On conflicts between ethical and logical principles in artificial intelligence
Artificial intelligence is nowadays a reality. Setting rules on the potential outcomes of intelligent machines, so that no surprise can be expected by humans from the behavior of those machines, is becoming a priority for policy makers. In its recent Communication “Artificial Intelligence for Europe...
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| Vydáno v: | AI & society Ročník 35; číslo 4; s. 895 - 900 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.12.2020
Springer Springer Nature B.V |
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| ISSN: | 0951-5666, 1435-5655 |
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| Abstract | Artificial intelligence is nowadays a reality. Setting rules on the potential outcomes of intelligent machines, so that no surprise can be expected by humans from the behavior of those machines, is becoming a priority for policy makers. In its recent Communication “Artificial Intelligence for Europe” (EU Commission
2018
), for instance, the European Commission identifies the distinguishing trait of an intelligent machine in the presence of “
a certain degree of autonomy
” in decision making, in the light of the context. The crucial issue to be addressed is, therefore, whether it is possible to identify a set of rules for data use by intelligent machines so that the decision-making autonomy of machines can allow for humans’ traditional informational self-determination (humans provide machines only with the data they decide to), as enshrined in many existing legal frameworks (including, for personal data protection, the EU’s General Data Protection Regulation) (EU Parliament and Council
2016
) and can actually turn out to be further beneficial to individuals. Governing the autonomy of machines can be a very ambitious goal for humans since machines are geared first to the principles of formal logic and then—possibly—to ethical or legal principles. This introduces an unprecedented degree of complexity in how a norm should be engineered, which requires, in turn, an in-depth reflection in order to prevent conflicts between the legal and ethical principles underlying humans’ civil coexistence and the rules of formal logic upon which the functioning of machines is based (EU Parliament
2017
). |
|---|---|
| AbstractList | Artificial intelligence is nowadays a reality. Setting rules on the potential outcomes of intelligent machines, so that no surprise can be expected by humans from the behavior of those machines, is becoming a priority for policy makers. In its recent Communication “Artificial Intelligence for Europe” (EU Commission 2018), for instance, the European Commission identifies the distinguishing trait of an intelligent machine in the presence of “a certain degree of autonomy” in decision making, in the light of the context. The crucial issue to be addressed is, therefore, whether it is possible to identify a set of rules for data use by intelligent machines so that the decision-making autonomy of machines can allow for humans’ traditional informational self-determination (humans provide machines only with the data they decide to), as enshrined in many existing legal frameworks (including, for personal data protection, the EU’s General Data Protection Regulation) (EU Parliament and Council 2016) and can actually turn out to be further beneficial to individuals. Governing the autonomy of machines can be a very ambitious goal for humans since machines are geared first to the principles of formal logic and then—possibly—to ethical or legal principles. This introduces an unprecedented degree of complexity in how a norm should be engineered, which requires, in turn, an in-depth reflection in order to prevent conflicts between the legal and ethical principles underlying humans’ civil coexistence and the rules of formal logic upon which the functioning of machines is based (EU Parliament 2017). Artificial intelligence is nowadays a reality. Setting rules on the potential outcomes of intelligent machines, so that no surprise can be expected by humans from the behavior of those machines, is becoming a priority for policy makers. In its recent Communication "Artificial Intelligence for Europe" (EU Commission 2018 (See CR3)), for instance, the European Commission identifies the distinguishing trait of an intelligent machine in the presence of "a certain degree of autonomy" in decision making, in the light of the context. The crucial issue to be addressed is, therefore, whether it is possible to identify a set of rules for data use by intelligent machines so that the decision-making autonomy of machines can allow for humans' traditional informational self-determination (humans provide machines only with the data they decide to), as enshrined in many existing legal frameworks (including, for personal data protection, the EU's General Data Protection Regulation) (EU Parliament and Council 2016 (See CR9)) and can actually turn out to be further beneficial to individuals. Governing the autonomy of machines can be a very ambitious goal for humans since machines are geared first to the principles of formal logic and then-possibly-to ethical or legal principles. This introduces an unprecedented degree of complexity in how a norm should be engineered, which requires, in turn, an in-depth reflection in order to prevent conflicts between the legal and ethical principles underlying humans' civil coexistence and the rules of formal logic upon which the functioning of machines is based (EU Parliament 2017 (See CR14)). Artificial intelligence is nowadays a reality. Setting rules on the potential outcomes of intelligent machines, so that no surprise can be expected by humans from the behavior of those machines, is becoming a priority for policy makers. In its recent Communication “Artificial Intelligence for Europe” (EU Commission 2018 ), for instance, the European Commission identifies the distinguishing trait of an intelligent machine in the presence of “ a certain degree of autonomy ” in decision making, in the light of the context. The crucial issue to be addressed is, therefore, whether it is possible to identify a set of rules for data use by intelligent machines so that the decision-making autonomy of machines can allow for humans’ traditional informational self-determination (humans provide machines only with the data they decide to), as enshrined in many existing legal frameworks (including, for personal data protection, the EU’s General Data Protection Regulation) (EU Parliament and Council 2016 ) and can actually turn out to be further beneficial to individuals. Governing the autonomy of machines can be a very ambitious goal for humans since machines are geared first to the principles of formal logic and then—possibly—to ethical or legal principles. This introduces an unprecedented degree of complexity in how a norm should be engineered, which requires, in turn, an in-depth reflection in order to prevent conflicts between the legal and ethical principles underlying humans’ civil coexistence and the rules of formal logic upon which the functioning of machines is based (EU Parliament 2017 ). |
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| Author | D’Acquisto, Giuseppe |
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| Cites_doi | 10.1007/1-4020-8090-5_13 10.1145/3287560.3287571 10.24963/ijcai.2017/32 10.1007/978-94-017-2219-3_3 10.1145/3333611 10.4159/harvard.9780674736061 10.1038/nature16961 |
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| Keywords | Value alignment Formal logic constraints Algorithm transparency vs. explainability Machine incompleteness Artificial intelligence ethics |
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| References | D’Acquisto G, Naldi M (2017) Big data e privacy by design. Anonimizzazione, Pseudonimizzazione, Sicurezza, Giappichelli MacKay DJC (1992) Bayesian Interpolation. In: Smith CR, Erickson GJ, Neudorfer PO (eds) Maximum entropy and bayesian methods. Fundamental Theories of Physics (An International Book Series on The Fundamental Theories of Physics: Their Clarification, Development and Application), vol 50. Springer, Dordrecht, pp 36–66 Severino E (1988) La tendenza fondamentale del nostro tempo. Adelphi Gödel K (1931) Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme, I. Monatshefte für Mathematik und Physik, 38 EU Parliament (2019) Report on a comprehensive European industrial policy on artificial intelligence and robotics (2018/2088(INI)) EU Parliament and Council (2016) 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 EU Commission (2018) Communication from the Commission to the European Parliament, the European Council, the Council, the European economic and social committee and the Committee of the regions, Artificial Intelligence for Europe, COM/2018/0237 Association for Computing Machinery (ACM) U.S. Public Policy Council (2017) Algorithmic transparency and accountability, discussion panel event, 14 September 2017 Elzayn H, Jabbari S, Jung C, Kearns M, Neel S, Roth A, Schutzman Z (2019) Fair algorithms for learning in allocation problems, ACM conference on fairness, accountability and transparency EU Parliament (2017) European Parliament resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)) Odlyzko A (2019) Cybersecurity is not very important. ACM Ubiquity, June 2019 Hadfield-Menell D, Dragan A, Abbeel P, Russell S (2017) The off-switch game. In: International joint conference on artificial intelligence Russell S (2017) Provably beneficial artificial intelligence. OECD conference “AI: intelligent machines, smart policies”, Paris 26–27 Oct 2017 Sover A (2018) The languages of humor verbal, visual, and physical humor. Bloomsbury Academic IEEE Ethically Aligned Design (2019) A vision for prioritizing human well-being with autonomous and intelligent systems, March 2019 de La Boëtie E (1576) Discours de la Servitude Volontaire Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of Go with deep neural networks and tree search. Nature volume 529 Pasquale F (2015) The black box society: the secret algorithms that control money and information. Harvard University Press, Harvard Acquisti A, Grossklags J (2004) Privacy attitudes and privacy behavior—losses, gains, and hyperbolic discountin. Economics of Information Security, p 165–178 EU Commission (2019) The European Commission’s high-level expert group on artificial intelligence, ethics guidelines for trustworthy AI 927_CR10 927_CR8 927_CR20 927_CR1 927_CR2 927_CR3 927_CR4 927_CR5 927_CR6 927_CR7 cr-split#-927_CR9.2 cr-split#-927_CR9.1 927_CR19 927_CR18 927_CR17 927_CR16 927_CR15 927_CR14 927_CR13 927_CR12 927_CR11 |
| References_xml | – reference: Odlyzko A (2019) Cybersecurity is not very important. ACM Ubiquity, June 2019 – reference: Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D (2016) Mastering the game of Go with deep neural networks and tree search. Nature volume 529 – reference: EU Commission (2018) Communication from the Commission to the European Parliament, the European Council, the Council, the European economic and social committee and the Committee of the regions, Artificial Intelligence for Europe, COM/2018/0237 – reference: MacKay DJC (1992) Bayesian Interpolation. In: Smith CR, Erickson GJ, Neudorfer PO (eds) Maximum entropy and bayesian methods. Fundamental Theories of Physics (An International Book Series on The Fundamental Theories of Physics: Their Clarification, Development and Application), vol 50. Springer, Dordrecht, pp 36–66 – reference: Acquisti A, Grossklags J (2004) Privacy attitudes and privacy behavior—losses, gains, and hyperbolic discountin. Economics of Information Security, p 165–178 – reference: Hadfield-Menell D, Dragan A, Abbeel P, Russell S (2017) The off-switch game. In: International joint conference on artificial intelligence – reference: EU Parliament (2019) Report on a comprehensive European industrial policy on artificial intelligence and robotics (2018/2088(INI)) – reference: EU Parliament and Council (2016) 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 – reference: Severino E (1988) La tendenza fondamentale del nostro tempo. Adelphi – reference: EU Parliament (2017) European Parliament resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)) – reference: IEEE Ethically Aligned Design (2019) A vision for prioritizing human well-being with autonomous and intelligent systems, March 2019 – reference: Sover A (2018) The languages of humor verbal, visual, and physical humor. Bloomsbury Academic – reference: EU Commission (2019) The European Commission’s high-level expert group on artificial intelligence, ethics guidelines for trustworthy AI – reference: Gödel K (1931) Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme, I. Monatshefte für Mathematik und Physik, 38 – reference: Association for Computing Machinery (ACM) U.S. Public Policy Council (2017) Algorithmic transparency and accountability, discussion panel event, 14 September 2017 – reference: Elzayn H, Jabbari S, Jung C, Kearns M, Neel S, Roth A, Schutzman Z (2019) Fair algorithms for learning in allocation problems, ACM conference on fairness, accountability and transparency – reference: D’Acquisto G, Naldi M (2017) Big data e privacy by design. Anonimizzazione, Pseudonimizzazione, Sicurezza, Giappichelli – reference: de La Boëtie E (1576) Discours de la Servitude Volontaire – reference: Pasquale F (2015) The black box society: the secret algorithms that control money and information. Harvard University Press, Harvard – reference: Russell S (2017) Provably beneficial artificial intelligence. OECD conference “AI: intelligent machines, smart policies”, Paris 26–27 Oct 2017 – ident: #cr-split#-927_CR9.1 – ident: 927_CR1 doi: 10.1007/1-4020-8090-5_13 – ident: #cr-split#-927_CR9.2 – ident: 927_CR20 – ident: 927_CR7 doi: 10.1145/3287560.3287571 – ident: 927_CR11 doi: 10.24963/ijcai.2017/32 – ident: 927_CR8 – ident: 927_CR2 – ident: 927_CR5 – ident: 927_CR3 – ident: 927_CR4 – ident: 927_CR6 – ident: 927_CR12 doi: 10.1007/978-94-017-2219-3_3 – ident: 927_CR13 doi: 10.1145/3333611 – ident: 927_CR14 – ident: 927_CR15 – ident: 927_CR16 doi: 10.4159/harvard.9780674736061 – ident: 927_CR10 – ident: 927_CR19 doi: 10.1038/nature16961 – ident: 927_CR17 – ident: 927_CR18 |
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