University of Miami Business Law Review
Document Type
Article
Abstract
While undeniably powerful, artificial intelligence algorithms also pose significant risks. The ethical, legal, social, and scientific implications of various AI models can be profound, as demonstrated by the subprime mortgage crisis. This crisis, worsened by the unregulated use of derivative algorithms, is a stark reminder of the pivotal role of individual accountability, ethical responsibility, and regulation in preventing future “catastrophic harm.”1 This Article ventures into the uncharted territory of AI ethical governance and aims to advance AI scholarship and address the unresolved issue of ethical compliance management in AI. Employing a risk-based assessment tool is critical for developing regulatory and strategic models to achieve ethical compliance. This Article introduces a novel AI Integrative Risk-Based (AIRB) model that provides a robust approach to conducting AI risk assessments. It sets the stage for future United States regulations that can ensure ethical compliance in domestic and global markets responsive to identified risks. Each step in the model is designed to integrate well-accepted and proven risk-based methodologies, leveraging the strengths of each method at every stage to formulate ethical guardrails to protect the public from AI misapplication, regardless of intentionality
Recommended Citation
Miriam Weismann,
The Ethical Governance of Artificial Intelligence: Using the AI Integrative Risk-Based (AIRB) Model to Achieve Compliance Risk Management,
33 U. MIA Bus. L. Rev.
213
(2025)
Available at:
https://repository.law.miami.edu/umblr/vol33/iss2/4
