TY  - UNPD
A1  - Bauer, Kevin
A1  - Zahn, Moritz von
A1  - Hinz, Oliver
T1  - Please take over: Xai, delegation of authority, and domain knowledge
N2  - Recent regulatory measures such as the European Union’s AI Act re-quire artificial intelligence (AI) systems to be explainable. As such, under-standing how explainability impacts human-AI interaction and pinpoint-ing the specific circumstances and groups affected, is imperative. In this study, we devise a formal framework and conduct an empirical investiga-tion involving real estate agents to explore the complex interplay between explainability of and delegation to AI systems. On an aggregate level, our findings indicate that real estate agents display a higher propensity to delegate apartment evaluations to an AI system when its workings are explainable, thereby surrendering control to the machine. However, at an individual level, we detect considerable heterogeneity. Agents possess-ing extensive domain knowledge are generally more inclined to delegate decisions to AI and minimize their effort when provided with explana-tions. Conversely, agents with limited domain knowledge only exhibit this behavior when explanations correspond with their preconceived no-tions regarding the relationship between apartment features and listing prices. Our results illustrate that the introduction of explainability in AI systems may transfer the decision-making control from humans to AI under the veil of transparency, which has notable implications for policy makers and practitioners that we discuss.
T3  - SAFE working paper - 394 
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/70390
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-703907
UR  - https://ssrn.com/abstract=4512594
N1  - We gratefully acknowledge research support from the University of Mannheim, the Leibniz Institute for Financial Research SAFE, and the Goethe University Frankfurt.
PB  - SAFE
CY  - Frankfurt am Main
ER  -