challenges of XAI-based model improvement, Inf. Fusion 92 (2023) 154–176.

[31]

Bodria F., Giannotti F., Guidotti R., Naretto F., Pedreschi D., Rinzivillo S., Benchmarking and survey of explanation methods for black box models, Data Min. Knowl. Discov. (2023) 1–60.

[32]

Guidotti R., Counterfactual explanations and how to find them: literature review and benchmarking, Data Min. Knowl. Discov. (2022) 1–55.

[33]

Machlev R., Heistrene L., Perl M., Levy K., Belikov J., Mannor S., Levron Y., Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities, Energy AI 9 (2022).

[34]

Mei Y., Chen Q., Lensen A., Xue B., Zhang M., Explainable artificial intelligence by genetic programming: A survey, IEEE Trans. Evol. Comput. 27 (3) (2023) 621–641,.

Digital Library

[35]

Minh D., Wang H.X., Li Y.F., Nguyen T.N., Explainable artificial intelligence: a comprehensive review, Artif. Intell. Rev. (2022) 1–66.

[36]

Theissler A., Spinnato F., Schlegel U., Guidotti R., Explainable AI for time series classification: A review, taxonomy and research directions, IEEE Access 10 (2022) 100700–100724,.

[37]

Yang G., Ye Q., Xia J., Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond, Inf. Fusion 77 (2022) 29–52.

Digital Library

[38]

Zini J.E., Awad M., On the explainability of natural language processing deep models, ACM Comput. Surv. 55 (5) (2022) 1–31.

[39]

Antoniadi A.M., Du Y., Guendouz Y., Wei L., Mazo C., Becker B.A., Mooney C., Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review, Appl. Sci. 11 (11) (2021) 5088.

[40]

Heuillet A., Couthouis F., Díaz-Rodríguez N., Explainability in deep reinforcement learning, Knowl.-Based Syst. 214 (2021).

[41]

Markus A.F., Kors J.A., Rijnbeek P.R., The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies, J. Biomed. Inform. 113 (2021) 11,.

Digital Library

[42]

Mohseni S., Zarei N., Ragan E.D., A multidisciplinary survey and framework for design and evaluation of explainable AI systems, ACM Trans. Interact. Intell. Syst. (TiiS) 11 (3–4) (2021) 1–45.

[43]

Rojat T., Puget R., Filliat D., Del Ser J., Gelin R., Díaz-Rodríguez N., Explainable artificial intelligence (xai) on timeseries data: A survey, 2021, arXiv preprint arXiv:2104.00950.

[44]

Samek W., Montavon G., Lapuschkin S., Anders C.J., Müller K.-R., Explaining deep neural networks and beyond: A review of methods and applications, Proc. IEEE 109 (3) (2021) 247–278.

[45]

Vilone G., Longo L., Classification of explainable artificial intelligence methods through their output formats, Mach. Learn. Knowl. Extr. 3 (3) (2021) 615–661,. URL https://www.mdpi.com/2504-4990/3/3/32.

[46]

Zhou J., Gandomi A.H., Chen F., Holzinger A., Evaluating the quality of machine learning explanations: A survey on methods and metrics, Electronics 10 (5) (2021) 593.

[47]

Tjoa E., Guan C., A survey on explainable artificial intelligence (xai): Toward medical xai, IEEE Trans. Neural Netw. Learn. Syst. 32 (11) (2020) 4793–4813.

[48]

Carvalho D.V., Pereira E.M., Cardoso J.S., Machine learning interpretability: A survey on methods and metrics, Electronics 8 (8) (2019) 832.

[49]

Adadi A., Berrada M., Peeking inside the black-box: a survey on explainable artificial intelligence (XAI), IEEE Access 6 (2018) 52138–52160.

[50]

Guidotti R., Monreale A., Ruggieri S., Turini F., Giannotti F., Pedreschi D., A survey of methods for explaining black box models, ACM Comput. Surv. 51 (5) (2018) 1–42,.

Digital Library

[51]

Rawal A., McCoy J., Rawat D.B., Sadler B.M., Amant R.S., Recent advances in trustworthy explainable artificial intelligence: Status, challenges, and perspectives, IEEE Trans. Artif. Intell. 3 (6) (2021) 852–866.

[52]

Hinder F., Hammer B., Counterfactual explanations of concept drift, 2020, arXiv preprint arXiv:2006.12822.

[53]

Khan A., Thij M.t., Wilbik A., Vertical federated learning: A structured literature review, 2022, arXiv preprint arXiv:2212.00622.

[54]

Yeom S.-K., Seegerer P., Lapuschkin S., Binder A., Wiedemann S., Müller K.-R., Samek W., Pruning by explaining: A novel criterion for deep neural network pruning, Pattern Recognit. 115 (2021).

[55]

Ribeiro M.T., Singh S., Guestrin C., ”Why should I trust you?”: Explaining the predictions of any classifier, in: Aggarwal C., Krishnapuram B., Rastogi R., Shen D., Shah M., Smola A. (Eds.), Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, in: KDD 2016, Association for Computing Machinery, New York, NY, USA, 2016, pp. 1135–1144,.

Digital Library

[56]

Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Guyon I., Luxburg U.V., Bengio S., Wallach H., Fergus R., Vishwanathan S., Garnett R. (Eds.), Advances in Neural Information Processing Systems, vol. 30, Curran Associates, Inc., 2017, pp. 4768–4777. URL https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf.

[57]

Garreau D., Luxburg U., Explaining the explainer: A first theoretical analysis of LIME, in: International Conference on Artificial Intelligence and Statistics, PMLR, 2020, pp. 1287–1296.

[58]

D. Slack, S. Hilgard, E. Jia, S. Singh, H. Lakkaraju, Fooling lime and shap: Adversarial attacks on post hoc explanation methods, in: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2020, pp. 180–186.

[59]

Lapuschkin S., Wäldchen S., Binder A., Montavon G., Samek W., Müller K.-R., Unmasking Clever Hans predictors and assessing what machines really learn, Nature Commun. 10 (1) (2019) 1096.

[60]

Geirhos R., Jacobsen J.-H., Michaelis C., Zemel R., Brendel W., Bethge M., Wichmann F.A., Shortcut learning in deep neural networks, Nat. Mach. Intell. 2 (11) (2020) 665–673,.

[61]

Speith T., How to evaluate explainability – a case for three criteria, in: Knauss E., Mussbacher G., Arora C., Bano M., Schneider J.-G. (Eds.), Proceedings of the 30th IEEE International Requirements Engineering Conference Workshops, in: REW 2022, IEEE, Piscataway, NJ, USA, 2022, pp. 92–97,.

[62]

S. Lapuschkin, A. Binder, K.-R. Müller, W. Samek, Understanding and comparing deep neural networks for age and gender classification, in: Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017, pp. 1629–1638.

[63]

Grinsztajn L., Oyallon E., Varoquaux G., Why do tree-based models still outperform deep learning on typical tabular data?, Koyejo S., Mohamed S., Agarwal A., Belgrave D., Cho K., Oh A. (Eds.), Advances in Neural Information Processing Systems, vol. 35, Curran Associates, Inc., 2022, pp. 507–520. URL https://proceedings.neurips.cc/paper_files/paper/2022/file/0378c7692da36807bdec87ab043cdadc-Paper-Datasets_and_Benchmarks.pdf.

[64]

Rudin C., Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell. 1 (5) (2019) 206–215.

[65]

Crook B., Schlüter M., Speith T., Revisiting the performance-explainability trade-off in explainable artificial intelligence (XAI), in: Dalpiaz F., Horkoff J., Schneider K. (Eds.), Proceedings of the 31st IEEE International Requirements Engineering Conference Workshops, IEEE, Piscataway, NJ, USA, 2023, pp. 316–324.

[66]

Rokach L., Decision forest: Twenty years of research, Inf. Fusion 27 (2016) 111–125.

Digital Library

[67]

Hatwell J., Gaber M.M., Azad R.M.A., CHIRPS: Explaining random forest classification, Artif. Intell. Rev. 53 (2020) 5747–5788.

[68]

Fürnkranz J., Kliegr T., Paulheim H., On cognitive preferences and the plausibility of rule-based models, Mach. Learn. 109 (4) (2020) 853–898.

[69]

Krakauer D.C., Unifying complexity science and machine learning, Front. Complex Syst. 1 (2023),. URL https://www.frontiersin.org/articles/10.3389/fcpxs.2023.1235202.

[70]

Fernandez A., Herrera F., Cordon O., del Jesus M.J., Marcelloni F., Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?, IEEE Comput. Intell. Mag. 14 (1) (2019) 69–81.

Digital Library

[71]

Huang X., Marques-Silva J., From decision trees to explained decision sets, in: 26th European Conference on Artificial Intelligence, Vol. 372, ECAI 2023, IOS Press, 2023, pp. 1100–1108.

[72]

Huang X., Khetan A., Cvitkovic M., Karnin Z., Tabtransformer: Tabular data modeling using contextual embeddings, 2020, arXiv preprint arXiv:2012.06678.

[73]

Gorishniy Y., Rubachev I., Khrulkov V., Babenko A., Revisiting deep learning models for tabular data, Adv. Neural Inf. Process. Syst. 34 (2021) 18932–18943.

[74]

S.Ö. Arik, T. Pfister, Tabnet: Attentive interpretable tabular learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 6679–6687, (8).

[75]

Abnar S., Zuidema W., Quantifying attention flow in transformers, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, 2020, pp. 4190–4197,. URL https://aclanthology.org/2020.acl-main.385.

[76]

Ali A., Schnake T., Eberle O., Montavon G., Müller K.-R., Wolf L., XAI for transformers: Better explanations through conservative propagation, in: International Conference on Machine Learning, PMLR, 2022, pp. 435–451.

[77]

Deb M., Deiseroth B., Weinbach S., Schramowski P., Kersting K., AtMan: Understanding transformer predictions through memory efficient attention manipulation, 2023, arXiv preprint arXiv:2301.08110.

[78]

Reduan Achtibat S.M.V.H., Dreyer M., Jain A., Wiegand T., Lapuschkin S., Samek W., Attnlrp: attention-aware layer-wise relevance propagation for transformers, arXiv:2402.05602 (2024) https://arxiv.org/abs/2402.05602.

[79]

Lécué F., On the role of knowledge graphs in explainable AI, Semant. Web 11 (1) (2020) 41–51,.

Digital Library

[80]

Speith T., Langer M., A new perspective on evaluation methods for explainable artificial intelligence (XAI), in: Dalpiaz F., Horkoff J., Schneider K. (Eds.), Proceedings of the 31st IEEE International Requirements Engineering Conference Workshops, IEEE, Piscataway, NJ, USA, 2023, pp. 325–331.

[81]

Čyras K., Rago A., Albini E., Baroni P., Toni F., Argumentative XAI: A survey, in: Zhou Z.-H. (Ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, International Joint Conferences on Artificial Intelligence Organization, 2021, pp. 4392–4399,. Survey Track.

[82]

Baum K., Hermanns H., Speith T., From machine ethics to machine explainability and back, in: Charles M., Diochnos D.I., Dix J., Hoffman F., Simari G.R. (Eds.), International Symposium on Artificial Intelligence and Mathematics, International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, FL, USA, 2018, pp. 1–8.

[83]

Baum K., Hermanns H., Speith T., Towards a framework combining machine ethics and machine explainability, in: Finkbeiner B., Kleinberg S. (Eds.), Proceedings of the 3rd Workshop on Formal Reasoning about Causation, Responsibility, and Explanations in Science and Technology, Electronic Proceedings in Theoretical Computer Science, Sydney, NSW, AU, 2018, pp. 34–49,.

[84]

Vassiliades A., Bassiliades N., Patkos T., Argumentation and explainable artificial intelligence: a survey, Knowl. Eng. Rev. 36 (2021).

[85]

Longo L., Argumentation for knowledge representation, conflict resolution, defeasible inference and its integration with machine learning, in: Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, Springer, 2016, pp. 183–208.

[86]

Zeng Z., Miao C., Leung C., Chin J.J., Building more explainable artificial intelligence with argumentation, in: McIlraith S.A., Weinberger K.Q. (Eds.), Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence Conference, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI Press, Palo Alto, CA, USA, 2018, pp. 8044–8046,.

[87]

Baroni P., Caminada M., Giacomin M., An introduction to argumentation semantics, Knowl. Eng. Rev. 26 (4) (2011) 365–410.

Digital Library

[88]

L. Rizzo, L. Longo, Inferential Models of Mental Workload with Defeasible Argumentation and Non-monotonic Fuzzy Reasoning: a Comparative Study, in: Proceedings of the 2nd Workshop on Advances in Argumentation in Artificial Intelligence, Co-Located with XVII International Conference of the Italian Association for Artificial Intelligence, AI3@AI*IA 2018, 20-23 November 2018, Trento, Italy, 2018, pp. 11–26.

[89]

Rizzo L., Majnaric L., Longo L., A comparative study of defeasible argumentation and non-monotonic fuzzy reasoning for elderly survival prediction using biomarkers, in: AI* IA 2018–Advances in Artificial Intelligence: XVIIth International Conference of the Italian Association for Artificial Intelligence, Trento, Italy, November 20–23, 2018, Proceedings 17, Springer, 2018, pp. 197–209.

[90]

Longo L., Rizzo L., Dondio P., Examining the modelling capabilities of defeasible argumentation and non-monotonic fuzzy reasoning, Knowl.-Based Syst. 211 (2021).

[91]

S Band S., Yarahmadi A., Hsu C.-C., Biyari M., Sookhak M., Ameri R., Dehzangi I., Chronopoulos A.T., Liang H.-W., Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods, Inform. Med. Unlocked 40 (2023),. URL https://www.sciencedirect.com/science/article/pii/S2352914823001302.

[92]

Tschandl P., Codella N., Akay B.N., Argenziano G., Braun R.P., Cabo H., Gutman D., Halpern A., Helba B., Hofmann-Wellenhof R., et al., Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study, Lancet Oncol. 20 (7) (2019) 938–947.

[93]

Amann J., Blasimme A., Vayena E., Frey D., Madai V.I., Explainability for artificial intelligence in healthcare: a multidisciplinary perspective, BMC Med. Inform. Decis. Mak. 20 (1) (2020) 1–9.

[94]

Coalition for Health AI (CHAI) J., Blueprint for trustworthy AI implementation guidance and assurance for healthcare, 2023, URL https://www.coalitionforhealthai.org/papers/Blueprint%20for%20Trustworthy%20AI.pdf.

[95]

Han T., Srinivas S., Lakkaraju H., Which explanation should I choose? A function approximation perspective to characterizing post hoc explanations, Adv. Neural Inf. Process. Syst. 35 (2020) URL https://par.nsf.gov/biblio/10396110.

[96]

Agarwal C., Krishna S., Saxena E., Pawelczyk M., Johnson N., Puri I., Zitnik M., Lakkaraju H., OpenXAI: Towards a transparent evaluation of model explanations, Koyejo S., Mohamed S., Agarwal A., Belgrave D., Cho K., Oh A. (Eds.), Advances in Neural Information Processing Systems, vol. 35, Curran Associates, Inc., 2022, pp. 15784–15799.

[97]

Bussmann N., Giudici P., Marinelli D., Papenbrock J., Explainable machine learning in credit risk management, Comput. Econ. 57 (2021) 203–216.

[98]

Sachan S., Yang J.-B., Xu D.-L., Benavides D.E., Li Y., An explainable AI decision-support-system to automate loan underwriting, Expert Syst. Appl. 144 (2020).

[99]

Rudin C., Radin J., Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition, Harv. Data Sci. Rev. 1 (2) (2019) 10–1162.

[100]

Mishra S., Dutta S., Long J., Magazzeni D., A survey on the robustness of feature importance and counterfactual explanations, 2021, arXiv preprint arXiv:2111.00358.

[101]

Sharma S., Dhal S., Rout T., Acharya B.S., Drones and machine learning for estimating forest carbon storage, Carbon Res. 1 (1) (2022) 21.

[102]

Möllmann T.B., Möhring B., A practical way to integrate risk in forest management decisions, Ann. For. Sci. 74 (2017) 1–12.

[103]

Gollob C., Ritter T., Nothdurft A., Forest inventory with long range and high-speed personal laser scanning (PLS) and simultaneous localization and mapping (SLAM) technology, Remote Sens. 12 (9) (2020) 1509.

[104]

Holzinger A., Saranti A., Angerschmid A., Retzlaff C.O., Gronauer A., Pejakovic V., Medel-Jimenez F., Krexner T., Gollob C., Stampfer K., Digital transformation in smart farm and forest operations needs human-centered AI: challenges and future directions, Sensors 22 (8) (2022) 3043.

[105]

Holzinger A., Stampfer K., Nothdurft A., Gollob C., Kieseberg P., Challenges in Artificial Intelligence for Smart Forestry, Eur. Res. Consort. Informatics Math.(ERCIM) News, 2022, pp. 40–41.

[106]

Holzinger A., The next frontier: AI we can really trust, in: Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I, Springer, 2022, pp. 427–440.

[107]

Holzinger A., Dehmer M., Emmert-Streib F., Cucchiara R., Augenstein I., Del Ser J., Samek W., Jurisica I., Díaz-Rodríguez N., Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence, Inf. Fusion 79 (2022) 263–278.

Digital Library

[108]

Luckin R., Holmes W., Griffiths M., Forcier L.B., Intelligence Unleashed: An Argument for AI in Education, The Open University, 2016.

[109]

Zawacki-Richter O., Marín V.I., Bond M., Gouverneur F., Systematic review of research on artificial intelligence applications in higher education–where are the educators?, Int. J. Educ. Technol. High. Educ. 16 (1) (2019) 1–27.

[110]

Longo L., Empowering qualitative research methods in education with artificial intelligence, in: Costa A.P., Reis L.P., Moreira A. (Eds.), Computer Supported Qualitative Research, Springer International Publishing, Cham, 2020, pp. 1–21.

[111]

Desmarais M.C., Baker R.S.d., A review of recent advances in learner and skill modeling in intelligent learning environments, User Model. User-Adapt. Interact. 22 (2012) 9–38.

[112]

VanLehn K., The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems, Educ. Psychol. 46 (4) (2011) 197–221.

[113]

Bull S., There are open learner models about!, IEEE Trans. Learn. Technol. 13 (2) (2020) 425–448.

[114]

(du) B.B., Artificial intelligence as an effective classroom assistant, IEEE Intell. Syst. 31 (6) (2016) 76–81.

[115]

Holstein K., McLaren B.M., Aleven V., Co-designing a real-time classroom orchestration tool to support teacher-AI complementarity, Grantee Submiss. (2019).

[116]

A. Singh, S. Karayev, K. Gutowski, P. Abbeel, Gradescope: a fast, flexible, and fair system for scalable assessment of handwritten work, in: Proceedings of the Fourth (2017) Acm Conference on Learning@ Scale, 2017, pp. 81–88.

[117]

Hiremath G., Hajare A., Bhosale P., Nanaware R., Wagh K., Chatbot for education system, Int. J. Adv. Res. Ideas Innov. Technol. 4 (3) (2018) 37–43.

[118]

Liz-Domínguez M., Caeiro-Rodríguez M., Llamas-Nistal M., Mikic-Fonte F.A., Systematic literature review of predictive analysis tools in higher education, Appl. Sci. 9 (24) (2019) 5569.

[119]

Khosravi H., Kitto K., Joseph W., RiPPLE: A crowdsourced adaptive platform for recommendation of learning activities, J. Learn. Anal. 6 (3) (2019) 91–105.

[120]

Holmes W., Porayska-Pomsta K., Holstein K., Sutherland E., Baker T., Shum S.B., Santos O.C., Rodrigo M.T., Cukurova M., Bittencourt I.I., et al., Ethics of AI in education: Towards a community-wide framework, Int. J. Artif. Intell. Educ. (2021) 1–23.

[121]

Baker R.S., Hawn A., Algorithmic bias in education, Int. J. Artif. Intell. Educ. (2021) 1–41.

[122]

Kizilcec R.F., Lee H., Algorithmic fairness in education, in: The Ethics of Artificial Intelligence in Education, Routledge, 2022, pp. 174–202.

[123]

S. Abdi, H. Khosravi, S. Sadiq, D. Gasevic, Complementing educational recommender systems with open learner models, in: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 2020, pp. 360–365.

[124]

Croitoru F.-A., Hondru V., Ionescu R.T., Shah M., Diffusion models in vision: A survey, IEEE Trans. Pattern Anal. Mach. Intell. 45 (9) (2023) 10850–10869,.

Digital Library

[125]

Yang L., Zhang Z., Song Y., Hong S., Xu R., Zhao Y., Zhang W., Cui B., Yang M.-H., Diffusion models: A comprehensive survey of methods and applications, ACM Comput. Surv. (2023),. Just Accepted.

Digital Library

[126]

Topal M.O., Bas A., van Heerden I., Exploring transformers in natural language generation: GPT, BERT, and XLNet, 2021, ArXiv abs/2102.08036. URL https://api.semanticscholar.org/CorpusID:231933669.

[127]

Bricken T., Templeton A., Batson J., Chen B., Jermyn A., Towards Monosemanticity: Decomposing Language Models With Dictionary Learning, Anthropic, 2023, URL https://transformer-circuits.pub/2023/monosemantic-features/index.html.

[128]

Cammarata N., Carter S., Goh G., Olah C., Petrov M., Schubert L., Voss C., Egan B., Lim S.K., Thread: Circuits, Distill (2020),.

[129]

Elhage N., Nanda N., Olsson C., Henighan T., A Mathematical Framework for Transformer Circuits, Anthropic, 2021, URL https://transformer-circuits.pub/2021/framework/index.html.

[130]

Olah C., Cammarata N., Schubert L., Goh G., Petrov M., Carter S., Zoom in: An introduction to circuits, Distill (2020),. https://distill.pub/2020/circuits/zoom-in.

[131]

Nanda N., Chan L., Lieberum T., Smith J., Steinhardt J., Progress measures for grokking via mechanistic interpretability, 2023, URL https://arxiv.org/abs/2301.05217v2.

[132]

Zhang S.D., Tigges C., Biderman S., Raginsky M., Ringer T., Can transformers learn to solve problems recursively?, 2023,. arXiv:2305.14699 [cs]. URL http://arxiv.org/abs/2305.14699.

[133]

Black S., Sharkey L., Grinsztajn L., Winsor E., Braun D., Merizian J., Parker K., Guevara C.R., Millidge B., Alfour G., Leahy C., Interpreting neural networks through the polytope lens, 2022, URL https://arxiv.org/abs/2211.12312v1.

[134]

Zhong Z., Liu Z., Tegmark M., Andreas J., The clock and the pizza: Two stories in mechanistic explanation of neural networks, 2023, URL https://arxiv.org/abs/2306.17844v1.

[135]

Zimmermann R.S., Klein T., Brendel W., Scale alone does not improve mechanistic interpretability in vision models, 2023, URL https://arxiv.org/abs/2307.05471v1.

[136]

Amari S.-i., Information Geometry and Its Applications, Springer, 2016, Google-Books-ID: UkSFCwAAQBAJ.

[137]

Brcic M., Yampolskiy R.V., Impossibility results in AI: A survey, ACM Comput. Surv. 56 (1) (2023) 8:1–8:24,. URL https://dl.acm.org/doi/10.1145/3603371.

Digital Library

[138]

Liu Z., Gan E., Tegmark M., Seeing is believing: Brain-inspired modular training for mechanistic interpretability, 2023,. arXiv:2305.08746 [cond-mat, q-bio]. URL http://arxiv.org/abs/2305.08746.

[139]

Rodríguez-Barroso N., Jiménez-López D., Luzón M.V., Herrera F., Martínez-Cámara E., Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges, Inf. Fusion 90 (2023) 148–173.

[140]

Bárcena J.L.C., Ducange P., Marcelloni F., Nardini G., Noferi A., Renda A., Ruffini F., Schiavo A., Stea G., Virdis A., Enabling federated learning of explainable AI models within beyond-5G/6G networks, Comput. Commun. 210 (2023) 356–375.

[141]

W. Du, M.J. Atallah, Secure multi-party computation problems and their applications: a review and open problems, in: Proceedings of the 2001 Workshop on New Security Paradigms, 2001, pp. 13–22.

[142]

Simonyan K., Vedaldi A., Zisserman A., Deep inside convolutional networks: Visualising image classification models and saliency maps, in: Bengio Y., LeCun Y. (Eds.), 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Workshop Track Proceedings, 2014, pp. 1–8. URL http://arxiv.org/abs/1312.6034.

[143]

Zeiler M.D., Fergus R., Visualizing and understanding convolutional networks, in: European Conference on Computer Vision, Springer, 2014, pp. 818–833.

[144]

R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, in: IEEE International Conference on Computer Vision, ICCV, 2017, pp. 618–626.

[145]

Bach S., Binder A., Montavon G., Klauschen F., Müller K.-R., Samek W., On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PLoS One 10 (7) (2015).

[146]

Kim B., Wattenberg M., Gilmer J., Cai C., Wexler J., Viegas F., et al., Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV), in: International Conference on Machine Learning, PMLR, 2018, pp. 2668–2677.

[147]

Chen C., Li O., Tao D., Barnett A., Rudin C., Su J.K., This looks like that: deep learning for interpretable image recognition, Adv. Neural Inf. Process. Syst. 32 (2019).

[148]

M. Nauta, R. Van Bree, C. Seifert, Neural prototype trees for interpretable fine-grained image recognition, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 14933–14943.

[149]

D. Rymarczyk, Ł. Struski, J. Tabor, B. Zieliński, Protopshare: Prototypical parts sharing for similarity discovery in interpretable image classification, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 1420–1430.

[150]

Koh P.W., Nguyen T., Tang Y.S., Mussmann S., Pierson E., Kim B., Liang P., Concept bottleneck models, in: International Conference on Machine Learning, PMLR, 2020, pp. 5338–5348.

[151]

Zarlenga M.E., Barbiero P., Ciravegna G., Marra G., Giannini F., Diligenti M., Shams Z., Precioso F., Melacci S., Weller A., et al., Concept embedding models, 2022, arXiv preprint arXiv:2209.09056.

[152]

Achtibat R., Dreyer M., Eisenbraun I., Bosse S., Wiegand T., Samek W., Lapuschkin S., From attribution maps to human-understandable explanations through Concept Relevance Propagation, Nat. Mach. Intell. 5 (9) (2023) 1006–1019,. Number: 9, Publisher: Nature Publishing Group. URL https://www.nature.com/articles/s42256-023-00711-8.

[153]

Mao J., Gan C., Kohli P., Tenenbaum J.B., Wu J., The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision, in: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019, URL https://openreview.net/forum?id=rJgMlhRctm.

[154]

Sarker M.K., Zhou L., Eberhart A., Hitzler P., Neuro-symbolic artificial intelligence, AI Commun. 34 (3) (2021) 197–209.

[155]

Hamilton K., Nayak A., Božić B., Longo L., Is neuro-symbolic AI meeting its promises in natural language processing? A structured review, Semant. Web 15 (Preprint) (2022) 1–42.

[156]

Tiddi I., Schlobach S., Knowledge graphs as tools for explainable machine learning: A survey, Artificial Intelligence 302 (2022).

Digital Library

[157]

Räuker T., Ho A., Casper S., Hadfield-Menell D., Toward transparent ai: A survey on interpreting the inner structures of deep neural networks, in: 2023 IEEE Conference on Secure and Trustworthy Machine Learning, SaTML, IEEE, 2023, pp. 464–483.

[158]

J. Johnson, B. Hariharan, L. Van Der Maaten, L. Fei-Fei, C. Lawrence Zitnick, R. Girshick, Clevr: A diagnostic dataset for compositional language and elementary visual reasoning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2901–2910.

[159]

K. Yi, C. Gan, Y. Li, P. Kohli, J. Wu, A. Torralba, J.B. Tenenbaum, CLEVRER: Collision Events for Video Representation and Reasoning, in: ICLR, 2020.

[160]

Müller H., Holzinger A., Kandinsky patterns, Artificial Intelligence 300 (2021).

Digital Library

[161]

de Vries H., Bahdanau D., Murty S., Courville A.C., Beaudoin P., CLOSURE: assessing systematic generalization of CLEVR models, in: Visually Grounded Interaction and Language (ViGIL), NeurIPS 2019 Workshop, Vancouver, Canada, December 13, 2019, 2019, URL https://vigilworkshop.github.io/static/papers/28.pdf.

[162]

Schneider J., Apruzzese G., Concept-based adversarial attacks: Tricking humans and classifiers alike, in: 2022 IEEE Security and Privacy Workshops, SPW, IEEE, 2022, pp. 66–72.

[163]

Nguyen A., Dosovitskiy A., Yosinski J., Brox T., Clune J., Synthesizing the preferred inputs for neurons in neural networks via deep generator networks, Advances in Neural Information Processing Systems, 2016, pp. 3387–3395.

[164]

Schneider J., Vlachos M., A survey of deep learning: From activations to transformers, 2023, arXiv preprint arXiv:2302.00722.

[165]

Schneider J., Vlachos M., Explaining classifiers by constructing familiar concepts, Mach. Learn. (2022) 1–34.

[166]

Yeh C.-K., Hsieh C.-Y., Suggala A., Inouye D.I., Ravikumar P.K., On the (in) fidelity and sensitivity of explanations, Adv. Neural Inf. Process. Syst. 32 (2019).

[167]

Gao Y., Gu S., Jiang J., Hong S.R., Yu D., Zhao L., Going beyond XAI: A systematic survey for explanation-guided learning, 2022, arXiv preprint arXiv:2212.03954.

[168]

Ferrario A., Loi M., The robustness of counterfactual explanations over time, IEEE Access 10 (2022) 82736–82750.

[169]

L. Qiu, Y. Yang, C.C. Cao, Y. Zheng, H. Ngai, J. Hsiao, L. Chen, Generating perturbation-based explanations with robustness to out-of-distribution data, in: Proceedings of the ACM Web Conference 2022, 2022, pp. 3594–3605.

[170]

Seuß D., Bridging the gap between explainable AI and uncertainty quantification to enhance trustability, 2021, arXiv preprint arXiv:2105.11828.

[171]

Kuppa A., Le-Khac N.-A., Black box attacks on explainable artificial intelligence (XAI) methods in cyber security, in: 2020 International Joint Conference on Neural Networks, IJCNN, IEEE, 2020, pp. 1–8.

[172]

Oksuz A.C., Halimi A., Ayday E., AUTOLYCUS: Exploiting explainable AI (XAI) for model extraction attacks against decision tree models, 2023, arXiv preprint arXiv:2302.02162.

[173]

Pahde F., Dreyer M., Samek W., Lapuschkin S., Reveal to revise: An explainable AI life cycle for iterative bias correction of deep models, in: Greenspan H., Madabhushi A., Mousavi P., Salcudean S., Duncan J., Syeda-Mahmood T., Taylor R. (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Springer Nature Switzerland, Cham, 2023, pp. 596–606.

[174]

Krishnan M., Against interpretability: A critical examination of the interpretability problem in machine learning, Philos. Technol. 33 (3) (2020) 487–502,.

[175]

Lipton Z.C., The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery, Queue 16 (3) (2018) 31–57.

Digital Library

[176]

Ehsan U., Riedl M.O., Social construction of XAI: Do we need one definition to rule them all?, in: Muller M., Angelov P., Daume III H., Guha S., Liao Q.V., Oliver N., Piorkowski D. (Eds.), Proceedings of the NeurIPS 2022 Workshop on Human-Centered AI, 2022, arXiv:2211.06499.

[177]

Clinciu M.-A., Hastie H., A survey of explainable AI terminology, in: Alonso J.M., Catala A. (Eds.), Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, in: NL4XAI 2019, Association for Computational Linguistics, Stroudsburg, PA, USA, 2019, pp. 8–13,.

[178]

Graziani M., Dutkiewicz L., Calvaresi D., Amorim J.P., Yordanova K., Vered M., Nair R., Abreu P.H., Blanke T., Pulignano V., Prior J.O., Lauwaert L., Reijers W., Depeursinge A., Andrearczyk V., Müller H., A global taxonomy of interpretable AI: Unifying the terminology for the technical and social sciences, Artif. Intell. Rev. (2022) 1–32,.

Digital Library

[179]

Díaz-Rodríguez N., Del Ser J., Coeckelbergh M., López de Prado M., Herrera-Viedma E., Herrera F., Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation, Inf. Fusion 99 (2023),.

Digital Library

[180]

Robbins S., A misdirected principle with a catch: Explicability for AI, Minds Mach. 29 (4) (2019) 495–514,.

Digital Library

[181]

Kizilcec R.F., How much information? Effects of transparency on trust in an algorithmic interface, in: Kaye J., Druin A., Lampe C., Morris D., Hourcade J.P. (Eds.), Proceedings of the 34th Conference on Human Factors in Computing Systems, in: CHI 2016, Association for Computing Machinery, New York, NY, USA, 2016, pp. 2390–2395,.

Digital Library

[182]

Ghosh B., Malioutov D., Meel K.S., Interpretable classification rules in relaxed logical form, in: Miller T., Weber R., Magazzeni D. (Eds.), Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence, in: IJCAI XAI 2019, 2019, pp. 14–20.

[183]

J. Newman, A Taxonomy of Trustworthiness for Artificial Intelligence, CLTC White Paper Series, North Charleston, SC, USA, 2023, URL.

[184]

Palladino N., A ‘biased’ emerging governance regime for artificial intelligence? How AI ethics get skewed moving from principles to practices, Telecommun. Policy 47 (5) (2023),.

Digital Library

[185]

Khalifa K., Inaugurating understanding or repackaging explanation?, Philos. Sci. 79 (1) (2012) 15–37.

[186]

Strevens M., No understanding without explanation, Stud. Hist. Philos. Sci. A 44 (3) (2013) 510–515.

[187]

P. Lipton, Understanding without explanation, in: Scientific Understanding: Philosophical Perspectives, 2009, pp. 43–63.

[188]

Elgin C.Z., True Enough, MIT Press, 2017.

[189]

Kvanvig J., Responses to critics, in: Epistemic Value, Oxford University Press, Oxford, 2009, pp. 339–351.

[190]

Mizrahi M., Idealizations and scientific understanding, Philos. Stud. 160 (2012) 237–252.

[191]

Carter J.A., Gordon E.C., Objectual understanding, factivity and belief, in: Epistemic Reasons, Norms and Goals, Vol. 423, De Gruyter, Berlin, 2016.

[192]

Erasmus A., Brunet T.D., Fisher E., What is interpretability?, Philos. Technol. 34 (4) (2021) 833–862.

[193]

Pritchard D., Knowing the Answer, Understanding and Epistemic Value, Citeseer, 2008.

[194]

Zagzebski L., On Epistemology, Wadsworth, 2009.

[195]

Lombrozo T., Wilkenfeld D., Lombrozo T., Wilkenfeld D., Mechanistic versus functional understanding, in: Varieties of Understanding: New Perspectives from Philosophy, Psychology, and Theology, Oxford University Press, New York, NY, 2019, pp. 209–229.

[196]

Sullivan E., Understanding from machine learning models, British J. Philos. Sci. (2022).

[197]

Creel K.A., Transparency in complex computational systems, Philos. Sci. 87 (4) (2020) 568–589.

[198]

Durán J.M., Dissecting scientific explanation in AI (sXAI): A case for medicine and healthcare, Artificial Intelligence 297 (2021).

[199]

Zednik C., Solving the black box problem: A normative framework for explainable artificial intelligence, Philos. Technol. 34 (2) (2021) 265–288.

[200]

Fleisher W., Understanding, idealization, and explainable AI, Episteme 19 (4) (2022) 534–560.

[201]

Pirozelli P., Sources of understanding in supervised machine learning models, Philos. Technol. 35 (2) (2022) 23.

[202]

M.M. De Graaf, B.F. Malle, How people explain action (and autonomous intelligent systems should too), in: 2017 AAAI Fall Symposium Series, 2017, pp. 19–26.

[203]

B. Mittelstadt, C. Russell, S. Wachter, Explaining explanations in AI, in: Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019, pp. 279–288.

[204]

Guidotti R., Evaluating local explanation methods on ground truth, Artificial Intelligence 291 (2021).

[205]

Sevillano-García I., Luengo J., Herrera F., REVEL framework to measure local linear explanations for black-box models: Deep learning image classification case study, Int. J. Intell. Syst. 2023 (2023) 1–34.

[206]

M.T. Keane, E.M. Kenny, E. Delaney, B. Smyth, If only we had better counterfactual explanations: Five key deficits to rectify in the evaluation of counterfactual xai techniques, in: Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI-21, 2021, pp. 4466–4474.

[207]

Dodge J., Liao Q.V., Zhang Y., Bellamy R.K.E., Dugan C., Explaining models: an empirical study of how explanations impact fairness judgment, in: IUI, ACM, 2019, pp. 275–285.

[208]

Lucic A., Haned H., de Rijke M., Why does my model fail?: contrastive local explanations for retail forecasting, in: FAT*, ACM, 2020, pp. 90–98.

[209]

Metta C., Guidotti R., Yin Y., Gallinari P., Rinzivillo S., Exemplars and counterexemplars explanations for skin lesion classifiers, in: HHAI, in: Frontiers in Artificial Intelligence and Applications, vol. 354, IOS Press, 2022, pp. 258–260.

[210]

Hoffman R.R., Mueller S.T., Klein G., Litman J., Metrics for explainable AI: Challenges and prospects, 2018, ArXiv abs/1812.04608.

[211]

van der Lee C., Gatt A., van Miltenburg E., Krahmer E., Human evaluation of automatically generated text: Current trends and best practice guidelines, Comput. Speech Lang. 67 (2021),. URL https://www.sciencedirect.com/science/article/pii/S088523082030084X.

[212]

Nauta M., Trienes J., Pathak S., Nguyen E., Peters M., Schmitt Y., Schlötterer J., van Keulen M., Seifert C., From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable AI, ACM Comput. Surv. (2023),.

Digital Library

[213]

Confalonieri R., Alonso-Moral J.M., An operational framework for guiding human evaluation in Explainable and Trustworthy AI, IEEE Intell. Syst. (2023) 1–13,.

Digital Library

[214]

Hedström A., Weber L., Bareeva D., Motzkus F., Samek W., Lapuschkin S., Höhne M.M.C., Quantus: An explainable AI toolkit for responsible evaluation of neural network explanation, J. Mach. Learn. Res. 24 (34) (2023) 1–11. URL http://jmlr.org/papers/v24/22-0142.html.

[215]

Arras L., Osman A., Samek W., CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations, Inf. Fusion 81 (2022) 14–40,.

Digital Library

[216]

Pahde F., Dreyer M., Samek W., Lapuschkin S., Reveal to revise: An explainable AI life cycle for iterative bias correction of deep models, in: Greenspan H., Madabhushi A., Mousavi P., Salcudean S., Duncan J., Syeda-Mahmood T., Taylor R. (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Springer Nature Switzerland, Cham, 2023, pp. 596–606.

[217]

Longo L., Formalising human mental workload as non-monotonic concept for adaptive and personalised web-design, in: User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings 20, Springer, 2012, pp. 369–373.

[218]

Longo L., Designing medical interactive systems via assessment of human mental workload, in: 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, IEEE, 2015, pp. 364–365.

[219]

Hancock G., Longo L., Young M., Hancock P., Mental workload, in: Handbook of Human Factors and Ergonomics, Wiley Online Library, 2021, pp. 203–226.

[220]

Longo L., Wickens C.D., Hancock P.A., Hancock G.M., Human mental workload: A survey and a novel inclusive definition, Front. Psychol. 13 (2022),. URL https://www.frontiersin.org/article/10.3389/fpsyg.2022.883321.

[221]

Confalonieri R., Weyde T., Besold T.R., del Prado Martín F.M., Using ontologies to enhance human understandability of global post-hoc explanations of Black-box models, Artificial Intelligence 296 (2021),.

[222]

Nielsen I.E., Dera D., Rasool G., Ramachandran R.P., Bouaynaya N.C., Robust explainability: A tutorial on gradient-based attribution methods for deep neural networks, IEEE Signal Process. Mag. 39 (4) (2022) 73–84.

[223]

Yuan J., Chen T., Li B., Xue X., Compositional scene representation learning via reconstruction: A survey, IEEE Trans. Pattern Anal. Mach. Intell. 45 (10) (2023) 11540–11560,.

Digital Library

[224]

Klinger T., Adjodah D., Marois V., Joseph J., Riemer M., Pentland A., Campbell M., A study of compositional generalization in neural models, 2020, arXiv preprint arXiv:2006.09437.

[225]

Rizzo L., Longo L., An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems, Expert Syst. Appl. 147 (2020).

[226]

L. Rizzo, L. Longo, A Qualitative Investigation of the Explainability of Defeasible Argumentation and Non-Monotonic Fuzzy Reasoning, in: Proceedings for the 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science Trinity College Dublin, Dublin, Ireland, December 6-7th, 2018, 2018, pp. 138–149.

[227]

Vilone G., Longo L., A novel human-centred evaluation approach and an argument-based method for explainable artificial intelligence, in: Artificial Intelligence Applications and Innovations: 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part I, Springer, 2022, pp. 447–460.

[228]

Vilone G., Longo L., An XAI method for the automatic formation of an abstract argumentation framework from a neural network and its objective evaluation, in: 1st International Workshop on Argumentation for EXplainable AI Co-Located with 9th International Conference on Computational Models of Argument (COMMA 2022), in: CEUR Workshop Proceedings, vol. 3209, CEUR-WS.org, 2022, URL http://ceur-ws.org/Vol-3209/2119.pdf.

[229]

Vielhaben J., Lapuschkin S., Montavon G., Samek W., Explainable ai for time series via virtual inspection layers, Pattern Recognition 150 (2024) 110309,. https://www.sciencedirect.com/science/article/pii/S0031320324000608.

Digital Library

[230]

Ahmed T., Longo L., Interpreting disentangled representations of person-specific convolutional variational autoencoders of spatially preserving EEG topographic maps via clustering and visual plausibility, Information 14 (9) (2023),. URL https://www.mdpi.com/2078-2489/14/9/489.

[231]

Quine W.V., On what there is, in: Quine W.V. (Ed.), From a Logical Point of View, Harvard University Press, Cambridge, Mass., 1953, pp. 1–19.

[232]

Krakauer D., The computational systems of the world, BioScience 64 (4) (2014) 351–354,.

[233]

Badreddine S., Garcez A.d., Serafini L., Spranger M., Logic tensor networks, Artificial Intelligence 303 (2022).

[234]

Meske C., Bunde E., Schneider J., Gersch M., Explainable artificial intelligence: Objectives, stakeholders and future research opportunities, Inf. Syst. Manage. (2020).

[235]

Weller A., Transparency: Motivations and challenges, in: Samek W., Montavon G., Vedaldi A., Hansen L.K., Müller K.-R. (Eds.), Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, in: Lecture Notes in Computer Science, Springer International Publishing, Cham, 2019, pp. 23–40.

[236]

R. Hamon, H. Junklewitz, G. Malgieri, P.D. Hert, L. Beslay, I. Sanchez, Impossible explanations? Beyond explainable AI in the GDPR from a COVID-19 use case scenario, in: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021, pp. 549–559.

[237]

Flack J.C., Multiple time-scales and the developmental dynamics of social systems, Philos. Trans. R. Soc. B 367 (1597) (2012) 1802–1810,. Publisher: Royal Society. URL https://royalsocietypublishing.org/doi/10.1098/rstb.2011.0214.

[238]

Juric M., Sandic A., Brcic M., AI safety: state of the field through quantitative lens, in: 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), 2020, pp. 1254–1259,. ISSN: 2623-8764.

[239]

Doshi-Velez F., Kim B., Towards a rigorous science of interpretable machine learning, 2017, arXiv:1702.08608.

[240]

Beckers S., Causal explanations and XAI, in: Conference on Causal Learning and Reasoning, PMLR, 2022, pp. 90–109.

[241]

Chou Y.-L., Moreira C., Bruza P., Ouyang C., Jorge J., Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications, Inf. Fusion 81 (2022) 59–83.

Digital Library

[242]

Cinquini M., Guidotti R., CALIME: Causality-aware local interpretable model-agnostic explanations, 2022, arXiv preprint arXiv:2212.05256.

[243]

Del Ser J., Barredo-Arrieta A., Díaz-Rodríguez N., Herrera F., Saranti A., Holzinger A., On generating trustworthy counterfactual explanations, Inform. Sci. 655 (2024).

[244]

P. Sanchez, S.A. Tsaftaris, Diffusion causal models for counterfactual estimation, in: Conference on Causal Learning and Reasoning, CLeaR, 2022.

[245]

M. Augustin, V. Boreiko, F. Croce, M. Hein, Diffusion Visual Counterfactual Explanations, in: NeurIPS, 2022.

[246]

Schneider J., Vlachos M., Personalization of deep learning, in: Data Science–Analytics and Applications: Proceedings of the 3rd International Data Science Conference–iDSC2020, Springer, 2021, pp. 89–96.

[247]

J. Schneider, J.P. Handali, Personalized Explanation for Machine Learning: a Conceptualization, in: European Conference on Information Systems, ECIS, 2019.

[248]

Zhu B., Jordan M., Jiao J., Principled reinforcement learning with human feedback from pairwise or K-wise comparisons, in: Krause A., Brunskill E., Cho K., Engelhardt B., Sabato S., Scarlett J. (Eds.), Proceedings of the 40th International Conference on Machine Learning, in: Proceedings of Machine Learning Research, vol. 202, PMLR, 2023, pp. 43037–43067.

[249]

Bewley T., Lecue F., Interpretable preference-based reinforcement learning with tree-structured reward functions, in: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’22, International Foundation for Autonomous Agents and Multiagent Systems, 2022, pp. 118–126.

[250]

Bunt A., Lount M., Lauzon C., Are explanations always important?: a study of deployed, low-cost intelligent interactive systems, in: Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces, IUI ’12, ACM, New York, NY, USA, 2012, pp. 169–178,. URL http://doi.acm.org/10.1145/2166966.2166996.

Digital Library

[251]

Rudin C., Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell. 1 (5) (2019) 206–215,. Number: 5 Publisher: Nature Publishing Group. URL https://www.nature.com/articles/s42256-019-0048-x.

[252]

Hamon R., Junklewitz H., Sanchez I., Malgieri G., De Hert P., Bridging the gap between AI and explainability in the GDPR: Towards trustworthiness-by-design in automated decision-makin, IEEE Comput. Intell. Mag. 17 (1) (2022) 72–85.

[253]

Gunning D., Stefik M., Choi J., Miller T., Stumpf S., Yang G.-Z., XAI—Explainable artificial intelligence, Science Robotics 4 (37) (2019),. URL https://robotics.sciencemag.org/content/4/37/eaay7120.

[254]

Krajna A., Brcic M., Lipic T., Doncevic J., Explainability in reinforcement learning: perspective and position, 2022,.

[255]

Ghassemi M., Oakden-Rayner L., Beam A.L., The false hope of current approaches to explainable artificial intelligence in health care, Lancet Digit. Health 3 (11) (2021) e745–e750.

[256]

Cabitza F., Campagner A., Famiglini L., Gallazzi E., La Maida G.A., Color shadows (part I): Exploratory usability evaluation of activation maps in radiological machine learning, in: Machine Learning and Knowledge Extraction: 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, Vienna, Austria, August 23–26, 2022, Proceedings, Springer, 2022, pp. 31–50.

[257]

Cabitza F., Campagner A., Ronzio L., Cameli M., Mandoli G.E., Pastore M.C., Sconfienza L., Folgado D., Barandas M., Gamboa H., Rams, hounds and white boxes: Investigating human-AI collaboration protocols in medical diagnosis, Artif. Intell. Med. (2023).

[258]

G. Bansal, T. Wu, J. Zhou, R. Fok, B. Nushi, E. Kamar, M.T. Ribeiro, D. Weld, Does the whole exceed its parts? the effect of ai explanations on complementary team performance, in: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1–16.

[259]

Natale S., Deceitful Media: Artificial Intelligence and Social Life After the Turing Test, Oxford University Press, USA, 2021.

[260]

Cabitza F., Campagner A., Datteri E., To err is (only) human. Reflections on how to move from accuracy to trust for medical AI, in: Exploring Innovation in a Digital World: Cultural and Organizational Challenges, Springer, 2021, pp. 36–49.

[261]

Cabitza F., Campagner A., Simone C., The need to move away from agential-AI: Empirical investigations, useful concepts and open issues, Int. J. Hum.-Comput. Stud. 155 (2021).

[262]

T. Miller, Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven Decision Support using Evaluative AI, in: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023, pp. 333–342.

[263]

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, CHI ’18, ACM, New York, NY, USA, 2018, pp. 582:1–582:18,. URL http://doi.acm.org/10.1145/3173574.3174156.

Digital Library

[264]

Baum K., Mantel S., Schmidt E., Speith T., From responsibility to reason-giving explainable artificial intelligence, Philos. Technol. 35 (1) (2022) 1–30,.

[265]

Thornton S., Karl Popper, in: Zalta E.N., Nodelman U. (Eds.), The Stanford Encyclopedia of Philosophy, Winter 2023 ed., Metaphysics Research Lab, Stanford University, 2023.

[266]

M.L. Leavitt, A. Morcos, Towards falsifiable interpretability research, in: NeurIPS 2020 Workshop: ML Retrospectives, Surveys and Meta-Analyses, ML-RSA, 2020.

[267]

Dosilovic F.K., Brcic M., Hlupic N., Explainable artificial intelligence: A survey, in: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2018, pp. 0210–0215,.

[268]

A. Krajna, M. Brcic, M. Kovac, A. Sarcevic, Explainable Artificial Intelligence: An Updated Perspective, in: Proceedings of 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO) 2022, Opatija, Croatia, 2022, pp. 859–864.

[269]

Murdoch W.J., Singh C., Kumbier K., Abbasi-Asl R., Yu B., Definitions, methods, and applications in interpretable machine learning, Proc. Natl. Acad. Sci. 116 (44) (2019) 22071–22080,. Publisher: Proceedings of the National Academy of Sciences. URL https://www.pnas.org/doi/10.1073/pnas.1900654116.

[270]

J. Schneider, C. Meske, M. Vlachos, Deceptive AI Explanations: Creation and Detection, in: International Conference on Agents and Artificial Intelligence, ICAART, 2022, pp. 44–55.

[271]

Schneider J., Breitinger F., AI Forensics: Did the artificial intelligence system do it? why?, 2020, arXiv preprint arXiv:2005.13635.

[272]

Schneider J., Vlachos M., Reflective-net: Learning from explanations, Data Min. Knowl. Discov. (2023).

[273]

Yampolskiy R.V., Unexplainability and incomprehensibility of AI, J. Artif. Intell. Conscious. 07 (02) (2020) 277–291,. Publisher: World Scientific Publishing Co. URL https://www.worldscientific.com/doi/10.1142/S2705078520500150.

[274]

Yampolskiy R.V., What are the ultimate limits to computational techniques: verifier theory and unverifiability, Phys. Scr. 92 (9) (2017),. Publisher: IOP Publishing.

[275]

Boutin V., Fel T., Singhal L., Mukherji R., Nagaraj A., Colin J., Serre T., Diffusion models as artists: Are we closing the gap between humans and machines?, in: International Conference on Machine Learning, 2023, URL https://api.semanticscholar.org/CorpusID:256358696.

[276]

Thorp H.H., ChatGPT is fun, but not an author, Science 379 (6630) (2023) 313.

[277]

van Dis E.A., Bollen J., Zuidema W., van Rooij R., Bockting C.L., ChatGPT: five priorities for research, Nature 614 (7947) (2023) 224–226.

[278]

Boenisch F., A systematic review on model watermarking for neural networks, Front. Big Data 4 (2021).

[279]

J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, T. Goldstein, A Watermark for Large Language Models, in: Proceedings of the 40th International Conference on Machine Learning, 2023, pp. 202:17061–17084.

[280]

Bourtoule L., Chandrasekaran V., Choquette-Choo C.A., Jia H., Travers A., Zhang B., Lie D., Papernot N., Machine unlearning, in: 2021 IEEE Symposium on Security and Privacy, SP, IEEE, 2021, pp. 141–159.

[281]

Nguyen T.T., Huynh T.T., Nguyen P.L., Liew A.W.-C., Yin H., Nguyen Q.V.H., A survey of machine unlearning, 2022, arXiv preprint arXiv:2209.02299.

[282]

Cohen J.E., Between Truth and Power, Oxford University Press, 2019.

[283]

Cabitza F., Campagner A., Malgieri G., Natali C., Schneeberger D., Stoeger K., Holzinger A., Quod erat demonstrandum?-Towards a typology of the concept of explanation for the design of explainable AI, Expert Syst. Appl. 213 (2023).

[284]

Malgieri G., “Just” algorithms: justification (beyond explanation) of automated decisions under the general data protection regulation, Law Bus. 1 (1) (2021) 16–28.

[285]

Bayamlioglu E., Contesting automated decisions, Eur. Data Prot. L. Rev. 4 (2018) 433.

[286]

Henin C., Le Métayer D., Beyond explainability: justifiability and contestability of algorithmic decision systems, AI Soc. (2021) 1–14.

[287]

Henin C., Le Métayer D., A framework to contest and justify algorithmic decisions, AI Ethics 1 (4) (2021) 463–476.

[288]

L.M. Austin, Enough about me: why privacy is about power, not consent (or harm), in: A. Sarat (Ed.), A World Without Privacy: What Law Can and Should Do?, 2014, pp. 131–189.

[289]

Wilsdon L., Carissa véliz, privacy is power: Why and how you should take back control of your data, 2022.

[290]

Costanza-Chock S., Design Justice: Community-Led Practices to Build the Worlds We Need, The MIT Press, 2020.

[291]

Kaminski M.E., Malgieri G., Algorithmic impact assessments under the GDPR: producing multi-layered explanations, Int. Data Priv. Law (2020) 19–28.

[292]

Gregory J., Scandinavian approaches to participatory design, Int. J. Eng. Educ. 19 (1) (2003) 62–74.

[293]

Mantelero A., Beyond Data: Human Rights, Ethical and Social Impact Assessment in AI, Springer Nature, 2022.

[294]

Malgieri G., In/acceptable marketing and consumers’ privacy expectations: Four tests from EU data protection law, J. Consum. Mark. 40 (2) (2023) 209–223.

[295]

Bodker K., Kensing F., Simonsen J., Participatory IT Design: Designing for Business and Workplace Realities, MIT Press, 2009.

Digital Library

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Information Fusion Volume 106, Issue C

Jun 2024

580 pages

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Africano BMpungu GJjingo DMarvin G(2024)PII Detection in Low-Resource Languages Using Explainable Deep Learning TechniquesProceedings of the 2024 Sixteenth International Conference on Contemporary Computing10.1145/3675888.3676036(94-103)Online publication date: 8-Aug-2024
https://dl.acm.org/doi/10.1145/3675888.3676036
Schmitt VVilla-Arenas LFeldhus NMeyer JSpang RMöller S(2024)The Role of Explainability in Collaborative Human-AI Disinformation DetectionProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659031(2157-2174)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659031
Hamilton KLongo LBozic BChua TNgo CKumar RLauw HKa-Wei Lee R(2024)GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text.Companion Proceedings of the ACM Web Conference 202410.1145/3589335.3651909(1431-1440)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651909
Nannini LMarchiori Manerba MBeretta I(2024)Mapping the landscape of ethical considerations in explainable AI researchEthics and Information Technology10.1007/s10676-024-09773-726:3Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1007/s10676-024-09773-7
Schwammberger M(2023)From Explanation Correctness to Explanation Goodness: Only Provably Correct Explanations Can Save the WorldBridging the Gap Between AI and Reality10.1007/978-3-031-73741-1_19(307-317)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-73741-1_19

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Affiliations

Luca Longo

The Artificial Intelligence and Cognitive Load Research Lab, Ireland

School of Computer Science, Technological University Dublin, Republic of Ireland

Mario Brcic

University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia

Federico Cabitza

University of Milano-Bicocca, Milan, Italy

IRCCS Ospedale Galeazzi Sant’Ambrogio, Milan, Italy

Jaesik Choi

Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Republic of Korea

INEEJI Corporation, Republic of Korea

Roberto Confalonieri

Department of Mathematics, University of Padua, Italy

Javier Del Ser

TECNALIA, Basque Research & Technology Alliance (BRTA), Derio, Spain

University of the Basque Country (UPV/EHU), Bilbao, Spain

Department of Computer Science and Artificial Intelligence, DaSCI Andalusian Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain

Riccardo Guidotti

University of Pisa, Pisa, Italy

Yoichi Hayashi

Department of Computer Science, Meiji University, Tokyo, Japan

Francisco Herrera

Department of Computer Science and Artificial Intelligence, DaSCI Andalusian Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain

Andreas Holzinger

Human-Centered AI Lab, University of Natural Resources and Life Sciences Vienna, Austria

Richard Jiang

School of Computing and Communications, Lancaster University, UK

Hassan Khosravi

The University of Queensland, Brisbane, Australia

Freddy Lecue

National Institute for Research in Digital Science and Technology (INRIA), Sophia Antipolis, France

Gianclaudio Malgieri

eLaw Center for Law and Digital Technologies, Leiden University, Netherlands

Andrés Páez

Department of Philosophy, Universidad de los Andes, Bogotá, Colombia

Center for Research and Formation in Artificial Intelligence (CinfonIA), Universidad de los Andes, Bogotá, Colombia

Wojciech Samek

Technical University of Berlin, Berlin, Germany

Fraunhofer Heinrich Hertz Institute, Berlin, Germany

Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany

Johannes Schneider

Department of Information Systems and Computer Science, University of Liechtenstein, Liechtenstein

Timo Speith

Department of Philosophy, University of Bayreuth, Bayreuth, Germany

Center for Perspicuous Computing, Saarland University, Saarbrücken, Germany

Simone Stumpf

School of Computing Science, University of Glasgow, United Kingdom

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