Aram A. Gavoor (George Washington University Law School) and Raffi Teperdjian (George Washington University Law School) have posted “A Structural Solution to Mitigating Artificial Intelligence Bias in Administrative Agencies” (89 George Washington Law Review Arguendo (2021 Forthcoming)) on SSRN. Here is the abstract:
The rise of artificial intelligence (AI) from nascent theoretical science to an advancing juggernaut of industry with national security implications has begun to permeate U.S. federal administrative agencies. For all the potential benefits AI brings, misapplied or underregulated administrative agency utilization of AI risks eroding American values. The Executive Branch must carefully calibrate its administrative uses of AI to mitigate for biases that flow from models ranging from simple algorithms to complex machine learning systems, especially for biases that would adversely affect protected classes and vulnerable groups. Save for a voluntary survey by an independent advisory agency, the federal government lacks an organic accounting of AI use cases and development across administrative agencies. Recent executive actions have only begun to address these issues by establishing broad-stroke foundational principles and recommendations that can lead to the development of optimal AI regulation and general utilization. Despite these initial gains, the prospective utilization of AI in administrative adjudications, rulemakings, grant administration and the like, lacks the structural framework to apply meaningful implementing and accountability mechanisms. The Biden administration will have the opportunity and challenge to expand on the foundation of the prior two administrations and normalize the process of administrative integration of AI with the quality control, consistency measures, and policymaking processes that best leverage federal government resources. This is especially important in light of the related national security implications that flow from this issue. Regardless of whether the Biden administration seeks to undergird executive discretion with legislation or operate on a self-restraint basis, the appropriate regulation of AI in administrative agencies should balance technological innovation with legal compliance and fidelity to well-tread limiting principles. We conclude that two units of the Executive Office of the President, the Office of Information and Regulatory Affairs and the Office of Science and Technology Policy, are optimally situated and experienced to lead the policy-making, adoption, and utilization of AI systems in administrative agencies.