Combining behavioral insights with artificial intelligence: New perspectives for technology assessment

Authors

DOI:

https://doi.org/10.14512/tatup.32.1.43

Keywords:

artificial intelligence, behavioral economics, human bias, policy decisions, uncertainty

Abstract

Policy decisions concerning technology applications can have far-reaching societal consequences. Rationality-enhancing procedures are thus essential to ensure that such decisions are in the best interest of society. We propose a novel framework addressing this challenge. It combines a structured approach to decision-making, the mediating assessments protocol (MAP), with artificial intelligence (AI) methods to mitigate human bias and handle uncertainty in a normative manner. We introduce the steps for implementing MAP and discuss how it can be complemented and improved by AI methods such as dynamic programming, reinforcement learning and natural language processing. As a potential practical application, we consider the construction of a new wind park in a community and highlight critical aspects warranting special caution.

References

Alesina, Alberto; Miano, Armando; Stantcheva, Stefanie (2020): The polarization of reality. In: AEA Papers and Proceedings 110, pp. 324–328. https://doi.org/10.1257/pandp.20201072 DOI: https://doi.org/10.1257/pandp.20201072

Barham, Bradford; Chavas, Jean-Paul; Fitz, Dylan; Rios-Salas, Vanessa; Schechter, Laura (2014): The roles of risk and ambiguity in technology adoption. In: Journal of Economic Behavior & Organization 97, pp. 204–218. https://doi.org/10.1016/j.jebo.2013.06.014 DOI: https://doi.org/10.1016/j.jebo.2013.06.014

Beiderbeck, Daniel; Frevel, Nicolas; von der Gracht, Heiko; Schmidt, Sascha; Schweitzer, Vera (2021): Preparing, conducting, and analyzing Delphi surveys. Cross-disciplinary practices, new directions, and advancements. In: MethodsX 8, p. 1–20. https://doi.org/10.1016/j.mex.2021.101401 DOI: https://doi.org/10.1016/j.mex.2021.101401

Bellman, Richard (2010): Dynamic programming. With a new introduction by Stuart Dreyfus. Princeton: Princeton University Press.

Bertsekas, Dimitri (2019): Reinforcement learning and optimal control. Belmont: Athena Scientific.

Bertsekas, Dimitri; Tsitsiklis, John (1996): Neuro-dynamic programming. Belmont: Athena Scientific.

Holt, Charles; Laury, Susan (2002): Risk aversion and incentive effects. In: American Economic Review 92 (5), pp. 1644–1655. https://doi.org/10.1257/000282802762024700 DOI: https://doi.org/10.1257/000282802762024700

Jurafsky, Dan; Martin, James (2014): Speech and language processing. An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River: Pearson.

Kahneman, Daniel (2011): Thinking, fast and slow. New York: Farrar, Straus and Giroux.

Kahneman, Daniel; Lovallo, Dan; Sibony, Olivier (2019): A structured approach to strategic decisions. In: MIT Sloan Management Review 60 (3), 04. 03. 2019, pp. 67–73. Available online at https://sloanreview.mit.edu/media-download/65293/a-structured-approach-to-strategic-decisions/, last accessed on 06. 02. 2023.

Kahneman, Daniel; Sibony, Olivier; Sunstein, Cass (2021): Noise. A flaw in human judgment. New York: Little, Brown Spark.

Katzenbach, Christian; Ulbricht, Lena (2019): Algorithmic governance. In: Internet Policy Review 8 (4), pp. 1–18. https://doi.org/10.14763/2019.4.1424 DOI: https://doi.org/10.14763/2019.4.1424

Kaupunkiympäristön, Helsingin; Yleissuunnittelu, Maankäytöön (2016): Wind power survey for Helsinki 2015, 23. 08. 2016. Available online at https://hri.fi/data/en_GB/dataset/helsingin-tuulivoimakysely-2015, last accessed on 06. 02. 2023.

Leiren, Merethe; Aakre, Stine; Linnerud, Kristin; Julsrud, Tom; Di Nucci, Maria-Rosaria; Krug, Michael (2020): Community acceptance of wind energy developments. Experience from wind energy scarce regions in Europe. In: Sustainability 12 (5), pp. 1–22. https://doi.org/10.3390/su12051754 DOI: https://doi.org/10.3390/su12051754

Lenk, Klaus (2018): Formen und Folgen algorithmischer Public Governance. In: Resa Mohabbat Kar, Basanta Thapa and Peter Parycek (eds.): (Un)berechenbar? Algorithmen und Automatisierung in Staat und Gesellschaft, pp. 228–267. Berlin: Kompetenzzentrum Öffentliche IT. Available online at https://www.oeffentliche-it.de/documents/10181/14412/(Un)berechenbar+-+Algorithmen+und+Automatisierung+in+Staat+und+Gesellschaft, last accessed on 06. 02. 2023.

Russell, Stuart; Norvig, Peter (2021): Artificial intelligence: A modern approach. Harlow: Pearson.

Stolwijk, Sjoerd; Vis, Barbara (2020): Politicians, the representativeness heuristic and decision-making biases. In: Political Behavior 43 (4), pp. 1411–1432. https://doi.org/10.1007/s11109-020-09594-6 DOI: https://doi.org/10.1007/s11109-020-09594-6

Sutton, Richard; Barto, Andrew (2018): Reinforcement learning. An introduction. Cambridge: MIT Press.

Wiering, Marco; van Otterlo, Martijn (eds.) (2012): Reinforcement learning. State-of-the-art. Softcover reprint. Heidelberg: Springer. https://doi.org/10.1007/978-3-642-27645-3 DOI: https://doi.org/10.1007/978-3-642-27645-3

Downloads

Published

23.03.2023

How to Cite

1.
Horvath L, Renz E, Rohwer C, Schury D. Combining behavioral insights with artificial intelligence: New perspectives for technology assessment. TATuP [Internet]. 2023 Mar. 23 [cited 2023 Jun. 9];32(1):43-8. Available from: https://www.tatup.de/index.php/tatup/article/view/7042