Open Journal Systems

Artificial intelligence and decision problems: The need for an ethical context

José Luis Verdegay, M. Teresa Lamata, David Pelta, Carlos Cruz

Article ID: 1791
Vol 2, Issue 2, 2021

VIEWS - 256 (Abstract)

Abstract

Computers process information and make decisions. Until recently, the decisions they made were not complex, but due to the incessant technological advances that are taking place, systems based on artificial intelligence are achieving levels of competence in decision-making that in many contexts equal or surpass those of humans. These are autonomous decision-making systems that, although they can increase the capacity and efficiency of people in their fields of action, they could also replace them, something that is of concern to society as a whole. Avoiding dysfunctions in these systems is a priority social, scientific and technological objective, which requires theoretical models that include all the richness and variety of decision problems, that precisely define the elements that characterize them and that address the ethical principles that should guide their operation. This article describes each of these aspects in separate sections.

Keywords

artificial intelligence; autonomous decision systems; decision processes; decision problems; ethics; concurrency

Full Text:

PDF

References

1. Verdegay JL. What do algorithms think about? Opening Speech of the Academic Year 2019–2020 at the University of Granada [Internet]. Granada; 2019. Available from: https://digibug.ugr.es/handle/10481/ 57034

2. Bazaraa MS, Sherali HD, Shetty CM. Nonlinear programming: Theory and algorithms. Hoboken: John Wiley & Sons; 2013.

3. Lamata MT, Pelta DA, Verdegay JL. The role of the context in decision and optimization problems. In: Lesot MJ and Marsala C (editors). Fuzzy Approaches for soft computing and approximate reasoning: Theories and applications. studies in fuzziness and soft computing. New York: Springer Cham; 2021. p. 75–84.

4. European Group on Ethics in Science and New Technologies. Statement on artificial intelligence, robotics and ‘Autonomous’ systems. Brussels: European Commission; 2018. p. 24.

5. Future of Life Institute. AI principles [Internet]. 2017. Available from: https://futureoflife.org/ai-principles.

6. ENIA. National Artificial Intelligence Strategy [Internet]. 2020. Available from: https://www.lamon cloa.gob.es/presidente/actividades/Documents/2020/ENIAResumen2B.pdf

7. Ministry of Economic Affairs and Digital Transformation. Digital Spain 2025 Plan [Internet]. 2020. Available from: https://portal.mineco.gob.es/ RecursosArticulo/mineco/prensa/ficheros/noticias/2018/Agenda_Digital_2025.pdf

8. Ministry of Economic Affairs and Digital Transformation. National Digital Skills Plan [Internet]. 2020. Available from: https://portal. mineco.gob.es/RecursosArticulo/mineco/ministerio/ficheros/210127_plan_nacional_de_competencias_digitales.pdf.

9. Foot P. The problem of abortion and the doctrine of the double effect. Oxford Review 1967; 5: 5–15.

10. Simon H. The new science of management decision. Lower Manhattan: Harper & Brothers; 1960.

11. Rios S. Decision analysis. ICE editions. San Vicente del Raspeig: Instituto de Ciencias de la Educación; 1976.

12. Smithson M. Ignorance and uncertainty: Emerging paradigms. Berlin, Heidelberg: Springer Verlag; 1989.

13. Bellman RE, Zadeh LA. Decision-making in a fuzzy environment. Management Science 1970; 1: B–141–B–164.

14. Zadeh LA. Fuzzy sets. Information and Control 1965; 8(3): 338–353.

15. Thaler RH, Sunstein CR. Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press; 2008.

16. United Nations. Our common future: Brundtland Report. New York: United Nations; 1987.

17. Yankelevich D. Predicting a predicted future [Internet]. 2017. Available from: https://www. linkedin.com/pulse/prediciendo-un-futuro-predicho-daniel-yankelevich/?originalSubdomain=es

18. CSR Observatory. Corporate social responsibility (CSR) [Internet]. 2004. Available from: http://observatoriorsc.org/la-rsc-que-es/.

19. Hejase HJ, Hamdar B, Hashem F, et al. Decision making under stress: An exploratory study in Lebanon. Journal of Middle East and North Africa Sciences 2017; 3(12): 1–16.

20. Baum SD. Social choice ethics in artificial intelligence. Artficial Intelligence and Society 2020; 35(1): 165–176.

21. Govia L. Coproduction, ethics and artificial intelligence: A perspective from cultural anthropology. Journal of Digital Social Research 2020; 2(3): 42–64.

22. Hickok M. Lessons learned from AI ethics principles for future actions. Artificial Intelligence and Ethics 2021; 1(1): 41–47.

23. Nath R, Sahu V. The problem of machine ethics in artificial intelligence. Artificial Intelligence and Society 2020; 35(1): 103–111.

24. Larus J, Hankin C, Carson SG, et al. When computers decide: European recommendations on machine- learned automated decision making. Informatics Europe & EUACM 2018.

25. Strubell E, Ganesh A, McCallum A. Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics; 2019 Jul 28-Aug 2; Florence, Italy. Stroudsburg: Association for Computational Linguistics; 2019.

26. Schwartz R, Dodge J, Smith NA, et al. Green AI [Internet]. 2019. Available from: https://arxiv.org/abs /1907.10597

27. Dutton T. An overview of national AI strategies [Internet]. 2019. Available from: https://medium. com/politics-ai/an-overview-of-national-ai-strategies-2a70ec6edfd

DOI: https://doi.org/10.54517/met.v2i2.1791
(256 Abstract Views, 0 PDF Downloads)