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In this Chapter explores the ethical and technical dimensions of data mining and artificial intelligence (AI), focusing on privacy concerns, explainable AI (XAI), and fostering trust in AI systems. It emphasizes the need for responsible data practices, including adherence to legal frameworks like GDPR and CCPA, obtaining informed consent, and implementing transparency in data usage policies. The chapter also explores the problem related to the low transparency of contemporary data mining methods, introducing methodologies of explainable AI. The XAI tools like SHAP, LIME, and ELI5 offer methods to interpret and justify AI decisions, enhancing trust and accountability. The Python lab guides readers in providing a global and a local explanation for the results of the Naïve Bayes newspaper article classifier developed in Chapter 7 and the Covid-19 BERT sentiment analyzer developed in Chapter 14 .

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Ethics and Explainable AI

  • 章节

  • 发表日期：

    2026年

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  • Andrei P. Kirilenko

摘要

In this Chapter explores the ethical and technical dimensions of data mining and artificial intelligence (AI), focusing on privacy concerns, explainable AI (XAI), and fostering trust in AI systems. It emphasizes the need for responsible data practices, including adherence to legal frameworks like GDPR and CCPA, obtaining informed consent, and implementing transparency in data usage policies. The chapter also explores the problem related to the low transparency of contemporary data mining methods, introducing methodologies of explainable AI. The XAI tools like SHAP, LIME, and ELI5 offer methods to interpret and justify AI decisions, enhancing trust and accountability. The Python lab guides readers in providing a global and a local explanation for the results of the Naïve Bayes newspaper article classifier developed in Chapter 7 and the Covid-19 BERT sentiment analyzer developed in Chapter 14 .