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Trust Dynamics in Financial Decision Making: Behavioral Responses to AI and Human Expert Advice Following Structural
Hyo Young Kim1, Young Soo Park2
1Department of Artificial Intelligence, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si 15073, Republic of Korea.
Trust in financial forecasting AI and human experts decreases after market structural breaks. Level shifts impact trust more than outliers, with human experts experiencing a greater decline than AI systems.
Area of Science:
- Behavioral Economics
- Artificial Intelligence Ethics
- Financial Markets
Background:
- Understanding trust in AI vs. human experts is crucial for decision-making.
- Market structural breaks introduce uncertainty, challenging existing trust dynamics.
- Theoretical frameworks like attribution theory and algorithm aversion inform psychological responses.
Purpose of the Study:
- To compare trust in AI and human experts during financial forecasting under market structural changes.
- To investigate the differential impact of Additive Outliers and Level Shifts on trust.
- To analyze psychological responses to AI and human advice amidst uncertainty.
Main Methods:
- A controlled online experiment with 157 participants recruited via Amazon Mechanical Turk (MTurk).
- Participants forecasted stock prices, randomly assigned to AI or human expert conditions.
- Exposure to different structural break scenarios (Additive Outliers vs. Level Shifts).
Main Results:
- Initial trust levels in AI and human experts were comparable.
- Credibility of advice was more severely compromised by Level Shifts than Additive Outliers.
- Trust decline was more pronounced for human experts compared to AI following structural breaks.
Conclusions:
- Market structural breaks significantly impact trust in forecasting advice.
- Level shifts erode trust more than single anomalies, particularly for human experts.
- Findings offer insights into behavioral responses to AI and human systems in uncertain financial environments.
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