Robo-advisors (RAs) support economic decisions for customers using artificial intelligence (AI). RAs are gaining increasing significance but lack market penetration. A significant issue is the perceived low transparency of such AI systems. This study examines the public’s demands on RAs with text-mining methods from the perspective of explainable artificial intelligence (XAI).
The results reveal understandability and trustworthiness issues for each of the RA use phases (configuration, matching, and maintenance). In particular, five barriers emerge in RA if information needs remain unanswered: entry barrier, assessment barrier, evaluation barrier, continuation barrier and withdrawal barrier. The barriers can be mitigated by combining explanation, design and communication measures. The results are discussed regarding theoretical implications and practical recommendations for facilitating the adoption of RAs.
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