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Value Dialectics, Regulatory Orientation and Implementation Path of Algorithm Transparency in Robo-advisory |
LIU Bo-han |
Law School, Guizhou University, Guiyang, Guizhou, 550025 |
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Abstract Robo-advisory using AI technology to analyze big data can better implement the principle of investor suitability and generate more suitable investment allocation schemes for investors, but they also conceal negative effects such as financial institutions’ profit-grabbing, risk mismatch of investment solutions, and hollowing out of the legal obligations of investment advisors. Algorithm transparency helps to break the tyranny of algorithms, eliminate algorithm discrimination, and lay the foundation for algorithm accountability, while algorithm confidentiality is necessary to maintain national scientific and technological strength, and to maintain the competitiveness of scientific and technological enterprises in the market. In the face of the value conflict between algorithm transparency and algorithm confidentiality, complete algorithm transparency and absolute algorithm confidentiality are both biased, and different degrees and levels of algorithm transparency should be realized for different subjects in different ways according to the different purposes of regulating algorithms. The hierarchy of algorithmic transparency is a specific application of the principle of proportionality, the function of measuring interests, and the proportional analysis paradigm of “ends-means” in the governance of robo-advisory algorithms. In the realization path of algorithmic transparency of robo-advisory, “compliance review + algorithmic filing” is used to realize algorithmic model transparency in order to prevent and control systemic financial risks, “algorithmic informing + algorithmic interpretation” is used to realize algorithmic logic transparency in order to protect the investors right to know. Afterward, it evaluates the degree of influence of algorithms on investors’ trust interests, and clarifies the subjectivity, causality and relevance of algorithmic decision-making, so as to determine and allocate algorithmic responsibilities.
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Received: 07 October 2023
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