Regulating Algorithmic Accountability in Financial Advising: Rethinking the SEC’s AI Proposal

Authors

Chen Wang, Center for Research on Digital Economy and Legal Innovation

First Page

955

Document Type

Article

Abstract

As artificial intelligence increasingly reshapes financial advising, the SEC has proposed new rules requiring brokerdealers and investment advisers to eliminate or neutralize conflicts of interest arising from AI use. This Article critically assesses the proposal’s scope, rationale, and feasibility, contending that its sweeping definitions and prescriptive mandates risk overregulation and conflict with the SEC’s longstanding disclosure-based regulatory approach. Drawing on case law, fiduciary duties, Regulation Best Interest, and existing antifraud provisions, this Article argues that the current legal framework, grounded in disclosure and informed consent, remains sufficient to manage AI related conflicts. It cautions against imposing categorical conflict elimination requirements that may stifle innovation, limit market efficiency, and overlook existing mechanisms of market discipline and enforcement. Instead, the SEC should focus on enhancing disclosure standards and leveraging its antifraud authority to address emerging risks, such as AI washing. By advocating for a principles-based, disclosuredriven approach, this Article proposes a more balanced and effective path to algorithmic accountability in financial advising, aligning regulatory oversight with technological innovation and market realities.

Recommended Citation

Chen Wang, Regulating Algorithmic Accountability in Financial Advising: Rethinking the SEC’s AI Proposal, 73 Buff. L. Rev. 955 (2025).
Available at: https://digitalcommons.law.buffalo.edu/buffalolawreview/vol73/iss4/4