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Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

2023 · Information Fusion · 1,679 citations · 0 from inside this corpus

Sajid Ali, Tamer Abuhmed, Shaker El–Sappagh, Khan Muhammad, José M. Alonso, Roberto Confalonieri, Riccardo Guidotti, Javier Del Ser, Natalia Díaz-Rodríguez, Francisco Herrera

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw… (2023)Explainable Artificial Intell…Fuzzy sets (1965)Fuzzy sets"Why Should I Trust You?" (2016)"Why Should I Trust You?"Advances in Neural Information Processing Systems 28 (2015)Advances in Neural Informatio…Distilling the Knowledge in a Neural Network (2015)Distilling the Knowledge in a…From local explanations to global understanding with explainable AI for trees (2020)From local explanations to gl…Stop explaining black box machine learning models for high stakes decisions and use inter… (2019)Stop explaining black box mac…Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe… (2019)Explainable Artificial Intell…Outline of a New Approach to the Analysis of Complex Systems and Decision Processes (1973)Outline of a New Approach to …Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018)Peeking Inside the Black-Box:…Deep learning and process understanding for data-driven Earth system science (2019)Deep learning and process und…Machine Learning: Algorithms, Real-World Applications and Research Directions (2021)Machine Learning: Algorithms,…Structure‐Mapping: A Theoretical Framework for Analogy* (1983)Structure‐Mapping: A Theoreti…A survey of methods for explaining black box models (2019)A survey of methods for expla…Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) (2001)Statistical Modeling: The Two…Towards A Rigorous Science of Interpretable Machine Learning (2017)Towards A Rigorous Science of…Explainable AI: A Review of Machine Learning Interpretability Methods (2020)Explainable AI: A Review of M…Methods for interpreting and understanding deep neural networks (2017)Methods for interpreting and …Catastrophic forgetting in connectionist networks (1999)Catastrophic forgetting in co…Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (2016)Proceedings of the 2016 Confe…Definitions, methods, and applications in interpretable machine learning (2019)Definitions, methods, and app…A simple and fast algorithm for K-medoids clustering (2008)A simple and fast algorithm f…Anchors: High-Precision Model-Agnostic Explanations (2018)XAI—Explainable artificial intelligence (2019)XAI—Explainable artificial in…Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual C… (2014)Peeking Inside the Black Box:…Machine Learning Interpretability: A Survey on Methods and Metrics (2019)Machine Learning Interpretabi…The false hope of current approaches to explainable artificial intelligence in health care (2021)The false hope of current app…Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models (2020)Visualizing the Effects of Pr…Explaining nonlinear classification decisions with deep Taylor decomposition (2016)Explaining nonlinear classifi…Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019)Explainable AI: Interpreting,…Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks (2020)Score-CAM: Score-Weighted Vis…
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What this paper cites, inside the corpus

PaperYearCited
Fuzzy sets196567,093
"Why Should I Trust You?"201616,210
Advances in Neural Information Processing Systems 28201514,672
Distilling the Knowledge in a Neural Network201514,099
From local explanations to global understanding with explainable AI for trees20209,936
Stop explaining black box machine learning models for high stakes decisions and use inter…20199,924
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe…20199,796
Outline of a New Approach to the Analysis of Complex Systems and Decision Processes19738,748
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)20186,166
Deep learning and process understanding for data-driven Earth system science20195,475
Machine Learning: Algorithms, Real-World Applications and Research Directions20215,368
Structure‐Mapping: A Theoretical Framework for Analogy*19835,087
A survey of methods for explaining black box models20194,992
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)20014,362
Towards A Rigorous Science of Interpretable Machine Learning20173,190
Explainable AI: A Review of Machine Learning Interpretability Methods20202,907
Methods for interpreting and understanding deep neural networks20172,763
Catastrophic forgetting in connectionist networks19992,358
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing20162,293
Definitions, methods, and applications in interpretable machine learning20192,161
A simple and fast algorithm for K-medoids clustering20082,129
Anchors: High-Precision Model-Agnostic Explanations20182,124
XAI—Explainable artificial intelligence20192,002
Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual C…20141,838
Machine Learning Interpretability: A Survey on Methods and Metrics20191,822
The false hope of current approaches to explainable artificial intelligence in health care20211,572
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models20201,450
Explaining nonlinear classification decisions with deep Taylor decomposition20161,413
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning20191,386
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks20201,378

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Explainable Artificial Intelligence (XAI)Computer Science
Artificial Intelligence in Healthcare and EducationMedicine
Machine Learning in HealthcareComputer Science

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