Explainable AI: The New 42?

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542

DeepStack: Expert-level artificial intelligence in heads-up no-limit poker

Moravčík, M., Schmid, M., BURCH, N. R., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., & Bowling, M. (2017) · Science · doi:10.1126/science.aam6960 · Cited by 885

What do we need to build explainable AI systems for the medical domain?

Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636

Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Kulesza, T., Burnett, M., Wong, W.-K., & Stumpf, S. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701399 · Cited by 576

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534

Toward Effective Communication of AI-Based Decisions in Assistive Tools: Conveying Confidence and Doubt to People with Visual Impairments at Accelerated Speech

Akter, T., Swaminathan, M., & Kapadia, A. C. (2024) · International Web for All Conference (W4A) · doi:10.1145/3677846.3677862 · Cited by 2

FaceOracle: Chat with a Face Image Oracle

Kabbani, W., Raja, K. B., Ramachandra, R., & Busch, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92089-9_14 · Cited by 3

A Case for Guided Machine Learning

Westphal, F., Lavesson, N., & Grahn, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_22 · Cited by 2

Advancing Edge Artificial Intelligence

Vermesan, O., & Marples, D. (2023) · doi:10.13052/rp-9788770041010 · Cited by 2

APPROACHES TO KNOWLEDGE EXTRACTION IN SCIENTIFIC SUBJECT DOMAINS

Tuchkova, N. P., & Ataeva, O. M. (2020) · Информационные и математические технологии в науке и управлении · doi:10.38028/esi.2020.18.2.001 · Cited by 2

Smart Advertisement for Maximal Clicks in Online Social Networks Without User Data

Hudson, N., Khamfroush, H., Harrison, B. E., & Craig, A. W. (2020) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp50058.2020.00042 · Cited by 2

Do ML Experts Discuss Explainability for AI Systems? A discussion case in the industry for a domain-specific solutionPreprint

Ferreira, J. J., & Monteiro, M. D. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12450 · Cited by 6

Towards AI Standards: Thought-Leadership in Ai Legal, Ethical and Safety Specifications Through ExperimentationPreprint

Kazim, E., Polle, R., Carvalho, G., Koshiyama, A., Inness, C., Knight, A., Gorski, C., Barber, D., Lomas, E., Yılmaz, E., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3935987 · Cited by 2

Native Concept Frameworks in Unsupervised Generative Learning

Dolgikh, S. (2021) · International Conference on Advanced Computer Information Technologies (ACIT) · doi:10.1109/acit52158.2021.9548372 · Cited by 1

Data-Driven Suitability Analysis to Enable Machine Learning Explainability and Security

Wolf, S., Foster, R., Haile, J., & Borowczak, M. (2021) · Resilience Week (RWS) · doi:10.1109/rws52686.2021.9611792 · Cited by 1

Usability and Adoption of Graphical Tools for Data-Driven Development

Weber, T., & Mayer, S. (2024) · Mensch und Computer (MuC) · doi:10.1145/3670653.3670658 · Cited by 1

Principal Component Regression to Study the Impact of Economic Factors on Disadvantaged Communities

Mohankumar, N. M., Jain, M., Wan, H., Ganguli, S., Wilson, K. D., & Anderson, D. M. (2024) · Journal of Social Computing · doi:10.23919/jsc.2024.0026 · Cited by 1

Employing Explainable Artificial Intelligence (XAI) for Enhanced Water Quality Assessment

Maurya, R., Saha, S., Misra, B., Chatterjee, D., Chakraborty, S., & Sarkar, P. (2025) · International Conference on Computing, Intelligence, and Application (CIACON) · doi:10.1109/ciacon65473.2025.11189765 · Cited by 1

The Case for Sustainable AI in the Creative Industry

Sowriraghavan, A. (2025) · Women in engineering and science · doi:10.1007/978-3-031-95890-8_10 · Cited by 2

Benchmarking Explainable AI Methods for Tabular Healthcare Data:Towards Standardized and Clinically Interpretable Evaluation Frameworks

Singh, S., & Rajput, S. D. (2025) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon67554.2025.11378728 · Cited by 1

Counterfactual Explanation Bake-Off: A Review and Experimental Evaluation for Time Series Classification

Li, P., Bahri, O., Boubrahimi, S. F., & Hamdi, S. M. (2026) · Machine Learning · doi:10.1007/s10994-026-07056-4 · Cited by 1

Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study

Thrun, M. C., Hoffmann, J., Krause, S. W., Krawitz, P. M., Stier, Q., Neubauer, A., Brendel, C. A., & Ultsch, A. (2026) · PLoS Medicine · doi:10.1371/journal.pmed.1004889 · Cited by 1

On the Need for Building Trust With Autonomous Underwater Vehicles

Scielzo, S., & Kocak, D. M. (2020) · Marine Technology Society Journal · doi:10.4031/mtsj.54.5.2 · Cited by 1

Trends and Emerging Technologies in AI

Hoppe, D. (2021) · Springer eBooks · doi:10.1007/978-3-030-68049-7_12 · Cited by 2

Exploring New Redshift Indicators for Radio-Powerful AGN

Carvajal, R., Matute, I., Afonso, J., Amarantidis, S., Barbosa, D., Cunha, P. A. C., & Humphrey, A. (2021) · Galaxies · doi:10.3390/galaxies9040086 · Cited by 1

Counterfactual Causal Analysis on Structured Data

Paul, S. K., Firdausi, T. J., Jana, S., Das, A., & Nandi, P. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-7088-6_16 · Cited by 1

Sustainability Effects of Robust and Resilient Artificial Intelligence

Priebe, T., Kieseberg, P., Adrowitzer, A., Eigner, O., & Kovac, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40837-3_12 · Cited by 1

On the Interpretability of a Tree-based Ensemble for Predicting Software Effort

Najm, A., & Zakrani, A. (2023) · IEEE Congress on Information Science and Technology (CiSt) · doi:10.1109/cist56084.2023.10410020 · Cited by 1

Explainable AI for Chest Diagnosis Prediction

Gangam, M., Baghel, V., Ali, M. M., Raj, M., Pranav, A., & ranjan, V. (2024) · International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) · doi:10.1109/ic-etite58242.2024.10493633 · Cited by 1

Requirements for Explainable Smart Systems in the Enterprises from Users and Society Based on FAT.

Nakao, Y., Shigezumi, J., Yokono, H., & Takagi, T. (2019) · IUI Workshops · Cited by 1

SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge

Hünemörder, M., Kazempour, D., Kröger, P., & Seidl, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32047-8_20 · Cited by 1

On Trusting a Cyber Librarian: How Rethinking Underlying Data Storage Infrastructure Can Mitigate Risksof Automation

Israel, M. J., Graves, M., & Amer, A. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-76426-5_3 · Cited by 1

Cognitive Production Systems: A Mapping StudyPreprint

Deutschmann, B., Ghofrani, J., & Reichelt, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13235 · Cited by 1

Hierarchical Concept Bottleneck Models for Explainable Images Segmentation, Objects Fine Classification and TrackingPreprint

Pittino, F., Dimitrievska, V., & Heer, R. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3994205 · Cited by 1

Can AI provenance inform archival provenance?

Corry, F. (2026) · Cambridge Forum on AI: Culture and Society · doi:10.1017/cfc.2026.10025

Do ML Experts Discuss Explainability for AI Systems

Ferreira, J. J., & Monteiro, M. D. S. (2020)

Minke Whale Detection in Underwater Imagery using Classification CNNs

Konovalov, D. A., Swinhoe, N., Efremova, D. B., Birtles, A., Kusetic, M., Adams, K., Hillcoat, S., Curnock, M., Williams, G., Sobtzick, S., et al. (2020) · Global Oceans 2020: Singapore – U.S. Gulf Coast · doi:10.1109/ieeeconf38699.2020.9389164

Recommender Systems for Capability Matchmaking

Badewitz, W., Stamer, F., Linzbach, J., Dann, D., Weinhardt, C., & Lichtenberger, S. (2021) · IEEE Conference on Business Informatics · doi:10.1109/cbi52690.2021.10059

Exploiting auto-encoders for explaining black-box classifiers

Guidotti, R. (2022) · Intelligenza Artificiale · doi:10.3233/ia-220139

The Future

Furrer, F. J. (2022) · Springer eBooks · doi:10.1007/978-3-658-37182-1_6

Expression Recognition Using a Flow-Based Latent-Space Representation

Aathreya, S., & Canavan, S. J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37745-7_11

Artificial Intelligence Augmented Medtech: Toward Personalized Patient Treatment

Neumann, D., Schlichting, S., Rackebrandt, K., & Klasen, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-32614-1_14

Faithful Counterfactual Visual Explanations (Fcve)Preprint

Khan, B., Tariq, S. A., Zia, T., Ahsan, M., & Windridge, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4663959

Estimating Challenges Of Atificial Inteligence (ML): Information Extraction Perspective

Siddiqui, A. T., Goyal, N., & Ahmed, M. N. (2023) · International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) · doi:10.1109/iccams60113.2023.10525977

Utilizations of AI in Cryptography

Meera, S., Kumar, S. D., Sharmikha Sree, R., A., K. R., & Deepika, R. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-4159-9.ch004

Development of an Effective Safety Assessment Capable of Measuring the Safety of AI-Based Systems.

Nyakundi, N. B. N., & Reza, H. (2024) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit60633.2024.10609839

A primer and practical recommendations for AI and machine learning terminology in medicine and behavioral sciences (Preprint)Preprint

Cao, B., Greiner, R., Greenshaw, A. J., & Sui, J. (2024) · doi:10.2196/preprints.66100

Assessing Aggressive Driving Behaviour Using Attention Based Models

Aechtner, J., Wilbik, A., & Popa, M. C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74650-5_4

The ( α, β )- k Boolean Signatures of Molecular Toxicity: Microcystin as a Case StudyPreprint

Moscato, P., Jaeger-Honz, S., Haque, M. N., & Schreiber, F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.29.630644

Rademacher complexity-based simplification approach to generate interpretable solutions with genetic programmingPreprint

Mejia-Zuluaga, P. A., García, L. F. D., & Valdiviezo-N., J. C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6233085

Proposition d’un outil d’aide à la décision adapté aux contraintes et aux enjeux d’un soutien informatique aux consultations médicales coutumières

Richard, A. (2021) · theses.fr (ABES) · doi:10.70675/e1f75a35z05f1z45cezaa76zab827023a534

Proof-oriented domain-specific language design for high-assurance software

Merigoux, D. (2021) · theses.fr (ABES) · doi:10.70675/95fceee8z06bbz48ccz8b06z87fb9e672ea8

Biomarker estimation from medical images : segmentation-based and segmentation-free approaches

Zhang, J. (2022) · theses.fr (ABES) · doi:10.70675/b7a14877zbf9cz4b9cza3dez50e1f164a611

Deep Learning for anomaly detection in industry 4.0

Keleko, A. T. (2022) · theses.fr (ABES) · doi:10.70675/c5883623za980z4e72z8275z2b478cb8b270

A Neural-Symbolic learning framework to produce interpretable predictions for image classification

Bennetot, A. (2022) · theses.fr (ABES) · doi:10.70675/2b59f598z2028z49afz8a20z29b21f19f394

Towards Reliable ML : Leveraging Multi-Modal Representations, Information Bottleneck and Extreme Value Theory

Atienza, N. (2025) · theses.fr (ABES) · doi:10.70675/81053b26z6d53z4f04zacc2zb3dd0156bba4

“Do I Trust the AI?” Towards Trustworthy AI-Assisted Diagnosis: Understanding User Perception in LLM-Supported Clinical Reasoning

Xu, Y., Zhu, Y., Wang, H., Wu, Y. G., Ouyang, Y., Li, H., Zhou, W., Liu, X., Jiang, C., & Li, Q. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790835 · Cited by 4

Natural language processing (NLP) and machine learning: An analysis of language learning and literary language creation through AI

Chauhan, D. S., & Vashist, S. (2026) · AIP conference proceedings · doi:10.1063/5.0329446

A novel framework for audio signal encryption via S-box cryptography and feature extraction

Rasheed, M. W., Mahboob, A., Najaf, D. E., & Mehmood, R. (2026) · Signal Image and Video Processing · doi:10.1007/s11760-026-05384-5

Artificial intelligence in customer service: applications and future directions

dos Reis, J. C. G. (2026) · Service Oriented Computing and Applications · doi:10.1007/s11761-026-00500-2

Explaining the Unexpected: The Role of Risk and Visual Information in Explanation Satisfaction for Autonomous Vehicles

Arisoni, A., Leite, I., & Yadollahi, E. (2026) · IEEE RO-MAN · doi:10.1109/ro-man72286.2026.11714911

Exploring the Logic of Counterfactuals for Explaining Algorithmic Decisions

Berz, C. (2026) · Topoi · doi:10.1007/s11245-026-10441-2

Explainable AI (ex-AI)

Holzinger, A. (2018) · Informatik-Spektrum · doi:10.1007/s00287-018-1102-5 · Cited by 67

From Machine Learning to Explainable AI

Holzinger, A. (2018) · 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490530 · Cited by 306

Scope and Sense of Explainability for AI-Systems

Leventi-Peetz, A.-M., Östreich, T., Lennartz, W., & Weber, K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82193-7_19 · Cited by 2

Explainable Artificial Intelligence: A Narrative Review from Rule-Based System Explanations to Post-hoc Attribution and Interpretable Models

Revista, Z., & IA, 1. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22172812

Explainable AI

Dix, A. J., & Finlay, J. (2025) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003082880-25