The November 8, 2024 meeting of the Evidence Committee made it clear that the august members of the committee do not believe our warnings. They will do little or nothing to protect our system of justice from the oncoming storm of deepfake justice. They think it is a fake problem and Judge Paul Grimm (ret) and Professor Maura Grossman are wrong. This is not unexpected. Losey, The Problem of Deepfakes and AI-Generated Evidence: Is it time to revise the rules of evidence? Part One and Part Two. Here is a deepfake video of me talking about the committee and deepfake videos.
True Deep Fake videos claim to be true and are much better than this.
Check out the EDRM CLE on DeepFakes on December 5, 2024 for more information. Ralph (the real one) appears on a panel with Judge Ralph Artigliere (ret.) and Professor Maura Grossman. Bottom line: we must all be very diligent and learn as much as we can about fake videos and what to do when you are hit with one. Also, what to do if your client presents you with a video too good to be true or otherwise suspect. We are now living in a world of “liar’s dividend” and it is hitting our courts now.
Ralph Losey Copyright 2024. — All Rights Reserved.
The future of Artificial Intelligence isn’t just on the horizon—it’s already transforming industries and reshaping how businesses operate. But with this rapid evolution comes new challenges. Ethical concerns, privacy risks, and potential regulatory pitfalls are just a few of the issues that organizations must navigate. That’s where the Organisation for Economic Co-operation and Development (OECD) comes in. To help groups embrace AI responsibly, the OECD has developed a set of guiding principles designed to ensure AI is implemented ethically and effectively. Are you prepared to harness the power of AI while safeguarding your company against the risks? Discover how the OECD’s blueprint can help guide you through this complex landscape.
Blueprint for ethical AI providing some legal protection of Boards of Directors. Image by Ralph Losey using Visual Muse.
Introduction
The Organisation for Economic Co-operation and Development (OECD) plays a vital role in shaping policies across the world to foster prosperity, equality, and sustainable development. In recent years, the OECD has shifted its focus toward the responsible development of AI, recognizing its potential to transform industries and economies. For businesses any other organizations considering the adoption of AI into their workflows the OECD’s AI Principles (as slightly amended 2/5/24) provide a good starting point to develop internal policies. They can help guide your board to make decisions that ensure AI technology is deployed ethically and responsibly. This can help protect them from liability, and their employees, customers, and the world from harm.
What is the OECD?
The Organisation for Economic Co-operation and Development (OECD) is an independent, international organization dedicated to shaping global economic policies that are based on individual freedoms and democratic values. The U.S. was one of the twenty founding members in 1960 when the Articles of the Convention were signed, establishing the OECD. It now has 38 member countries, mainly advanced economies. Though the OECD initially focused on economic growth, international trade, and education, it has become increasingly concerned with the ethical and responsible development of artificial intelligence.
In 2019, the OECD introduced its AI Principles–the first intergovernmental standard for AI use. These principles reflect a growing recognition that AI will play an important role in global economies, societies, and governance structures. The OECD’s mission is clear: AI technologies must not only drive innovation but also be applied in ways that respect human rights, democracy, and ethical principles. These AI guidelines are vital in a world where AI could be both a powerful tool for good and a source of significant risks if misused. The Five AI Principles and Recommendations were slightly amended on February 5, 2024.
The OECD is a highly respected group that collaborates with many international organizations, such as the United Nations (UN), World Bank, International Monetary Fund (IMF), and World Trade Organization (WTO). The OECD helps these groups align and coordinate efforts in global governance and policymaking. The OECD also engages in regional initiatives, providing tailored advice and support to specific regions such as Latin America, Southeast Asia, and Africa. Bottom line, the OECD has long played a crucial role in shaping global policy, promoting international cooperation, and providing data-driven, evidence-based recommendations to governments around the world.
World in AI hands (6 fingers) in Surrealistic style by Ralph Losey using Viausl Muse
Five Key OECD AI Principles
Before starting an AI program, businesses should consider the potential risks that AI poses to their operations, employees, and customers. By taking proactive steps to mitigate these risks, organizations can safeguard themselves from unforeseen consequences while reaping the benefits of AI. The OECD’s AI Principles(amended 2/5/24) represent one of many frameworks businesses should evaluate when integrating AI technologies into their operations. It is well respected around the world and should be a part of any organization’s due diligence.
These principles are built around five core guidelines:
Principle 1. Inclusive Growth, Sustainable Development, and Well-being
The first OECD AI principle stresses that AI should promote inclusive growth, sustainable development, and well-being for individuals and society. AI should benefit people and the planet.This core value reflects the potential of AI to contribute to human flourishing through better healthcare, education, and environmental sustainability.
AI should contribute to environmental sustainability. Image by Ralph Losey using Visual Muse.
Companies should be aware of the many challenges ahead. While AI-driven solutions, such as climate modeling or precision agriculture, can help tackle environmental crises, there is concern that rapid technological advancements may lead to widening inequality. For instance, the automation of jobs could disproportionately affect lower-income workers, potentially exacerbating inequality. Thus, this principle necessitates a strategy that ensures AI’s benefits are distributed equitably.
For businesses considering AI, three key actions should always be top-of-mind for board members:
Engage Relevant Stakeholders: Before implementing AI, include a diverse group of stakeholders in the decision-making. This should involve executives, legal and data privacy experts, subject matter experts, human resources, and marketing/customer support teams. Each group brings unique perspectives that can help ensure the AI program is equitable and aligned with the company’s values.
Evaluate Positive and Negative Outcomes: Consider both the potential benefits and risks to AI users and individuals whose data may be processed. AI should enhance productivity, but it must also respect the well-being of all involved parties.
Consider Environmental Impact: AI systems require substantial computational resources, which contribute to a large carbon footprint. Sustainable AI practices should be considered to reduce energy consumption and minimize environmental impact.
Consider environmental impact of AI, including power consumption. Image by Ralph Losey using Visual Muse.
Principle 2. Respect for the rule of law, human rights and democratic values, including fairness and privacy.
The wording of the second principle was revised somewhat in 2024. The full explanation for revised Principle Two is set out in the amendment recommendation of February 5, 2024.
a) AI actors should respect the rule of law, human rights, democratic and human-centred values throughout the AI system lifecycle. These include non-discrimination and equality, freedom, dignity, autonomy of individuals, privacy and data protection, diversity, fairness, social justice, and internationally recognised labour rights. This also includes addressing misinformation and disinformation amplified by AI, while respecting freedom of expression and other rights and freedoms protected by applicable international law.
b) To this end, AI actors should implement mechanisms and safeguards, such as capacity for human agency and oversight, including to address risks arising from uses outside of intended purpose, intentional misuse, or unintentional misuse in a manner appropriate to the context and consistent with the state of the art.
Respecting human rights means ensuring that Generative AI systems do not reinforce biases or violate individuals’ rights. For example, there is growing concern over the use of AI in facial recognition technology, where misidentification disproportionately affects marginalized groups. AI must be designed to avoid such outcomes by integrating fairness into algorithms and maintaining democratic values like transparency and fairness.
Image of AI helping to protect fairness and justice by Ralph Losey using Visual Muse.
Businesses integrating AI into their operations should address several legal issues, including intellectual property, data protection, and human rights laws. To do this there are four things a board of directors should consider:
Ensure Compliance with Laws: Verify that Generative AI (GAI) adheres to copyright laws and data protection regulations such as GDPR or CCPA. Implement safeguards to ensure the system does not infringe upon users’ privacy or autonomy.
Prevent Discrimination: Conduct thorough audits to ensure that GAI outputs are fair and free from discrimination. Discriminatory outcomes can damage reputations and result in legal challenges.
Monitor for Misinformation: GAI systems must be designed to resist distortion by misinformation or disinformation. Mechanisms should be in place to quickly halt GAI operations if harmful behaviors are detected.
Develop Policies and Oversight: Establish clear policies and procedures that govern the use of GAI within your business. This includes implementing human oversight to ensure AI actions align with ethical and legal standards.
Professional human supervision of AI generation is imperative. Image by Ralph Losey using Visual Muse.
Principle 3. Transparency and Explainability
Transparency and explainability are fundamental to user trust in AI systems. This principle calls for AI systems to be transparent so that users can understand how decisions are made. With complex AI algorithms, it is often difficult to decipher how certain outcomes are generated—a problem referred to as the “black box” issue in AI.
While transparency enables users to scrutinize AI decisions, the challenge lies in making these highly technical systems comprehensible to non-experts. This requires a good education program by experts. Moreover, explainability must strike a balance between safeguarding intellectual property and providing adequate insight into AI operations, especially when used in public sector decision-making.
Businesses and other organizations must ensure that employees and other users of its computer systems understand when and how AI is used, along with some understanding of how AI decisions are made, and what mistakes to look out for. See e.g.Navigating the AI Frontier: Balancing Breakthroughs and Blind Spots (e-Discovery Team, October 2024). For businesses, ensuring transparency involves two critical steps:
Inform Users: Be transparent with employees, consumers, and stakeholders that GAI is being used. Where required by law, obtain explicit consent from users before collecting or processing their data.
Explain AI Processes: Provide clear, easy-to-understand explanations of how AI systems function. This includes offering insight into the sources of data used for training the AI and explaining the logic behind AI outputs, such as content recommendations or predictions. It is also important to explain the errors to look out for and other idiosyncrasies of the system to look out for. Everyone should be taught the “trust but verify” process and remember that they are ultimately responsible for their actions, not the AI. See e.g. Panel of AI Experts for Lawyers: Custom GPT Software Is Now Available (6/21/24);Can AI Really Save the Future? A Lawyer’s Take on Sam Altman’s Optimistic Vision (10/04/24).
Image by Ralph Losey using Visual Muse
Principle 4. Robustness, Security, and Safety
This principle demands that AI systems be resilient, secure, and reliable. As AI systems are increasingly integrated into sectors like healthcare, transportation, and critical infrastructure, their reliability is essential. A malfunctioning AI in these areas could result in dire consequences, from life-threatening medical errors to catastrophic failures in critical systems.
Cybersecurity is a significant concern, as more advanced AI systems become attractive targets for hackers. The OECD recognizes the importance of safeguarding AI systems and other systems from security breaches. All organizations today must guard against malicious attacks to protect their data and public safety. Organizations using AI must adopt a comprehensive set of IT security policies. Two key actions points that the Board should start with are:
Plan for Contingencies: Implement a Cybersecurity Incident Response Plan that outlines steps to take if the AI or other technology system malfunctions or behaves in an undesirable manner. This plan should detail how to quickly halt operations, troubleshoot issues, and safely decommission the system if necessary. You should probably have legal specialists on call in case your systems are hacked.
Ensure Security and Safety: Businesses should continuously monitor their technology and AI systems to ensure they operate securely and safely under various conditions. Regular audits, including red team testing, can help detect vulnerabilities before they become significant problems.
Safety and Security of data should be prime directive. Futuristic style image by Ralph Losey.
Principle 5. Accountability
Accountability in AI development and use is paramount. This principle asserts that those involved in creating, deploying, and managing AI systems must be held accountable for their impacts. Human oversight is critical to safeguard against mistakes, biases, or unintended consequences. This is another application of “trust but verify” on a management level. This is particularly relevant in scenarios where AI systems are set up to help make decisions affecting people’s lives, such as loan approvals, hiring decisions, or judicial sentencing. These should never be autonomous, but recommendation with a human in charge. This is especially true for physical security systems.
A clear accountability framework is critical. The accountability principle ensures that even in highly automated systems, human oversight is necessary to safeguard against mistakes, biases, or unintended consequences. The Board of Directors should, as a starting point:
Designate Responsible Parties: Assign specific individuals or departments to oversee the AI system’s operations. These stakeholders must maintain comprehensive documentation, including data sets used for training, decisions made throughout the AI lifecycle, and records of how the system performs over time.
Conduct Risk Assessments: Periodically evaluate the risks associated with AI, particularly in relation to the system’s outputs and decision-making processes. Regular assessments help ensure the system continues to function as intended and complies with ethical standards.
Image of key legal issue by Ralph Losey using Visual Muse.
Strengths and Weaknesses of the OECD AI Principles
The OECD AI principles are ambitious and reflect a comprehensive effort to create a global framework for responsible AI. However, while these guidelines are strong, they are not without their weaknesses.
Strengths
Comprehensive Ethical Guidelines: The principles cover a broad spectrum of ethical concerns, making them a strong foundation for policy guidance.
Global Influence: As an international standard, the OECD AI Principles provide a respected baseline for countries worldwide, not just the U.S. This allows for a coordinated approach to AI governance.
Commitment to Human Rights: By centering AI development on human dignity and rights, the OECD ensures that ethical concerns remain at the forefront of AI advancements.
Weaknesses
Lack of Enforcement: One of the significant drawbacks is the absence of enforcement mechanisms. The principles serve as guidelines, but without penalties for non-compliance, their effectiveness could be limited. A Board should add appropriate procedures that track their existing policies.
Ambiguity in Accountability: While the principle of accountability is emphasized, the specifics of assigning responsibility in complex AI systems remain unclear.
Rely on established policies like OECD. Image by Ralph Losey using Visual Muse.
Conclusion
Implementation of the OECD’s Five AI Principles is an essential step toward the responsible development of AI technologies. While the principles address key concerns such as human rights, transparency, and accountability, they also highlight the need for ongoing international collaboration and governance. In many countries outside of the U.S. there are, for instance. much stronger laws and regulations governing user privacy. Following the OECD Principles can help with regulatory compliance and show an organizations good faith to attempt to follow complex regulatory systems.
Regulatory compliance and privacy. Image by Ralph Losey using Visual Muse.
By relying on multiple AI frameworks, not just the OECD’s, businesses and their Boards can ensure a comprehensive approach to AI implementation. In the rapidly evolving field of AI, where state and foreign laws change rapidly, it is prudent for any CEO or Board of Directors to base it policies on stable, well-respected, principles. That can help establish good faith efforts to handle AI responsibly. Consultation with knowledgeable outside legal counsel is, of course, an important part of all corporate governance, including AI implementation.
Documenting Board decisions and tying them back to internationally accepted standards on AI is a good practice for any organization, local or global. It may not protect all of a company’s decisions from outside attack based on unfair 20/20 hindsight, but it should provide a solid foundation for good faith based defenses. This is especially true if these principles are adopted proactively and implemented with advice from respected third-party advisors. We are facing rapidly changing times, with both great opportunities and dangers. We all need to make our best efforts to act in a responsible manner and the OECD principles can help us to do that.
Click here to listen to an AI generated Podcast discussing the material in this article.
This is the conclusion to a two part article. Please read Part One first.
Professor Capra explains the proposals of Judge Grimm and Professor Grossman to modify Rule 901(b) to authenticate AI generated evidence by using Maura’s broken clock analogy:
The proposed revision substitutes the words “valid” and “reliable” for “accurate” in existing rule 901(b)(9), because evidence can be “accurate” in some instances but inaccurate in others (such as a broken watch, which “accurately” tells the time twice a day but is not a reliable means of checking the time otherwise).
‘Broken clocks right twice a day’ image in style of Salvador Dali by Ralph Losey
Maura Grossman provided further explanation in her presentation to the Committee on why they recommended replacing the term accurate with reliable and valid.
PROF. GROSSMAN. I want to talk about language because I’m a real stickler about words, and I’ll talk to you about the way science has viewed AI. There are two different concepts. One is validity. We don’t use the word “accuracy.” And the other is reliability. Validity is: does the process measure or predict what it’s. supposed to measure? So, I can have a perfectly good scale, but if I’m trying to measure height, then a scale is not a valid measure for height. Reliability has to do with “does it measure the same thing under substantially similar circumstances?” And it’s really important that we measure validity and reliability and not “accuracy” because a broken watch is accurate twice a day, right? But it’s not reliable.
So, for those of you who are more visual, when you’re valid and you’re reliable, you’re shooting at the target, and you are consistent. When you’re invalid and unreliable, you’re not shooting at the center, and you’re all over the place. When you’re invalid and reliable, you’re shooting at the wrong place, but you’re very consistent in shooting at the wrong place. And when you’re valid and unreliable, you are shooting at the center, but you’re all over the place.
We need evidence that is a product of a process that is both valid and reliable. Right now, the rules use the word “accuracy” or “accurate” in some places (such as in Rule 901(b)(9)) and “reliable” in other places (such as in Rule 702),189 and I think it’s confusing to practitioners because it doesn’t comport with what scientists mean by these words or how they’re used if you look them up in the dictionary.
As to the second proposal of Grimm and Grossman to add a new Rule 901(c) to address “Deepfakes,” Professor Capra did not like that one either. He rejected the proposal with the following argument.
It would seem that resolving the argument about the necessity of the rule should probably be delayed until courts actually start dealing on a regular basis with deepfakes. Only then can it be determined how necessary a rule amendment really is. Moreover, the possible prevalence of deepfakes might be countered in court by the use of watermarks and hash fingerprints that will assure authenticity (as discussed below). Again, the effectiveness of these countermeasures will only be determined after a waiting period.
The balancing test in the proposal–applied when the burden-shifting trigger is met–is that the “probative value” must outweigh the prejudicial effect. It can be argued that importing this standard confuses authenticity with probative value. . . . Put another way, the probative value of the evidence can only logically be assessed after it is determined to be authentic. Having authenticity depend on probative value is a pretty complicated endeavor. Moreover, presumably the prejudice referred to is that the item might be a deepfake. But if the proponent can establish that it is authentic, then there would be no prejudice to weigh. . . . At any rate, more discussion in the Committee is necessary to figure out whether, if there is going to be an amendment, what requirement must be placed on the proponent once the opponent shows enough to justify a deepfake inquiry.
Digital Art image by Ralph Losey using Visual Muse
From the record it appears that Grimm and Grossman were not given an opportunity to respond to these criticisms. So once again the Committee followed Professor Capra’s lead and all of the rule changes they proposed were rejected. Again, with respect, I think Dan Capra missed the point again. Authentic evidence can already be withheld as too prejudicial under current Federal Evidence Rule 403 (Excluding Relevant Evidence for Prejudice, Confusion, Waste of Time, or Other Reasons). But the process and interpretation of existing rules is what is too complex. That is a core reason for the Grimm and Grossman proposals.
Moreover, in the world of deepfakes things are not as black and white as Capra’s analysis assumes. Often authenticity of audio visuals is a gray area question, a continuum, and not a simple yes or no. It appears that the Committee’s decisions would benefit from the input of additional technology advisors, independent ones, on the rapidly advancing field of AI image generation.
The balancing procedure Grimm and Grossman suggested is appropriate. If it is a close question on authenticity, and the prejudice is small, then it makes sense to let it in. If authenticity is a close question, and the prejudice is great, say even outcome determinative, then exclude it. And of course, if the proof of authenticity is strong, and the probative value strong, even outcome determinative, then the evidence should be allowed. The other side of the coin, is that if the evidence is strong that the video is a fake, it should be excluded, even if that decision is outcome determinative.
Judge weighing the evidence in Art Deco style by Ralph Losey
Capra’s Questionable Evaluation of the Danger of Deepfakes
In his memorandum Professor Capra’s introduced the proposed Rule Changes with the following statement.
The consequence of not formally adopting the proposals below at this meeting is that any AI-related rule amendment will have to wait a year. One could argue that the Committee needs to act now, to get out ahead of what could be a sea change in the presentation of evidence. Yet there seems to be much merit in a cautious approach. To say that the area is fast-developing would be an understatement. The EU just recently scrapped its one-year-old regulations on AI, recognizing that many of the standards that were set had become outmoded. The case law on AI is just beginning. It surely makes sense to monitor the case law for (at least) a year to see how the courts handle AI-related evidence under the existing, flexible, Federal Rules.
Naturally the Committee went with what they were told was the cautious approach. But is doing nothing really a cautious approach? In times of crisis inaction is usually reckless, not cautious. Professor Capra’s views are appropriate for normal times, where you can wait a few years to see how new developments play out. But these are not normal times. Far from it.
We are seeing an acceleration of fraud, or fake everything, and a collapse of truth and honesty. Society has already been disrupted by rapid technical and social changes, and growing distrust of the judicial system. Fraud, propaganda and nihilistic relativism are rampant. What is the ground truth? How many people believe in an objective truth outside of the material sciences? How many do not even accept science? Is it not dangerous under these conditions to wait longer to try to curb the adverse impact of deepfakes?
Image of the serious questions raised by AI and Deepfakes. Image by Ralph Losey using Visual Muse
‘That Was Then, This Is Now’
There is little indication in Professor Capra’s reports that he appreciates the urgency of the times, nor the gravity of the problems created by deep fakes. The “Deepfake Defense” is more than a remote possibility. The lack of published opinions on deepfake evidence should not lull anyone into complacency. It is already being raised, especially in criminal cases.
Judge Dixon reports this defense was widely used in D.C. courts by individuals charged with storming the Capitol on January 6, 2021. The Committee needs more advisors like Judge Dixon. He wants new rules and his article The “Deepfake Defense” discusses three proposals: Grimm and Grossman’s, Delfino’s and LaMonaga’s. Here is Judge Dixon’s conclusion in his article:
As technology advances, deepfakes will improve and become more difficult to detect. Presently, the general population is not able to identify a deepfake created with current technology. AI technology has reached the stage where the technology needed to detect a deepfake must be more sophisticated than the technology that created the deepfake. So, in the absence of a uniform approach in the courtroom for the admission or exclusion of audio or video evidence where there are credible arguments on both sides that the evidence is fake or authentic, the default position, unfortunately, may be to let the jury decide.
Professor Capra addressed the new issues raised by electronic evidence decades ago by taking a go-slow approach and waiting to see if trial judges could use existing rules. That worked for him in the past, but that was then, this is now.
Courts in the past were able to adapt and used the old rules well enough. That does not mean that their evidentiary decisions might have been facilitated, and still might be, by some revisions related to digital versus paper. But Capra assumes that since the courts adapted to digital evidence when it became common decades ago, that his “wait and see” approach will work once again. He reminds the Committee of this in his memorandum:
In hindsight, it is fair to state that the Committee’s decision to forego amendments setting forth specific grounds for authenticating digital evidence was the prudent course. Courts have sensibly, and without extraordinary difficulty, applied the grounds of Rule 901 to determine the authenticity of digital evidence. . . .
The fact that the Committee decided not to promulgate special rules on digital communication is a relevant data point, but it is not necessarily dispositive of amending the rules to treat deepfakes.18
Professor Capra will only say that the past decision to do nothing is “not necessarily dispositive” on AI. That implies it is pretty close to dispositive. Memorandum to the Committee at pgs. 8-9, 20- (pgs. 21-22, 33- of 358). The Professor and Committee do not seem the appreciate two things:
The enormous changes in society and the courts that have taken place since the world switched from paper to digital. That happened in the nineties and early turn of the century. In 2024 we are living in a very different world.
The problem of deepfake audio-visuals is new. It is not equivalent to the problems courts have long faced with forged documents, electronic or paper. The change from paper to digital is not comparable to the change from natural to artificial intelligence. AI plays a completely different role in the cases now coming before the courts than has ever been seen before. Consider the words of Chief Justice John Roberts, Jr., in his 2023 Year-End Report:
Every year, I use the Year-End Report to speak to a major issue relevant to the whole federal court system. As 2023 draws to a close with breathless predictions about the future of Artificial Intelligence, some may wonder whether judges are about to become obsolete. I am sure we are not—but equally confident that technological changes will continue to transform our work. . . .
I predict that human judges will be around for a while. But with equal confidence I predict that judicial work—particularly at the trial level—will be significantly affected by AI. Those changes will involve not only how judges go about doing their job, but also how they understand the role that AI plays in the cases that come before them.
Is it really prudent and cautious for the Evidence Rules Committee to take the same approach with AI deepfakes as they did many years ago with digital evidence? AI now plays a completely new role in the evidence of the cases that now come before them. The emotional and prejudicial impact of deepfake audio-visuals is an entirely new and different problem. Plus, the times and circumstances in society have dramatically changed. The assumptions made by Committee Reporter Capra of the equivalence of the technology changes is a fundamental error. With respect, the Committee should reconsider and reverse its decision.
The assumption that the wait and see approach will work again with AI and deepfakes is another serious mistake. It is based on wishful thinking not supported by the evidence that the cure for deepfakes is just around the corner, that new software will soon be able to detect them. It is also based on wishful thinking that trial judges will again be able to muddle through just fine. Judge Grimm who just recently retired as a very active District Court trial judge disagrees. Judge Dixon who is still serving as a reserve senior trial judge in Washington D.C. disagrees. So do many others. The current rules are a muddled mess that needs to be cleaned up now. With respect, the Committee should reconsider and reverse its decision.
Social Conditions and Questions Compelling Action
Everyone today carries a video camera/phone and has access to free software on the internet to make fakes. Maura’s demonstration to the Committee showed that. That is why many think the time is now for new rules on AI, not tomorrow.
What are the consequences of continued inaction? What if courts are unable to twist existing rules to screen out fake evidence as Professor Capra hopes? What will happen to our system of justice if use of fake media becomes a common litigation tactic? How will the Liar’s Dividend pay out? What happens when susceptible, untrained juries are required to view deep fakes and then asked to do the impossible and disregard them?
If we cannot reliably determine what is fake and what is true in a court of law, what happens then? Are we not then wide open and without judicial recourse to criminal and enemy state manipulation? Can law enforcement and the courts help stop deepfake lies and propaganda? Can we even have free and fair elections? How can courts function effectively without reliable rules and methods to expose deepfakes? Should we make some rule changes right away to protect the system from collapse? Or should we wait until it all starts to fall apart?
Image in Conceptual Art style by Ralph Losey using Visual Muse
Professor Capra’s Conclusion in his Report to the Committee
Professor Capra ends his report with a one paragraph conclusion here quoted in full.
It is for the Committee to decide whether it is necessary to develop a change to the Evidence Rules in order to deal with deepfakes. If some rule is to be proposed, it probably should not be a specific rule setting forth the methods in which visual evidence can be authenticated — as those methods are already in Rule 901, and the overlap would be problematic. Possibly more productive solutions include heightening the standard of proof, or requiring an additional showing of authenticity — but only after some showing by the opponent has been made. But any possible change must be evaluated with the perspective that the authenticity rules are flexible, and have been flexibly and sensibly applied by the courts to treat other forms of technological fakery.
I expect the Rules Committee will follow Capra’s advice and do nothing. But 2024 is not over yet and so there is still hope.
What Comes Next?
The next Advisory Committee on Evidence Rules is scheduled for November 8, 2024 in New York, NY and will be open to the public both in-person and online. While observers are welcome, they may only observe, not participate.
In addition, we have just learned that Paul Grimm and Maura Grossman have submitted a revised proposal to the Committee, which will be discussed first. This was presumably done at the request of Professor Daniel Capra after some sort of discussion, but that is just speculation.
Grimm and Grossman’s Revised Proposal to Amend the Rules
The revised proposal, which includes the extensive rationale provided by Grimm and Grossman, can be found online here in PDF format.
REVISED Proposed Modification of Current Fed. R. Evid. 901(b)(9) for AI Evidence and Proposed New Fed. R. Evid. 901(c) for Alleged “Deepfake” Evidence. Submitted by Paul W. Grimm and Maura R. Grossman
[901](b) Examples. The following are examples only—not a complete list—of evidence that satisfies the requirement [of Rule 901(a)]: (9) Evidence about a Process or System. For an item generated by a process or system: (A) evidence describing it and showing that it produces an accurate a valid and reliable result; and (B) if the proponent acknowledges that the item was generated by artificial intelligence, additional evidence that: (i) describes the training data and software or program that was used; and (ii) shows that they produced valid and reliable results in this instance.
Note the only change from the last proposal for 901(b)(9) is use of the word “acknowledges” instead of “concedes” in 9(B). I agree this is a good and necessary revision because litigators hate to “concede” anything, but often have to “acknowledge.”
The revised proposed language for a new Rule 901(c) to address deepfakes is now as follows:
901(c): Potentially Fabricated or Altered Electronic Evidence. If a party challenging the authenticity of computer-generated or other electronic evidence demonstrates to the court that a jury reasonably could find that the evidence has been altered or fabricated, in whole or in part, using artificial intelligence,1 the evidence is admissible only if the proponent demonstrates that its probative value outweighs its prejudicial effect on the party challenging the evidence.
The changes made here from the last proposal were minor, but again appear helpful to clarify the intent. Here is the proposal showing strike outs and additions (underlined).
901(c): Potentially Fabricated or Altered Electronic Evidence.If a party challenging the authenticity of computer-generated or other electronic evidence demonstrates to the court that it is more likely than not either fabricated, or altered that a jury reasonably could find that the evidence has been altered or fabricated, in whole or in part, using artificial intelligence,1 the evidence is admissible only if the proponent demonstrates that its probative value outweighs its prejudicial effect on the party challenging the evidence.
I understand why these revisions were made, perhaps requested, and I again think they are all good. So too is the Rationale provided by Judge Paul and Professor Grimm. See the full second proposal Rationale here, but what follows are the excerpts of the Rationale that I found most helpful, all pertaining to new Rule 901(c):
A separate, new rule is needed for such altered or fake evidence, because when it is offered, the parties will disagree about the fundamental nature of the evidence. The opposing party will challenge the authenticity of the evidence and claim that it is AI-generated material, in whole or in part, and therefore, fake, while the proponent will insist that it is not AI-generated, but instead that it is simply a photograph or video (for example, one taken using a “smart phone”), or an audio recording (such as one left on voice mail), or an audiovisual recording (such as one filmed using a digital camera). Because the parties fundamentally disagree about the very nature of the evidence, the proposed rule change for authenticating acknowledged AI-generated evidence will not work. A separate, new rule is required. . . .
The proposed new rule places the burden on the party challenging the authenticity of computer-generated or electronic evidence as AI-generated material to make a showing to the court that a jury reasonably could find (but is not required to find) that it is either altered or fabricated, in whole or in part. This approach recognizes that the facts underlying whether the evidence is authentic or fake may be challenged, in which case the judge’s role under Fed. R. Evid. 104(a) is limited to preliminarily evaluating the evidence supporting and challenging authenticity, and determining whether a reasonable jury could find by a preponderance of the evidence that the proffered evidence is authentic. If the answer is “yes” then, pursuant to Fed. R. Evid. 104(b), the judge ordinarily would be required to submit the evidence to the jury under the doctrine of relevance conditioned upon a finding of fact, i.e., Fed. R. Evid. 104(b).
Because deepfakes are getting harder and harder to detect, and because they often can be so graphic or have such a profound impact that the jury may be unable to ignore or disregard the impact even of generative AI shown to be fake once they have already seen it, a new rule is warranted that places more limits on what evidence the jury will be allowed to see. See generally Taurus Myhand, Once The Jury Sees It, The Jury Can’t Unsee It: The Challenge Trial Judges Face When Authenticating Video Evidence in The Age of Deepfakes, 29 Widener L. Rev. 171, 174-5, 2023 (“The dangerousness of deepfake videos lie in the incomparable impact these videos have on human perception. Videos are not merely illustrative of a witnesses’ testimony, but often serve as independent sources of substantive information for the trier of fact. Since people tend to believe what they see, ‘images and other forms of digital media are often accepted at face value.’ ‘Regardless of what a person says, the ability to visualize something is uniquely believable.’ Video evidence is more cognitively and emotionally arousing to the trier of fact, giving the impression that they are observing activity or events more directly.” (Internal citations omitted).
If the judge is required by Fed. R. Evid. 104(b) to let the jury decide if image, audio, video, or audiovisual evidence is genuine or fake when there is evidence supporting each outcome, the jury is then in danger of being exposed to evidence that they cannot “un-remember,” even if the jurors have been warned or believe it may be fake. This presents an issue of potential prejudice that ordinarily would be addressed under Fed. R. Evid. 403. But Rule 403 assumes that the evidence is “relevant” in the first instance, and only then can the judge weigh its probative value against the danger of unfair prejudice. But when the very question of relevance turns on resolving disputed evidence, the current rules of evidence create an evidentiary “Catch 22”—the judge must let the jury see the disputed evidence on authenticity for their resolution of the authenticity challenge (see Fed. R. Evid. 104(b)), but that exposes them to a source of evidence that may irrevocably alter their perception of the case even if they find it to be inauthentic.
The proposed new Fed. R. Evid. 901(c) solves this “Catch 22” problem. It requires the party challenging the evidence as altered or fake to demonstrate to the judge that a reasonable jury could find that the challenged evidence has been altered or is fake. The judge is not required to make the finding that it is, only that a reasonable jury could so find. This is similar to the approach that the Supreme Court approved regarding Fed. R. Evid. 404(b) evidence (i.e., other crimes, wrongs, or acts evidence) in Huddleston v. U.S., 108 S. Ct. 1496, 1502 (1988) and the Third Circuit approved regarding Fed. R. Evid. 415 evidence (i.e., similar acts in civil cases involving sexual assault or child molestation) in Johnson v. Elk Lake School District. 283 F. 3d 138, 143-44 (3d. Cir. 2002).
Under the proposed new rule, if the judge makes the preliminary finding that a jury reasonably could find that the evidence has been altered or is fake, they would be permitted to exclude the evidence (without sending it to the jury), but only if the proponent of the evidence cannot show that its probative value exceeds its prejudicial impact. The proponent could make such a showing by offering additional facts that corroborate the information contained in the challenged image, video, audio, or audiovisual material. This is a fairer balancing test than Fed. R. Evid. 403, which leans strongly towards admissibility. Further, the proposed new balancing test already is recognized as appropriate in other circumstances. See, e.g., Fed. R. Evid 609(a)(1)(B) (requiring the court to permit a criminal defendant who testifies to be impeached with a prior felony conviction only if “the probative value of the evidence outweighs its prejudicial effect to that defendant.”)
With respect, the Committee should approve this revised rule proposal and seek its approval and adoption by the U.S. Supreme Court as soon as possible. The rules should have retroactive implementation wherever feasible. They may be needed very soon as the new article Deepfakes in Court eloquently explains.
Protesters outside Supreme Court by Ralph Losey & Photoshop
Deepfake In Courts Article: Introduction and Perspective
Deepfakes in Court is an 52-page law review article authored by eight scholars: the Hon. Paul W. Grimm (ret.), Duke Law School, Duke University, Maura R. Grossman, David R. Cheriton School of Computer Science, University of Waterloo and Osgoode Hall Law School, York University, Abhishek Dalal, Pritzker School of Law, Northwestern University; Chongyang Gao, Northwestern University; Daniel W. Linna Jr., Pritzker School of Law & McCormick School of Engineering, Northwestern University; Chiara Pulice, Dept. of Computer Science & Buffett Institute for Global Affairs, Northwestern University; V.S. Subrahmanian, Dept. of Computer Science & Buffett Institute for Global Affairs, Northwestern University and the Hon. John Tunheim, United States District Court for the District of Minnesota.
Deepfakes in Court considers how existing rules could be used to address deepfake evidence in sensitive trials, such as those concerning national security, elections, or other matters of significant public concern. A hypothetical scenario involves a Presidential election in 2028 where the court’s decision could determine the outcome of the election. The burden on judges in a crises scenario like that would be lessened by the adoption of the revised Grimm and Grossman rule proposals. But if they are not, the article shows how a national security case would play out under the existing rules.
The timeliness of this article is obvious in view of the pending national elections in the U.S. See e.g. Edlin and Norden,Foreign Adversaries Are Targeting the 2024 Election (Brennan Center for Justice, 8/20/24). Courtney Rozen of Bloomberg Law reports:
The rise of AI has supercharged bipartisan concerns about the possibility of deepfakes — manipulated images, audio, and video of humans — to sway voters ahead of the November elections. AI tools make it easier and cheaper to create deepfakes.
Safeguarding the integrity of elections is essential to democracy, and it’s critical that we ensure AI is not deployed to undermine the public’s trust through disinformation – especially in today’s fraught political climate. These measures will help to combat the harmful use of deepfakes in political ads and other content, one of several areas in which the state is being proactive to foster transparent and trustworthy AI.
SEC. 3.Section 20012 is added to the Elections Code, to read:20012. (a)The Legislature finds and declares as follows:
(1) California is entering its first-ever artificial intelligence (AI) election, in which disinformation powered by generative AI will pollute our information ecosystems like never before. Voters will not know what images, audio, or video they can trust.
(2) In a few clicks, using current technology, bad actors now have the power to create a false image of a candidate accepting a bribe, or a fake video of an elections official caught on tape saying that voting machines are not secure, or generate an artificial robocall in the Governors voice telling millions of Californians their voting site has changed.
Fake images could also be generated to try to support false information that a candidate promotes as true.
Horrible fake image of puppies outside an immigrant hut waiting to be cooked. Bu Ralph Losey using Visual Muse.
Description of the Deepfakes in Court Article
The Deepfakes in Court article by lead authors Grimm and Grossman begins by describing the growing concern over deepfakes—AI-generated media that can simulate real events, people, and speech with high accuracy. This could be incredibly troubling in high-stakes cases involving national security and elections. In cases like that false or manipulated evidence could have severe consequences. The article makes this point well.
The article continues by noting how easy it is now to create AI-generated content. While some platforms include restrictions and watermarks to prevent misuse, these protections are often inadequate. Deepfake generation is sophisticated enough that even experts struggle to distinguish real from fake, and watermarking or digital signatures can often be bypassed. This creates a “cat and mouse” game between deepfake creators and those attempting to detect and prevent their misuse.
Connie v. Eric: The All Too Possible Case That Everyone Should Fear
The core of the article is a hypothetical case involving the two Presidential candidates in the last ninety days before the election. One, named Connie, has filed suit against her opponent, Eric. Connie seeks an injunction and other relief against Eric and his campaign. She alleges Eric is behind the creation and circulation multiple deepfake videos and audios against her. The main ones show Connie having sex with a Chinese diplomat. In other videos she is shown soliciting bribes from Chinese officials. Still other videos show Connie’s supporters stuffing ballot boxes. All of the videos are very real looking and some are quite shocking. They are being circulated by thousands of bots across the internet.
Connie’s lawsuit seeks expedited adjudication and other injunctive relief within ninety days as to whether the videos are fake and whether Eric is behind them. Although some of the jurors assigned to the case have already seen at least some of the videos. Many have not. Can you imagine their reaction? Can they unsee that even if they later determine they are probably fake? Even if the judge tells them to disregard that evidence? What will the impact be?
Jurors will never unsee that scene, even if they decide it’s a Deepfake. Image by Ralph Losey in documentary photo style.
Since this is a hypothetical created by multiple professors the facts get even more complicated. Audios start to be circulated by Connie’s supporters where Eric is recorded saying “Wow! This technology is so good now it would be impossible for anyone to spot it as a fake.” There are more audios where he and his campaign make other damning admissions. All of the tapes sound exactly like Eric. He of course claims these audios are all fake and files counterclaims in the same lawsuit. Eric opposes a quick resolution of Connie’s lawsuit because he believes that overall, the videos help his campaign.
Of course, the circulation of these tapes and allegations lead to massive protests and further polarization of the country. The constant propaganda on both sides has triggered riots and violence between the two political parties and their supporters everywhere, but especially in the Capital. Discussion about actual issues is drowned out by the allegations of fraud by both sides. These are very dark times, with daily shootings. The election is only ninety days away.
Capital protest image in photorealistic style by Ralph Losey using Visual Muse
This is a scary hypothetical set of facts showing how deepfakes can easily be weaponized in an election. The facts in the article are actually much more complicated than I have described. See pages 16-21 of Deepfakes in Court. It reminds me of a law school final exam from hell but does its job well of showing the dazzlingly complex situation and the challenges faced under the Rules of Evidence. Plus, you get to read the perfect answers of how the existing rules would work under this all too possible scenario. This is all described in pages 18-47 of Deepfakes in Court. I urge you, no dare you, to read it. I am quite sure it was very challenging to write, even by the eight world authorities who prepared this.
What are the poor federal judges assigned to this case supposed to do? The article answers that question using the existing evidence rules. Let us hope real judges are not faced with this scenario, but if they are, then this article will provide a detailed roadmap as to how the case should proceed.
The GPTJudge Framework
The authors recommend a judge use what they call the “GPTJudge” framework when faced with deepfake issues, including expedited and active use of pre-trial conferences, focused discovery, and pre-trial evidentiary hearings. The framework includes expert testimony both before and during trial where experts would explain the underlying AI processes to the judge and help the court assess the reliability of the evidence. The idea is to show the possible application of existing rules to have a speedy trial on deepfake issues.
Photorealistic image using Visual Muse and Photoshop by Losey
The Deepfakes in Court article applies the existing rules and GPTJudge framework to the facts and emergency scenario outlined in the hypothetical. It explains the many decisions that a judge would likely face, but not the predicted rulings such as some law school exams might request. The article also does not predict the ultimate outcome of the case, whether an injunction would issue, and if it did, what it would say. That is really not necessary or appropriate because in real life the exact rulings would depend on the witness testimony and countless other facts that the judge would hear first before making a gatekeeper determination on showing the audio visuals to the jury. The devil is always in the details. The devil’s power in this case is compounded by the wording of the old rules.
Given the ease with which anyone can create a convincing deepfake, courts should expect to see a flood of cases in which the parties allege that evidence is not real, but AI generated. Election interference is one example of a national security scenario in which deepfakes have important consequences. There is unlikely to be a technical solution to the deepfake problem. Most experts agree that neither watermarks nor deepfake detectors will completely solve the problem, and human experts are unlikely to fare much better. Courts will have no option, at least for the time being, other than to use the existing Federal Rules of Evidence to address deepfakes. The best approach will be for judges to proactively address disputes regarding alleged deepfakes, including through scheduling conferences, permitted discovery, and hearings to develop the factual and legal issues to resolve these disputes well before trial.
Even as several scholars propose to amend the Federal Rules of Evidence in recognition of the threat posed by deepfake evidence, such changes are unlikely in the near future. Meanwhile, trial courts will require an interim solution as they grapple with AIM evidence. Rule 403 will play an important role, as the party against whom an alleged deepfake is proffered may be able to make a compelling argument that the alleged deepfake should be excluded because the probative value of the alleged deepfake is substantially outweighed by the potential for unfair prejudice because social science research shows that jurors may be swayed by audiovisual evidence even when they conclude that it is fake. This argument will be strongest when the alleged deepfake will lead the jury to decide the case based on emotion rather than on the merits.
Photorealistic image of jury watching a video by Ralph Losey using Visual Muse
Based on my long experience with people and courts I am inclined to agree with the article’s conclusion. Soon it may be obvious to the Rules Committee from multiple botched cases that all-too-human juries are ill equipped to make deepfake determinations. See e.g. footnotes 8-17 at pgs. 4-17 of Deepfakes in Court. Moreover, even the best of our judges may find it hopelessly complex and difficult to adjudicate deepfake cases under the existing rules.
Conclusion
Artificial intelligence and its misuse as deepfake propaganda is evolving quickly. Highly realistic fabricated media can already convincingly distort reality. This will likely get worse and keep us at risk of manipulation by criminals and foreign powers. This can even threaten our elections as shown by Deepfakes in Court.
There must be legal recourse to stop this kind of fraud and so protect our basic freedoms. People must have good cause to believe in our judicial system, to have confidence that courts are a kind of protected sanctuary where truth can still be found. If not, and if truth cannot be reliably determined, then people will lose whatever little faith they still have in the courts, despite the open corruption by some. This could lead to widespread disruption of society reacting to growing deepfake driven propaganda and the hate and persecution they bring about. If the courts cannot protect the people from the injustice of lying and fraud, what recourse will they have?
Protest in Washington in photo art style by Ralph Losey using Visual Muse
The upcoming Evidence Committee meeting is scheduled for November 8th, three days after election day on November 5th. What will our circumstances be? What will the mood of the country be? What will the mood and words be of the two candidates? Will the outcome even be known in three days after the election? Will the country be calm? Or will shock, anger and fear prevail? Will it even be possible for the Committee to meet in New York City on November 8th? And if they do, and approve new rules, will it be too little too late?
On April 19, 2024, the Advisory Committee on Evidence Rules for federal courts faced a critical question: Does AI-generated evidence, including deepfakes, demand new rules? The Committee’s surprising answer—’not yet.’ Was that the right call? Will they change their mind when they meet in November again right after the elections?
Image in Photorealistic style by Ralph Losey using Visual Muse
Part One analyzes the various rule change proposals. Chief among them is the proposal by Judge Paul Grimm (retired) and Professor Maura Grossman, who are well known to all legal tech readers. Several other interesting proposals were considered and discussed. You will hear the nerds inside view of the key driving facts at play here, the danger of deepfakes, the power of audio-video evidence, jury prejudice and the Liar’s Dividend. Part One also talks about why the Evidence Rules Committee chose not to act and why you should care.
Part Two will complete the story and look at what comes next with the meeting of November 8, 2024. It will also include a discussion of a second, slightly revised proposal by Paul Grimm and Maura Grossman that they just submitted and the latest article by Paul Grimm, Maura Grossman and six other experts: Deepfakes in Court: How Judges Can Proactively Manage Alleged AI-Generated Material in National Security Cases. They are all trying, once again, to push the Committee into action. Let us hope they succeed.
Summary of the Evidence Committee’s Decision and the Leadership of its Official Reporter
The Committee, under the strong leadership of its official Reporter for the last twenty-eight years, Daniel J. Capra, considered multiple proposals to amend the Rules of Evidence, but rejected them all. Professor Capra cited the need for further development. For now, courts must manage the significant new challenges of AI with existing rules.
They key segment of the Committee’s work is the 26-page memorandum found at Tab 1-A of the 358-page agenda book. It was written by Professor Daniel J. Capra, Fordham University School of Law and Adjunct Professor at Columbia Law. Dan Capra is a man almost my age, very powerful and respected in academic and judicial circles. He is a true legend in the fields of evidence, legal ethics and education, but he is no nerd. His comments and the transcript of his interaction with two of the top tech-nerds in law, Judge Paul Grimm (retired) and Professor Maura Grossman, make clear that Professor Capra lacks hands-on experience and deep understanding of generative AI.
That is a handicap to his leadership of the Committee on the AI issues. His knowledge is theoretical only, and just one of many, many topics that he reads about. He does not teach AI and the law, as both Grimm and Grossman do. This may explain why he wanted to just wait things out, again. He recommended, and the Committee agreed, apparently with no dissenters, to reject the concerns of almost all of the hands-on nerds, including all of the legal experts proposing rule changes. They all warn of the dangers of generative AI and deepfakes to interfere with our evidence based system of justice. It may even make it impossible to protect our upcoming election from deepfake interference. Daniel Capra gives some consideration to danger, but thinks the concerns are overblown and the Committee should continue to study and defer any action.
Don’t Look Up! image in Pop Art style by Ralph Losey using Visual Muse
Evaluation of the Pending Danger of Deepfakes
For authority that the dangers of deepfake are overblown, and so no rule changes are necessary, Professor Capra cites two articles. Professor Capra’s Memorandum to the Committee at pgs. 25-26 (pgs. 38-39 of 358). The first is unpersuasive, to say the least, a 2019 article in the Verge, Deepfake Propaganda is not a Real Problem, THE VERGE (Mar. 15, 2019). The article was written by Russell Brandom, who claims expertise on “the web, the culture, the law, the movies, and whatever else seems interesting.”
Given the current pace of technological advancement in the field of generative ML, it will soon become significantly easier to generate images that are indistinguishable from actual photographic images depicting the sexual abuse of real children.
For Ms. Pfeffferkorn the problem of deepfakes is now a very real and urgent problem. At page 25 of the paper she asserts: “There is an urgent need, exacerbated by the breakneck pace of advancements in machine learning, for Congress to invest in solving this technical challenge.”
Professor Capra and the Committee see no “urgent need” to act. They do so in part because of their belief that new technology will emerge (or already exists) that is able to detect deepfakes and so this problem will just go away. Professor Capra has one expert to support that view, Grant Fredericks, the president of Forensic Video Solutions. I looked at the company website and see no claims to development or use of any new technologies. Capra relies on the vendor promises to detect fake videos and keep them out of evidence, “both because they can be discovered using the advanced tools of his (Fredricks) trade and because the video’s proponent would be unable to answer basic questions to authenticate it (who created the video, when, and with what technology.” Professor Capra’s Memorandum to the Committee at pg. 26 (pg. 39 of 358).
Capra’s memorandum to the Committee at first discusses why GenAI fraud detection is so difficult. He explains the cat and mouse competition between image generation software makers and fraud detection software companies. Oddly enough, his explanation seems correct to me, and so appears to impeach his later conclusion and the opinion of his expert, Fredericks. Here is the part of Capra’s memorandum that I agree with:
Generally speaking, there is an arms race between deepfake technology and the technology that can be employed to detect deepfakes. . . . any time new software is developed to detect fakes, deepfake creators can use that to their advantage in their discriminator models. A New York Times report reviewed some of the currently available programs that try to detect deepfakes. The programs varied in their accuracy. None were accurate 100 percent of the time.
Professor Capra’s supports his statement that “none were accurate 100 percent of the time,” by citing to a NYT article, Another Side of the A.I. Boom: Detecting What A.I. Makes (NYT, May 19, 2023). I read the article and it states that there are now more than a dozen companies offering tools to identify whether something was made with artificial intelligence, including Sensity AI, Optic, Reality Defender and FakeCatcher. The article repeats Professor Capra’s arms race scenario, but adds how the detector software always lags behind. That is common in cybersecurity too, where the defender is always at a disadvantage. Here is a quote from the NYT article:
Detection tools inherently lag behind the generative technology they are trying to detect. By the time a defense system is able to recognize the work of a new chatbot or image generator, like Google Bard or Midjourney, developers are already coming up with a new iteration that can evade that defense. The situation has been described as an arms race or a virus-antivirus relationship where one begets the other, over and over.
That has always been my understanding too, which is why I cannot believe that new technology is around the corner to finally make detection foolproof or that Grant Fredericks has a magic potion. I think it is more likely that the spy versus spy race will continue and uncertainty will be with us for a long time. Still, I sincerely hope that Professor Capra is right, and the fake image dangers are overstated. That’s my hope, but reason and science tells me that’s a risky assumption and we should mitigate our risks by making some modest revisions to the rules now. I would start with the two short proposals of Grimm and Grossman (as slightly revised in September 2024 and explained in Part Two).
Spy v. Spy image in 50s Pop Art stye by Ralph Losey using Visual Muse
Professor Capra’s Discussion of the Proposed Rule Amendments
There were four rule change proposals before the Committee in April 2024. One by Professor Andrea Roth of the University of California, Berkeley, School of Law, a second by Professor Rebecca Delfino of Loyla Law School and a third by Judge Paul Grimm (retired) and Professor Maura Grossman, already well known to most of my readers. I omit discussion here of a fourth proposal by John LaMonga in the interests of time, but you can learn about it in Professor Capra’sMemorandum to the Committee at pgs. 23-25 (pgs. 36-38 of 358). Also see John P. LaMonaga, A Break from Reality: Modernizing Authentication Standards for Digital Video Evidence in the Era of Deepfakes, 69 Am. U.L. Rev. 1945, 1984 (2020).
Professor Andrea Roth’s Rule Proposals
Professor Roth’s suggestions are nerdy interesting and forward thinking. Her suggestions are found in Professor Capra’s Memorandum to the Committee at pgs. 10-13 (pgs. 23-26 of 358) and Capra’s critical comments of the proposals follow at pgs. 13-16 (pgs. 26-29 of 358). I urge interested readers to check out her proposals for yourself. Capra’s comments seem a bit overly critical and I look forward to hearing more from her in the future.
The proposal addresses what could be thought to be a gap in the rules. Expert witnesses must satisfy reliability requirements for their opinions, but it is a stretch, to say the least, to call machine learning output an “opinion of an expert witness.”
Panel of AI Experts image by Ralph Losey who consults with them frequently
For me Andrea Roth’ proposals are not a stretch, to say the least, but common sense based on my everyday use of generative AI.
Andrea Roth also suggests that Rule 806. Attacking and Supporting the Declarant’s Credibility be amended to allow opponents to “impeach” machine output in the same way as they would impeach hearsay testimony from a human witness. Professor Capra of course criticizes that too, but this time is more kind, saying at page 13 of his memo.
The goal here is to treat machine learning — which is thinking like a human — the same way that a human declarant may be treated. Thought must be given to whether all the forms of impeachment are properly applicable to machine learning. . . . The question is whether an improper signal is given by applying 806 wholesale to machine related evidence, when in fact not all the forms of impeachment are workable as applied to machines. That said, assuming that some AI-related rule is necessary, it seems like a good idea, eventually, to have a rule addressing the permitted forms of impeachment of machine learning evidence.
I thought Andrea Roth’s suggestion was a good one. I routinely cross-examine AI on their outputs and opinions. It is an essential prompt engineering skill to make sure their opinions are reliable and understand the sources of their opinions.
Due to concerns over the length of this article I must defer further discussion of Professor Andrea Roth’s work and proposals for another day.
Robot cross-examined in impressionism style by Ralph Losey using Visual Muse
Professor Rebecca Delfino’s Proposal to Remove Juries From Deepfake Authenticity Findings
Professor Rebecca Delfino of Loyla Law School is a member of the Committee’s expert panel. She is very concerned about the dangers of the powerful emotional impact of audiovisuals on jurors and the costs involved in authenticity determinations. Her recent writings on these issues include: Deepfakes on Trial: A Call to Expand the Trial Judge’s Gatekeeping Role To Protect Legal Proceedings from Technological Fakery, 74 HASTINGS L.J. 293 (2023); The Deepfake Defense—Exploring the Limits of the Law and Ethical Norms in Protecting Legal Proceedings from Lying Lawyers, Loyola Law School, 84 Ohio St. L.J., Issue 5 1068 [2024]; Pay-To-Play: Access To Justice In The Era Of Ai And Deepfakes, 55 Seton Hall L.Rev., Book 3, __ (forthcoming 2025) (Abstract: “The introduction of deepfake and AI evidence in legal proceedings will trigger a failure of the adversarial system because the law currently offers no effective solution to secure access to justice to pay for this evidence for those who lack resources.“) Professor Rebecca Delfino argues that the danger of deepfakes demands that the judge decide authenticity, not the jury.
Sub-Issue on Jury Determinations and the Psychological Impact of Deepfakes
I am inclined to agree with Professor Delfino. The important oral presentation of Paul Grimm and Maura Grossman to the Committee shows that they do too. We have a transcript of that by Fordham Law Review, Daniel J. Capra, Deepfakes Reach the Advisory Committee on Evidence Rules, 92 Fordham L.R. 2491 (2024) at pgs. 2421-2437.
Paul and Maura make a formidable team of presenters, including several notable moments where Maura shows Capra and his Committee a few deepfakes she made. In the first she put Paul Grimm’s head on Dan Capra’s body, and vice versa, which caused Dan to quip “I think you lose in that trade, Paul.” Then she asked the panel to close their eyes and listen to what turned out to be a fake audio of President Biden directing the Treasury Depart to make payment of $10,000 to Daniel Capra. Id. at pgs. 2427-2427.
I thought this was great ploy. Maura then told the Committee she made it in seconds using free software on the internet and that with more work it would sound exactly like the President. Id. at 2426-2427. Professor Capra, who has been stung before by surprise audios, did not seem amused and his ultimate negative recommendations show he was not persuaded.
Here are excerpts of the transcript of the next section of their presentation to the Committee.
PROF. GROSSMAN. Because there are two problems that these deepfakes and that generative AI cause. One is we’re moving into a world where none of us are going to be able to tell what is real from not real evidence—which of these videos are real, which of these aren’t. And I’m very worried about the cynicism and the attitude that people are going to have if they can’t trust a single thing anymore because I can’t use any of my senses to tell reality.
And the other is what they call the liar’s dividend, is why not doubt everything, even if it’s in fact real, because now I can say, “How do you know it’s not a deepfake?”, and we saw a lot of that in the January 6 cases. Some of the defendants said, “That wasn’t me there” or “How do you know it was me?”187 Elon Musk used that defense already.188 So you’re going to have both problems: one where it really is fake, and now every case going to require an expert; and the other where it really is real evidence, and you don’t want to become so cynical that you don’t believe any of it.
When we entered this age of deepfakes, anybody can deny reality. … That is the classic liar’s dividend.
The liar’s dividend is a term coined by law professors Bobby Chesney and Danielle Citron in a 2018 paper laying out the challenges deepfakes present to privacy, democracy, and national security. The idea is, as people become more aware of how easy it is to fake audio and video, bad actors can weaponize that skepticism. “Put simply: a skeptical public will be primed to doubt the authenticity of real audio and video evidence,” Chesney and Citron wrote.
Liar’s Dividend image by Ralph Losey in surrealistic style using Visual Muse
Back to the transcript of the presentation of Grossman and Grimm to the Committee: Judge Grimm went on to explain why, under the current rules, the jury may often have to make the final determination of authenticity. They emphasize that even if the jury decides it is inauthentic, the jury will still be tainted by the process, as they cannot unsee what they have seen. Instructions from a judge to disregard the video seen will be ineffective.
JUDGE GRIMM: Now there’s one monkey wrench in the machinery: When you’re dealing with authentication, you’re dealing with conditional relevance if there’s a challenge to whether or not the evidence is authentic. And so, if you’re going to have a factual situation where one side comes in and says, “This is the voice recording on my voicemail, this is the threatening message that was left on my voicemail, that’s Bill, I’ve known Bill for 10 years, I am familiar with Bill’s voice, that is plausible evidence from which a reasonable factfinder could find that it was Bill.”
If Bill comes in and says, “That was left at 12:02 PM last Saturday, at 12:02 PM I have five witnesses who will testify that I was at some other place doing something else where I couldn’t possibly have left that,” that is plausible evidence that it was not Bill.
And when that occurs, the judge doesn’t make the final determination under Rule 104(a).209 The jury does.210 And that’s a concern because the jury gets both versions now. It gets the plausible version that it is; it gets the plausible version that it’s not. The jury has to resolve that factual dispute before they know whether they can listen to that voicemail and take it into consideration as Bill’s voice in determining the outcome of the case.
PROF. GROSSMAN: Can I add just one thing? Two studies you should know about. One is jurors are 650 percent more likely to believe evidence if it’s audiovisual, so if that comes in and they see it or hear it, they are way more likely to believe it.211 (Rebecca A. Delfina, Deepfakes on Trial: A Call to Expand the Trial Judge’s Gatekeeping Role to Protect Legal Proceedings from Technological Fakery, 74 HASTINGS L.J. 293, 311 fn.101–02 (2023)).
And number two, there are studies that show that a group of you could play a card game. I could show you a video of the card game, and in my video it would be a deepfake, and I would have one of you cheating. Half of you would be willing to swear to an affidavit that you actually saw the cheating even though you didn’t because that video—that audio/video, the deepfake stuff—is so powerful as evidence that it almost changes perception.212 (See Wade, Green & Nash, Can Fabricated Evidence Induce False Eyewitness Testimony?, 24 APPLIED COGNITIVE PSYCH. 899 (2010)).
Fake videos can change your memory of perception. Image in Pop Art style by Ralph Losey
CHAIR SCHILTZ: But why would judges be any more resistant to the power of this than jurors?
JUDGE GRIMM: Well, for the same reason that that we believe that in a bench trial that the judge is going to be able to distinguish between the admissible versus the non-admissible.
CHAIR SCHILTZ: I know, but it is often fictional, right? There are certain things that I really am no better at than a juror is, like telling a real picture from an unreal picture, or deciding which of these two witnesses to believe—between the witness who says, “That’s his voice,” and the witness who said, “It couldn’t have been me.” Why am I any better at that than a juror?
JUDGE GRIMM: You might be better than a juror because you, as the judicial officer, can have it set up so that you have a hearing beforehand, which is a hearing on admissibility that the jury is not going to hear; and you have the witnesses come in, and you hear them; or you have a certificate under Rule 902(13). Also, you will be a repeat player.
PROF. GROSSMAN: Right. And you would at least know the questions to ask: How was this algorithm trained? Was it tested? What was it tested on? Who did the testing? Were they arm’s length? What’s the error rate?
JUDGE GRIMM: And order the discovery that the other side can have to be able to have the opportunity To challenge it by bringing that in.
The Chair, Hon. Patrick J. Schiltz asks good questions here and understands the issue. Anyone should be far more comfortable having a judge, especially one like Judge Schiltz, making the hard calls instead of a room of randomly called jurors. There is no question in my mind that judges are far better qualified than jurors to make these determinations. All three experts were making that point, Paul Grimm, Maura Grossman and Rebecca Delfino.
Real or Fake image in post-apocalyptic-futurism style by Ralph Losey
Back to Professor Rebecca Delfino’s Proposal
Here is Professor Capra explanation to the Committee of how Professor Delfino’s proposed rule changes would work. Unfortunately I have not found any argument from her on her proposal, just Capra’s explanation and he ultimately rejected it.
Professor Rebecca Delfino argues that the danger of deepfakes demands that the judge decide authenticity, not the jury.19 She contends that “[c]ountering juror skepticism and doubt over the authenticity of audiovisual images in the era of fake news and deepfakes calls for reallocating the fact finding authority to determine the authenticity of audiovisual evidence.” She contends that jurors cannot be trusted to fairly analyze whether a video is a deepfake, because deepfakes appear to be genuine, and “seeing is believing.” Professor Delfino suggests that Rule 901 should be amended to add a new subdivision (c), which would provide:
901(c). Notwithstanding subdivision (a), to satisfy the requirement of authenticating or identifying an item of audiovisual evidence, the proponent must produce evidence that the item is what the proponent claims it is in accordance with subdivision (b). The court must decide any question about whether the evidence is admissible.
She explains that the new Rule 901(c) “would relocate the authenticity of digital audiovisual evidence from Rule 104(b) to the category of relevancy in Rule 104(a)” and would “expand the gatekeeping function of the court by assigning the responsibility of deciding authenticity issues solely to the judge.”
The proposed rule would operate as follows: After the pretrial hearing to determine the authenticity of the evidence, if the court finds that the item is more likely than not authentic, the court admits the evidence. The court would instruct the jury that it must accept as authentic the evidence that the court has determined is genuine. The court would also instruct the jury not to doubt the authenticity, simply because of the existence of deepfakes. This new rule would take the Memorandum to the Committee at pgs. 22-23 (pgs. 35-36 of 358).
This proposal sounds feasible to me. It could help reduce the costs of expert battles and counter the Liar’s Dividend and CSI Effect. Professor Capra made a few helpful comments as to how Professor Delfino’s language would benefit by a few minor changes. But those are moot points because he respectfully declined to endorse the proposal noting that: “Given the presence of deepfakes in society, it may well be that jurors will do their own assessment, regardless of the instruction.” He seems to miss the point of minimizing the psychological impact on jurors by keeping deepfake videos and audios out of the jury room.
Fake or True image in photorealistic style by Ralph Losey using Visual Muse
Paul Grimm and Maura Grossman‘s Two Rule Proposals
Two rule change proposals were made in early 2024 by Paul Grimm and Maura Grossman. (They were revised slightly and resubmitted in September 2024 as explained in Part Two of this article.) Paul and Maura are both well known to my readers as progressive leaders in law and technology. They have been working on these evidentiary issues for years. See eg.,The GPTJudge: Justice in a Generative AI World, 23 Duke Law & Technology Review 1-34 (2023).
They were invited to present their proposals to the Committee to modify Rule 901(b)(9) for AI evidence and add a new Rule 901(c) for “Deepfake Evidence.” The transcript of their presentation was referred to previously. Deepfakes Reach the Advisory Committee on Evidence Rules, 92 Fordham L.R. 2491 (2024) at pgs. 2421-2437. I recommend you read this in full.
Here are the two rule changes Paul and Maura proposed:
901(b) Examples. The following are examples only—not a complete list—of evidence that satisfies the requirement [of Rule 901(b)]: (9) Evidence about a Process or System. For an item generated by a process or system: (A) evidence describing it and showing that it produces an accuratea valid and reliable result; and (B) if the proponent concedes that the item was generated by artificial intelligence, additional evidence that: (i) describes the software or program that was used; and (ii) shows that it produced valid and reliable results in this instance.
Proposed New Rule 901(c) to address “Deepfakes”:
901(c): Potentially Fabricated or Altered Electronic Evidence. If a party challenging the authenticity of computer-generated or other electronic evidence demonstrates to the court that it is more likely than not either fabricated, or altered in whole or in part, the evidence is admissible only if the proponent demonstrates that its probative value outweighs its prejudicial effect on the party challenging the evidence.
As you can see their proposed new rule 901(c) makes it clear that a judge may take the jury out of the “fake or real” determination in close questions, and in so doing take away most of the potential prejudicial impact upon jurors. The burden of possible unconscious prejudice and emotional impact from viewing inadmissible deepfake media would be born solely by the judge. As discussed, the judge is better trained for that and will have the benefit of pretrial hearings and expert testimony. The jury retains its traditional power over all other determinations of justiciable facts. Note that this proposal does not go as far as Professor Delfino’s in taking determinations away from the jury and expanding the gatekeeper role of the judge. More on 901(c) in general will follow, but first the proposed revisions to Rule 901(b)(9).
Accuracy v. Reliability and Validity
Professor Capra killed both of the Grimm and Grossman proposals after asking for input from only one expert on his panel, the one who happened to be the only one on the panel proposing a competing rule change, Professor Rebecca Wexler. You might expect her to oppose Grimm and Grossman’s proposal, lobbying instead for her own rival proposals. To her credit she did not. Instead, in Capra’s own words, she “supported the proposals but suggested that they should be extended beyond AI. Memorandum to the Committee at pgs. 9-10 (22-23 of 358). As to the amendment to Rule 901(b)(9) Professor Wexler said:
Re: the first Grimm/Grossman proposal, it may well be that the standard for authenticating system/process evidence should require a showing that the system/process produces “valid” and “reliable” results, rather than merely accurate results. . . .
I can understand the push to add a reliability requirement to 901(b)(9). It’s true that ML systems could rely on an opaque logic that gives accurate results most of the time but then sometimes goes off the rails and creates some seemingly illogical output. But manually coded systems can do the same thing. They could be deliberately or mistakenly programmed to fail in unexpected conditions, or even once every hundred runs on the same input data. So if reliability is important, why not make it a broader requirement?
Still, Capra seemed to give little weight to her input and stuck with his objection. He continued to insist that the use of the words “valid and reliable” instead of “accurate” in Rule 901(b)(9) is an unnecessary and confusing complication. It appears that he does not fully understand the nerdy AI based technical reasons behind this change. Notice that Capra once again relies on a vendor, Evidently AI, to try to support his attempt to get technical. Professor Capra says in his Memorandum to the Committee at page 7 (20 of 358).
The proposal (on Rule 901(b)(9)) distinguishes the terms “validity,” “reliability,” and “accuracy.” That is complicated and perhaps may be unnecessary for a rule of evidence. . . . As to “accuracy”, the proposal rejects the term, but in fact there is a good deal of material on machine learning that emphasizes “accuracy.” See, e.g., https://www.evidentlyai.com/classification-metrics/accuracy-precision-recall . . . The whole area is complicated enough without adding distinctions that may not make a difference.
Too complicated, really? Meaningless distinctions? Maura Grossman and Paul Grimm, who have extensive experience actually using these evidence rules in court, and are both bonafide nerds (especially Maura), were not, to my knowledge, given an opportunity to respond to these criticisms. I have not talked to them about this but would imagine they were not pleased.
Obviously fake Image of Judge Grimm as an unhappy robot by Ralph Losey using Visual Muse
To be continued … Part Two of this article will complete the analysis of the Grimm – Grossman rule proposals and look at what comes next with the Rule Committee meeting of November 8, 2024. It will also include a discussion of a second, slightly revised proposal by Paul Grimm and Maura Grossman that they just submitted and discussion of the new article by Judge Paul Grimm (retired), Professor Maura Grossman and six other experts: Deepfakes in Court: How Judges Can Proactively Manage Alleged AI-Generated Material in National Security Cases. They are all trying, once again, to push the Committee into action. Let us hope they succeed. Don’t look up, but an election is coming.
“Don’t Look Up” image in Dark Fantasy style by Ralph Losey using Visual Muse
Ralph Losey is an AI researcher, writer, tech-law expert, and former lawyer. He's also the CEO of Losey AI, LLC, providing non-legal services, primarily educational services pertaining to AI and creation of custom AI tools.
Ralph has long been a leader of the world's tech lawyers. He has presented at hundreds of legal conferences and CLEs around the world. Ralph has written over two million words on AI, e-discovery and tech-law subjects, including seven books.
Ralph has been involved with computers, software, legal hacking and the law since 1980. Ralph has the highest peer AV rating as a lawyer and was selected as a Best Lawyer in America in four categories: Commercial Litigation; E-Discovery and Information Management Law; Information Technology Law; and, Employment Law - Management.
Ralph is the proud father of two children and husband since 1973 to Molly Friedman Losey, a mental health counselor in Winter Park.
All opinions expressed here are his own, and not those of his firm or clients. No legal advice is provided on this web and should not be construed as such.
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