{"id":34652,"date":"2020-03-31T09:44:51","date_gmt":"2020-03-31T09:44:51","guid":{"rendered":"https:\/\/jums.academy\/l-szeli\/"},"modified":"2022-10-17T14:21:50","modified_gmt":"2022-10-17T14:21:50","slug":"l-szeli","status":"publish","type":"page","link":"https:\/\/jums.academy\/en\/l-szeli\/","title":{"rendered":"L. Szeli"},"content":{"rendered":"

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Junior Management Science, Volume 5, Issue 1, March 2020, 1-18<\/p>\n

UX in AI: Trust in Algorithm-based Investment Decisions
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Leon Szeli, University of Cambridge and Technical University Munich (Masterarbeit)<\/strong><\/p>\n<\/div>

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Abstract
\n<\/strong>This Thesis looks at investors\u2019 loss tolerance with portfolios managed by a human advisor compared to an algorithm with different degrees of humanization. The main goal is to explore differences between these groups (Humanized Algorithm, Dehumanized Algorithm, Humanized Human and Dehumanized Humans) and a potential diverging effect of humanizing. The Thesis is based on prior research (Hodge et al.,2018) but incorporates new aspects such as additional variables (demographics, prior experiences) and a comparison between users and non-users of automated-investment products. The core of this research is an experiment simulating an investment portfolio over time with four different portfolio managers. Subjects were asked to decide if they want to hold or sell a declining portfolio at \ufb01ve points in time to measure their loss tolerance. A cox regression model shows that portfolios managed by the Humanized Human had the highest loss tolerance. Humanizing leads to higher loss tolerance for the human advisor but to lower loss tolerance for algorithmic advisors within the non-user group.<\/p>\n<\/div>

Keywords: K\u00fcnstliche Intelligenz; Arti\ufb01cial Intelligence; Behavioral Finance; Behavioral Economics; Human-Computer-Interaction; User Experience; Investmententscheidungen; Nutzervertrauen.<\/em><\/p>\n<\/div>

DOI:\u00a0 https:\/\/doi.org\/10.5282\/jums\/v5i1pp1-18<\/a><\/em><\/p>\n<\/div>

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