Abstract
Is the activity of software neutral? Could the set of algorithms and big data which help the framework of an artificial intelligence in recognizing, classifying, or elaborating data be considered truly objective? As a matter of fact, data are never objective. Statistical models modify reality as they represent it. Furthermore, we are living in the era of learning machines: Robots are everyday more intelligent, useful, and independent. As they are implied in a long list of activities, we need to know which rules they do have to respect. Most of all, we need to establish who, and in what terms, could be responsible for their faults. That is why the European Union is elaborating a set of principles and rules on robots and artificial intelligence: New technologies have a great potential for the benefit of humanity, provided that ethics and law do not give up on ruling them.
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Essential Reading
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation).
European Parliament resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics (A8-0005/2017).
European Parliament resolution of 20 October 2020 with recommendations to the Commission on a framework of ethical aspects of artificial intelligence, robotics and related technologies (A9-0186/2020).
European Parliament resolution of 20 October 2020 with recommendations to the Commission on a civil liability regime for artificial intelligence (A9-0178/2020).
European Parliament resolution of 20 October 2020 on intellectual property rights for the development of artificial intelligence technologies (A9-1976/2020).
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Amato Mangiameli, A.C. (2021). Big Data Robotics and Rights. In: Cremades, J., Hermida, C. (eds) Encyclopedia of Contemporary Constitutionalism. Springer, Cham. https://doi.org/10.1007/978-3-319-31739-7_122-1
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DOI: https://doi.org/10.1007/978-3-319-31739-7_122-1
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