Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T04:33:26.441Z Has data issue: false hasContentIssue false
  • English
  • Français

Human-like machines: Transparency and comprehensibility

Published online by Cambridge University Press:  10 November 2017

Piotr M. Patrzyk ,
Daniela Link  and
Julian N. Marewski
Piotr M. Patrzyk
Affiliation:
Faculty of Business and Economics, University of Lausanne, Quartier UNIL-Dorigny, Internef, CH-1015 Lausanne, Switzerland. piotr.patrzyk@unil.chdaniela.link@unil.chjulian.marewski@unil.ch
Daniela Link
Affiliation:
Faculty of Business and Economics, University of Lausanne, Quartier UNIL-Dorigny, Internef, CH-1015 Lausanne, Switzerland. piotr.patrzyk@unil.chdaniela.link@unil.chjulian.marewski@unil.ch
Julian N. Marewski
Affiliation:
Faculty of Business and Economics, University of Lausanne, Quartier UNIL-Dorigny, Internef, CH-1015 Lausanne, Switzerland. piotr.patrzyk@unil.chdaniela.link@unil.chjulian.marewski@unil.ch
Get access Rights & Permissions [Opens in a new window]

Abstract

Artificial intelligence algorithms seek inspiration from human cognitive systems in areas where humans outperform machines. But on what level should algorithms try to approximate human cognition? We argue that human-like machines should be designed to make decisions in transparent and comprehensible ways, which can be achieved by accurately mirroring human cognitive processes.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e276
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arnold, T. & Scheutz, M. (2016) Against the moral Turing test: Accountable design and the moral reasoning of autonomous systems. Ethics and Information Technology 18(2):103–15. doi:10.1007/s10676-016-9389-x.CrossRefGoogle Scholar
Bennis, W. M., Medin, D. L. & Bartels, D. M. (2010) The costs and benefits of calculation and moral rules. Perspectives on Psychological Science 5(2):187–202. doi:10.1177/1745691610362354.CrossRefGoogle ScholarPubMed
Burrell, J. (2016) How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society 3(1):1–12. doi:10.1177/2053951715622512.CrossRefGoogle Scholar
Gigerenzer, G. (2001) The adaptive toolbox. In: Bounded rationality: The adaptive toolbox, ed. Gigerenzer, G. & Selten, R., pp. 37–50. MIT Press.Google Scholar
Gigerenzer, G. & Gaissmaier, W. (2011) Heuristic decision making. Annual Review of Psychology 62:451–82. doi:10.1146/annurev-psych-120709-145346.CrossRefGoogle ScholarPubMed
Hafenbrädl, S., Waeger, D., Marewski, J. N. & Gigerenzer, G. (2016) Applied decision making with fast-and-frugal heuristics. Journal of Applied Research in Memory and Cognition 5(2):215–31. doi:10.1016/j.jarmac.2016.04.011.CrossRefGoogle Scholar
Hertwig, R. & Herzog, S. M. (2009) Fast and frugal heuristics: Tools of social rationality. Social Cognition 27(5):661–98. doi:10.1521/soco.2009.27.5.661.CrossRefGoogle Scholar
Hoffman, M., Yoeli, E. & Nowak, M. A. (2015) Cooperate without looking: Why we care what people think and not just what they do. Proceedings of the National Academy of Sciences of the United States of America 112(6):1727–32. doi:10.1073/pnas.1417904112.CrossRefGoogle ScholarPubMed
Indurkhya, B. & Misztal-Radecka, J. (2016) Incorporating human dimension in autonomous decision-making on moral and ethical issues. In: Proceedings of the AAAI Spring Symposium: Ethical and Moral Considerations in Non-human Agents, Palo Alto, CA, ed. Indurkhya, B. & Stojanov, G.. AAAI Press.Google Scholar
Jara-Ettinger, J., Gweon, H., Schulz, L. E. & Tenenbaum, J. B. (2016) The naïve utility calculus: Computational principles underlying commonsense psychology. Trends in Cognitive Sciences 20(8):589–604. doi:10.1016/j.tics.2016.05.011.CrossRefGoogle ScholarPubMed
Keller, N. & Katsikopoulos, K. V. (2016) On the role of psychological heuristics in operational research; and a demonstration in military stability operations. European Journal of Operational Research 249(3):1063–73. doi:10.1016/j.ejor.2015.07.023.CrossRefGoogle Scholar
Malle, B. F. & Scheutz, M. (2014) Moral competence in social robots. In: Proceedings of the 2014 IEEE International Symposium on Ethics in Science, Technology and Engineering. IEEE. doi:10.1109/ETHICS.2014.6893446.CrossRefGoogle Scholar
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. & Floridi, L. (2016) The ethics of algorithms: Mapping the debate. Big Data & Society 3(2):1–21. doi:10.1177/2053951716679679.CrossRefGoogle Scholar
Todd, P. M. & Gigerenzer, G. (2007) Environments that make us smart: Ecological rationality. Current Directions in Psychological Science 16(3):167–71. doi:10.1111/j.1467-8721.2007.00497.x.CrossRefGoogle Scholar