On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Survey and critique of techniques for extracting rules from trained artificial neural networks

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From local explanations to global understanding with explainable AI for trees

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

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Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.03.012 · Cited by 3,291

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524

Definitions, methods, and applications in interpretable machine learning

Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167

The false hope of current approaches to explainable artificial intelligence in health care

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00208-9 · Cited by 1,724

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Explaining machine learning classifiers through diverse counterfactual explanations

Mothilal, R. K., Sharma, A., & Tan, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372850 · Cited by 1,233

Explainable artificial intelligence: A survey

Dosilovic, F. K., Brcic, M., & Hlupic, N. (2018) · 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) · doi:10.23919/mipro.2018.8400040 · Cited by 1,147

Explanation of machine learning models using shapley additive explanation and application for real data in hospital

Nohara, Y., Matsumoto, K., Soejima, H., & Nakashima, N. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106584 · Cited by 1,091

Drug discovery with explainable artificial intelligence

Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063

A Survey on the Explainability of Supervised Machine Learning

Burkart, N., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12228 · Cited by 1,008

To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making

Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124

Transformer Interpretability Beyond Attention Visualization

Chefer, H., Gur, S., & Wolf, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00084 · Cited by 903

GNNExplainer: Generating Explanations for Graph Neural Networks

Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019) · arXiv · doi:10.48550/arxiv.1903.03894 · Cited by 850

From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

Ahmed, I., Jeon, G., & Piccialli, F. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3146552 · Cited by 853

Explainable artificial intelligence: an analytical review

Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1424 · Cited by 843

The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies

Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103655 · Cited by 811

A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems

Mohseni, S., Zarei, N., & Ragan, E. D. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387166 · Cited by 785

Explaining Explanations in AI

Mittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794

Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011-2022)

Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107161 · Cited by 734

Machine learning for integrating data in biology and medicine: principles, practice, and opportunities

Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., & Hoffman, M. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.012 · Cited by 675

Bias and Fairness in Large Language Models: A Survey

Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 1,245

Manipulating and Measuring Model Interpretability

Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728

Self-Driving Laboratories for Chemistry and Materials Science

Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707

Explainability for Large Language Models: A Survey

Zhao, H., Chen, H., Yang, F., Liu, N., Deng, H., Cai, H., Wang, S., Yin, D., & Du, M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3639372 · Cited by 681

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

Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 626

Explainable machine learning in deployment

Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., & Eckersley, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3375624 · Cited by 633

Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges

Molnar, C., Casalicchio, G., & Bischl, B. (2020) · Communications in Computer and Information Science · doi:10.1007/978-3-030-65965-3_28 · Cited by 677

Captum: A unified and generic model interpretability library for PyTorch

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Survey of Explainable AI Techniques in Healthcare

Chaddad, A., Peng, J., Xu, J., & Bouridane, A. (2023) · Sensors · doi:10.3390/s23020634 · Cited by 591

A Review of Explainable Artificial Intelligence in Healthcare

Sadeghi, Z., Alizadehsani, R., CIFCI, M. A., Kausar, S., Rehman, R., Mahanta, P., Bora, P. K., Almasri, A., Alkhawaldeh, R. S., Hussain, S., et al. (2024) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109370 · Cited by 598

Bias in medical AI: Implications for clinical decision-making

Cross, J. L., Choma, M. A., & Onofrey, J. A. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000651 · Cited by 607

Trustworthy AI: From Principles to Practices

Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612

ERASER: A Benchmark to Evaluate Rationalized NLP Models

DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.408 · Cited by 561

Opening the black box: the promise and limitations of explainable machine learning in cardiology

Petch, J., Di, S., & Nelson, W. (2022) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.09.004 · Cited by 552

Consistent Individualized Feature Attribution for Tree Ensembles

Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430

Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376219 · Cited by 564

The promise of machine learning in predicting treatment outcomes in psychiatry

Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., et al. (2021) · World Psychiatry · doi:10.1002/wps.20882 · Cited by 531

Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Salahuddin, Z., Woodruff, H. C., Chatterjee, A., & Lambin, P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105111 · Cited by 508

Fairness in machine learning: A survey

Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646

Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

Das, A., & Rad, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11371 · Cited by 504

From explanations to feature selection: assessing SHAP values as feature selection mechanism

Marcilio, W. E., & Eler, D. M. (2020) · 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi51738.2020.00053 · Cited by 504

Developing clinical prediction models: a step-by-step guide

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Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?

Jacovi, A., & Goldberg, Y. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.386 · Cited by 503

Explainable artificial intelligence model to predict acute critical illness from electronic health records

Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access · doi:10.1109/access.2022.3204051 · Cited by 450

Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

Buhrmester, V., Münch, D., & Arens, M. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3040048 · Cited by 403

Deep learning in business analytics and operations research: Models, applications and managerial implications

Kraus, M., Feuerriegel, S., & Oztekin, A. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.09.018 · Cited by 392

Understanding the role of individual units in a deep neural network

Bau, D., Zhu, J.-Y., Strobelt, H., Lapedriza, A., Zhou, B., & Torralba, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907375117 · Cited by 375

Applications of Explainable Artificial Intelligence in Diagnosis and Surgery

Zhang, Y., Weng, Y., & Lund, J. (2022) · Diagnostics · doi:10.3390/diagnostics12020237 · Cited by 368

Explanations Can Reduce Overreliance on AI Systems During Decision-Making

Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., & Krishna, R. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579605 · Cited by 402

Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP

Wang, K., Tian, J., Zheng, C., Yang, H., Ren, J., Liu, Y., Han, Q., & Zhang, Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104813 · Cited by 361

Explanations in Autonomous Driving: A Survey

Omeiza, D., Webb, H., Jirotka, M., & Kunze, L. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3122865 · Cited by 355

Shapley values for feature selection: the good, the bad, and the axioms

Fryer, D., Strumke, I., & Nguyen, H. (2021) · IEEE Access · doi:10.1109/access.2021.3119110 · Cited by 347

On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

Lai, V., & Tan, C. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287590 · Cited by 377

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan, K. S. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100048 · Cited by 335

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records

Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30018-2 · Cited by 336

Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning

Wang, D., Zhang, C., Wang, B., Li, B., Wang, Q., Liu, D., Wang, H., Zhou, Y., Shi, L., Lan, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12281-8 · Cited by 331

InterpretML: A Unified Framework for Machine Learning Interpretability

Nori, H., Jenkins, S., Koch, P., & Caruana, R. (2019) · arXiv · doi:10.48550/arxiv.1909.09223 · Cited by 322

Deep learning algorithm predicts diabetic retinopathy progression in individual patients

Arcadu, F., Benmansour, F., Maunz, A., Willis, J., Haskova, Z., & Prunotto, M. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0172-3 · Cited by 305

Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making

Wang, X., & Yin, M. (2021) · 26th International Conference on Intelligent User Interfaces · doi:10.1145/3397481.3450650 · Cited by 320

SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk

Gramegna, A., & Giudici, P. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.752558 · Cited by 306

Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications

Chou, Y.-L., Moreira, C., Bruza, P., Ouyang, C., & Jorge, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.003 · Cited by 302

An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery

Magesh, P. R., Myloth, R. D., & Tom, R. J. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104041 · Cited by 295

The relationship between trust in AI and trustworthy machine learning technologies

Toreini, E., Aitken, M., Coopamootoo, K., Elliott, K., Zelaya, C. G., & van Moorsel, A. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372834 · Cited by 296

Improving performance of deep learning models with axiomatic attribution priors and expected gradients

Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S. M., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00343-w · Cited by 300

Explaining models: an empirical study of how explanations impact fairness judgment

Dodge, J., Liao, Q. V., Zhang, Y., Bellamy, R. K. E., & Dugan, C. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302310 · Cited by 286

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283

Interpretable and explainable AI (XAI) model for spatial drought prediction

Dikshit, A., & Pradhan, B. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2021.149797 · Cited by 272

Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations

Rong, Y., Leemann, T., Nguyen, T.-T., Fiedler, L., Qian, P., Unhelkar, V., Seidel, T., Kasneci, G., & Kasneci, E. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3331846 · Cited by 278

Artificial intelligence in cancer research, diagnosis and therapy

Elemento, O., Leslie, C., Lundin, J., & Tourassi, G. (2021) · Nature Reviews Cancer · doi:10.1038/s41568-021-00399-1 · Cited by 264

Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

Cheng, L., Varshney, K. R., & Liu, H. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12814 · Cited by 261

Trustworthy ai: A computational perspective

Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261

Explainable intrusion detection for cyber defences in the internet of things: opportunities and solutions

Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266

Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?

Hase, P., & Bansal, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.491 · Cited by 254

Towards Out-Of-Distribution Generalization: A Survey

Liu, J., Shen, Z., He, Y., Zhang, X., Xu, R., Yu, H., & Cui, P. (2021) · arXiv · doi:10.48550/arxiv.2108.13624 · Cited by 253

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

Amann, J., Vetter, D., Blomberg, S. N., Christensen, H. C., Coffee, M., Gerke, S., Gilbert, T. K., Hagendorff, T., Holm, S., Livne, M., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000016 · Cited by 251

A Survey on Deepfake Video Detection

Yu, P., Xia, Z., Fei, J., & Lu, Y. (2021) · IET Biometrics · doi:10.1049/bme2.12031 · Cited by 253

Four principles of explainable artificial intelligence

Phillips, P. J., Hahn, C. A., Fontana, P. C., Yates, A. N., Greene, K., Broniatowski, D. A., & Przybocki, M. A. (2021) · doi:10.6028/nist.ir.8312 · Cited by 262

The explanation game: Explaining machine learning models using shapley values

Merrick, L., & Taly, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57321-8_2 · Cited by 277

Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study

Jabbour, S., Fouhey, D., Shepard, S., Valley, T. S., Kazerooni, E. A., Banovic, N., Wiens, J., & Sjoding, M. W. (2023) · JAMA · doi:10.1001/jama.2023.22295 · Cited by 236

Problems with Shapley-value-based explanations as feature importance measures

Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2020) · arXiv · doi:10.48550/arxiv.2002.11097 · Cited by 228

3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas

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Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review

Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021) · IEEE Access · doi:10.1109/access.2021.3086230 · Cited by 209

A Review on Explainability in Multimodal Deep Neural Nets

Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204

Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3294090 · Cited by 196

A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices

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Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task

Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200

The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations

Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., & Detyniecki, M. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/388 · Cited by 194

How machine learning can help select capping layers to suppress perovskite degradation

Hartono, N. T. P., Thapa, J., Tiihonen, A., Oviedo, F., Batali, C., Yoo, J. J., Liu, Z., Li, R., Marrón, D. F., Bawendi, M. G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17945-4 · Cited by 189

Transparency: Motivations and Challenges

Weller, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-28954-6_2 · Cited by 198

Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends.

Hughes, R. T., Zhu, L., & Bednarz, T. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.604234 · Cited by 178

Explanations can be manipulated and geometry is to blame

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