Explainable AI has recently gained momentum as an approach to overcome some of the more obvious ethical implications of the increasingly widespread application of AI (mostly machine learning). It is however not always completely evident whether providing explanations actually achieves to overcome those ethical issues, or rather create a false sense of control and transparency. This and other possible misuses of Explainable AI leads to the need to consider the possibility that providing explanations might itself represent a risk with respect to ethical implications at several levels. In this chapter, we explore through a series of scenarios how explanations in certain circunstances might affect negatively specific ethical values, from human agency to fairness. Through those scenarios, we discuss the need to consider ethical implications in the design and deployment of Explainable AI systems, focusing on how knowledge-based approaches can offer elements of solutions to the issues raised. We conclude on the requirements for ethical explanations, and on how hybrid-systems, combining machine learning with background knowledge, offer a way towards achieving those requirements.
{"title":"The Data Ethics Challenges of Explainable AI and Their Knowledge-Based Solutions","authors":"M. d’Aquin","doi":"10.3233/SSW200023","DOIUrl":"https://doi.org/10.3233/SSW200023","url":null,"abstract":"Explainable AI has recently gained momentum as an approach to overcome some of the more obvious ethical implications of the increasingly widespread application of AI (mostly machine learning). It is however not always completely evident whether providing explanations actually achieves to overcome those ethical issues, or rather create a false sense of control and transparency. This and other possible misuses of Explainable AI leads to the need to consider the possibility that providing explanations might itself represent a risk with respect to ethical implications at several levels. In this chapter, we explore through a series of scenarios how explanations in certain circunstances might affect negatively specific ethical values, from human agency to fairness. Through those scenarios, we discuss the need to consider ethical implications in the design and deployment of Explainable AI systems, focusing on how knowledge-based approaches can offer elements of solutions to the issues raised. We conclude on the requirements for ethical explanations, and on how hybrid-systems, combining machine learning with background knowledge, offer a way towards achieving those requirements.","PeriodicalId":331476,"journal":{"name":"Knowledge Graphs for eXplainable Artificial Intelligence","volume":"120 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128487951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Knowledge-Based Explanations for Transfer Learning","authors":"F. Lécué, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen","doi":"10.3233/SSW200018","DOIUrl":"https://doi.org/10.3233/SSW200018","url":null,"abstract":"","PeriodicalId":331476,"journal":{"name":"Knowledge Graphs for eXplainable Artificial Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129287220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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