Algorithmic Transparency and Portfolio Choices: Field Evidence

Abstract

This paper studies whether profile-based explanations influence investors' acceptance of algorithmic risk recommendations in a randomized controlled trial embedded directly in the platform's interface of a leading French robo-advisor. Users were assigned either to see graphical explanations of the drivers underlying their recommended risk score and associated portfolio or to receive the standard interface with no explanation. Our results, obtained in a real-world setting with actual clients of a FinTech, do not support the adherence gains from increased transparency that are widely anticipated in the literature. Overall, providing profile-based explanations is not found to increase acceptance of the recommended profile nor raise users' engagement with the platform. However, we find a heterogeneous treatment effect as profilebased explanations lead to a greater downward deviation among desktop users who have already deviated to safer-than-recommended portfolios, but this pattern disappears once users' experience of the platform is taken into account. We observe non-causal evidence in both conditions that behavior is shaped primarily by the digital context and experience: phone and first-time users are more likely to accept the portfolio recommendation than desktop and returning users. While such transparency-enhancing profile-based explanations are informative, they are not a universal lever for adherence, suggesting that explanation design should be tested and tailored across device types and users' experience.

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