Author: CARE Staff

The following two-part discussion highlights two important developments in recent efforts to address the uses of Artificial Intelligence in creating fictitious and damaging content.  The topics are, respectively, the creation and detection of false evidence in adjudicative and deliberative processes; and (2) the proliferation of injurious content on the Internet, assisted through the use of Artificial Intelligence.  The issues are grouped together because they share some common technological concerns – and they are noted together in the recent White House document, Winning the Race: AMERICA’S AI ACTION PLAN.  The style of treatment, as with other parts of this series, includes some historical context, meditation and (hopefully) constructive wandering, treatment of related issues, and focus on the current and potential future state of pertinent law and processes.  In many ways, the style departs from the usual forms of legal discussion.  However, this series of discussions on Artificial Intelligence was conceived as an alternative to the proliferation of articles in more traditional formats.                       

The core conclusions with regard to the topics treated in the following discussions are: (1) we need a readily available, competitively-checked deepfake and digital fraud detection resource for use in conjunction with evidentiary rules in their current or amended form; (2) the TAKE IT DOWN Act’s provisions, including takedown procedures for injurious visual content, should be expanded to expressly include audio content; and (3) the model provided by the TAKE IT DOWN Act’s expedited, bi-partisan consideration should now be focused on the roles of Artificial Intelligence in creating potentially dangerous Large Language Models services or products, such as chatbots.  These points are placed in context in the relevant sections of the discussions, but other less prominent issues – such as the appropriateness of criminal sanctions provided by the TAKE IT DOWN Act and the deadline to institute notice and takedown procedures – are also considered.

Gary Rinkerman is a Founding Partner at the law firm of Pierson Ferdinand, LLP, an Honorary Professor of Intellectual Property Law at Queen Mary University School of Law in London, a member of George Mason University’s Center For Assurance Research and Engineering, and a Senior Fellow at George Mason University’s Center for Excellence in Government Cybersecurity Risk Management and Resilience. The views and information provided in this article are solely the work of the author and do not comprise legal advice.  They are not for attribution to any entity represented by the author or with which he is affiliated or is a member – including, e.g., the firm in which he is a member or any of its clients.  All Internet citations and links in this article were visited and validated on September 4, 2025.

On July 23, 2025, the Trump Administration published Winning the Race: AMERICA’S AI ACTION PLAN (“AI Action Plan”).[1]  Among the goals of the AI Action Plan is the elimination of inappropriate bias[2] and false information in the government’s AI systems.  The corruptions caused by inappropriate biasing, which may be introduced at any number of stages in what has been called “the AI pipeline,” lead to systems and outputs that are unreliable and, in some instances, injurious.[3]  A core concern regarding the outputs of such flawed systems is that the outputs can contain AI hallucinations – a phenomenon in which the system “in a large language model (LLM), often a generative AI chatbot or computer vision tool, perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate.”[4] The roots of this concern are diverse and run deep, but a conspicuous and widely publicized example of hallucinating AI is provided by Google’s attempt to avoid bias and promote diversity in the outputs of its  generative AI Gemini model.  Google’s tampering with historical truths was ostensibly well-intentioned and harmless, but it pointed out some underlying AI-related concerns that have more serious and potentially injurious consequences.  These consequences arise when a flawed AI system’s output is being relied on to make decisions that affect people’s lives. The following text briefly recaps the Google Gemini matter and then discusses aspects of the AI Action Plan that are intended to identify and eliminate inappropriate bias and falsity in government AI systems.

*This article is the first in a series of discussions of Winning the Race: AMERICA’S AI ACTION PLAN, issued by the Trump Administration on July 23, 2025.

Attempts to exercise control over corporate artificial intelligence (“AI”) adoption, development, implementation, and management have surfaced in the form of Executive Orders, legislation, rulemaking, union and guild agreements, and case law. However, shareholders in publicly traded corporations have also used the shareholder Proposal and Proxy voting process to introduce sweeping or focused efforts to force corporate management to address and disclose information on a variety of AI-related issues. These Proposals are often couched in language that references ethical and significant social policy issues that arise in course of the target company’s AI-related business plans and activities. Of course, the determination of what is “ethical” or what constitutes a “significant social policy issue” can be quite subjective or driven by the advocate’s ancillary political and economic goals. This article discusses three recent attempts to use the shareholder Proposal and Proxy voting process to impose AI-related “transparency” and control requirements on major users of the technology. The common thread that runs through each effort is the strategic use of shareholder initiatives to attempt to influence the policies and management approach to AI and AI-related issues, including, in some instances, specific security, privacy, copyright, and personality rights issues.

Gary Rinkerman is a Founding Partner at the law firm of Pierson Ferdinand, LLP, an Honorary Professor of Intellectual Property Law at Queen Mary University School of Law in London, a member of George Mason University’s Center For Assurance Research and Engineering, and a Senior Fellow at George Mason University’s Center for Excellence in Government Cybersecurity Risk Management and Resilience. The views and information provided in this article express solely the work of author and do not comprise legal advice and are not for attribution to any entity represented by the author or with which he is affiliated or a member.