Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
•We review concepts related to the explainability of AI methods (XAI).•We comprehensive analyze the XAI literature organized in two taxonomies.•We identify future research directions of the XAI field.•We discuss potential implications of XAI and privacy in data fusion contexts.•We identify Responsib...
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| Vydáno v: | Information fusion Ročník 58; s. 82 - 115 |
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| Hlavní autoři: | , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.06.2020
Elsevier |
| Témata: | |
| ISSN: | 1566-2535, 1872-6305 |
| On-line přístup: | Získat plný text |
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| Abstract | •We review concepts related to the explainability of AI methods (XAI).•We comprehensive analyze the XAI literature organized in two taxonomies.•We identify future research directions of the XAI field.•We discuss potential implications of XAI and privacy in data fusion contexts.•We identify Responsible AI as a concept promoting XAI and other AI principles in practical settings.
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability. |
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| AbstractList | •We review concepts related to the explainability of AI methods (XAI).•We comprehensive analyze the XAI literature organized in two taxonomies.•We identify future research directions of the XAI field.•We discuss potential implications of XAI and privacy in data fusion contexts.•We identify Responsible AI as a concept promoting XAI and other AI principles in practical settings.
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability. In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability. |
| Author | Chatila, Raja Gil-Lopez, Sergio Barredo Arrieta, Alejandro Benjamins, Richard Molina, Daniel Díaz-Rodríguez, Natalia Barbado, Alberto Del Ser, Javier Garcia, Salvador Herrera, Francisco Bennetot, Adrien Tabik, Siham |
| Author_xml | – sequence: 1 givenname: Alejandro surname: Barredo Arrieta fullname: Barredo Arrieta, Alejandro organization: TECNALIA, Derio 48160, Spain – sequence: 2 givenname: Natalia surname: Díaz-Rodríguez fullname: Díaz-Rodríguez, Natalia organization: ENSTA, Institute Polytechnique Paris and INRIA Flowers Team, Palaiseau, France – sequence: 3 givenname: Javier surname: Del Ser fullname: Del Ser, Javier email: javier.delser@tecnalia.com organization: TECNALIA, Derio 48160, Spain – sequence: 4 givenname: Adrien surname: Bennetot fullname: Bennetot, Adrien organization: ENSTA, Institute Polytechnique Paris and INRIA Flowers Team, Palaiseau, France – sequence: 5 givenname: Siham surname: Tabik fullname: Tabik, Siham organization: DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada 18071, Spain – sequence: 6 givenname: Alberto surname: Barbado fullname: Barbado, Alberto organization: Telefonica, Madrid 28050, Spain – sequence: 7 givenname: Salvador surname: Garcia fullname: Garcia, Salvador organization: DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada 18071, Spain – sequence: 8 givenname: Sergio surname: Gil-Lopez fullname: Gil-Lopez, Sergio organization: TECNALIA, Derio 48160, Spain – sequence: 9 givenname: Daniel surname: Molina fullname: Molina, Daniel organization: DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada 18071, Spain – sequence: 10 givenname: Richard surname: Benjamins fullname: Benjamins, Richard organization: Telefonica, Madrid 28050, Spain – sequence: 11 givenname: Raja surname: Chatila fullname: Chatila, Raja organization: Institut des Systèmes Intelligents et de Robotique, Sorbonne Universitè, France – sequence: 12 givenname: Francisco surname: Herrera fullname: Herrera, Francisco organization: DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada 18071, Spain |
| BackLink | https://hal.science/hal-02381211$$DView record in HAL |
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| Keywords | Explainable Artificial Intelligence Privacy Data Fusion Comprehensibility Transparency Interpretability Accountability Machine Learning Responsible Artificial Intelligence Deep Learning Fairness |
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| Snippet | •We review concepts related to the explainability of AI methods (XAI).•We comprehensive analyze the XAI literature organized in two taxonomies.•We identify... In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across... |
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| SubjectTerms | Accountability Artificial Intelligence Comprehensibility Computer Science Data Fusion Deep Learning Explainable Artificial Intelligence Fairness Interpretability Machine Learning Privacy Responsible Artificial Intelligence Transparency |
| Title | Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI |
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