Authenticity, Admissibility, and the New Reality of Synthetic Content (Part 2 of 3)

Since the early days of legal history, evidence authentication followed a practical version of Occam’s Razor: the simplest explanation was usually the correct one. If a photograph looked authentic, a document appeared original, and the surrounding facts aligned, courts generally presumed the evidence was what it appeared to be. 

Photos were photos.
Audio was audio.
Documents were documents. 

Certainly, manipulation existed. But in most cases, the underlying premise remained intact: digital evidence generally reflected something real. If it looks and acts like a duck….. 

In digital forensics’ infancy, analysis used basic metadata authentication which would flush out the “non-duck” imposters. 

Artificial intelligence changes that assumption entirely. 

We are entering an era where digital evidence may be partially synthetic, fully synthetic, AI-enhanced, or AI-manipulated sometimes with no obvious indication that AI was involved at all. 

That reality is forcing litigators, judges, forensic investigators, and E-discovery professionals to confront a new evidentiary challenge. 

Seeing is no longer believing. 

This article is the second in our three-part AI on Trial series. In Part One, we explored how AI is transforming investigations by helping legal teams analyze complex datasets faster and more efficiently. 

Part Two focuses on a more complicated question: 

What happens when AI is no longer just helping analyze evidence but actively creating it? 

The answer raises difficult questions around authenticity, admissibility, disclosure, and defensibility. 

The Good: When AI Helps Explain the Truth 

Not all AI-generated evidence creates risk. 

Used responsibly, AI-generated content can improve understanding and help clarify complex facts. 

This is especially true for demonstrative evidence. 

AI-assisted demonstratives can help: 

  • Reconstruct complex timelines 
  • Visualize accident sequences 
  • Clarify spatial relationships 
  • Illustrate competing perspectives 
  • Improve courtroom communication 

In complex litigation, this can be extremely valuable. 

Large datasets, technical evidence, and complicated timelines are often difficult to explain clearly to judges and juries. AI-assisted visualizations can make complicated facts easier to understand. 

A frequently cited example is State of Florida v. Miguel Albisu, where AI-generated courtroom visuals were reportedly used to help illustrate perspective and context. 

Case Example: AI-Assisted Demonstratives in Court — State of Florida v. Miguel Albisu 

A useful example of responsible AI use in litigation can be seen in State of Florida v. Miguel Albisu, involves a 2023 incident at a Southwest Ranches wedding venue where the owner, Miguel Albisu, faces nine counts of aggravated assault with a firearm for waving a loaded gun at guests. Albisu argued self-defense under Florida’s “Stand Your Ground” law. Albisu’s defense team, led by attorney Kenneth Padowitz, sought to have the charges dismissed. They argued that Albisu was legally justified in drawing his firearm to protect himself, his family, and his property. His defense maintained that he was cornered against a bar and acted reasonably to stop an ongoing threat. In a highly publicized series of hearings in December 2024, the defense introduced a Virtual Reality (VR) recreation of the crime scene to bolster their self-defense claim. Broward County Circuit Court Judge Andrew Siegel, alongside the prosecuting and defense attorneys, put on Oculus Quest 2 headset. The VR simulation allowed the judge to experience the alleged altercation from Albisu’s exact perspective, demonstrating the chaotic nature of the fight and the moment he drew his weapon.  

Importantly, the AI-generated visuals were not introduced as independent substantive evidence. Instead, they functioned as demonstrative aids designed to help explain complex facts and improve understanding. 

That distinction matters. 

Used appropriately, AI-generated demonstratives can help legal teams: 

  • Clarify complex events 
  • Reconstruct timelines 
  • Illustrate perspective and spatial relationships 
  • Improve communication with judges and juries 

This represents one of AI’s most promising applications in litigation despite the ruling against Albisu. 

When used transparently, grounded in authentic evidence, and subject to proper disclosure, AI-assisted demonstratives can enhance understanding without compromising evidentiary integrity. 

The concern is not AI itself. 

The concern arises when AI-generated content is presented without transparency, without validation, or in a way that blurs the line between illustration and evidence. 

Used transparently and carefully, AI-assisted demonstratives can improve communication without distorting the underlying facts. 

AI-generated evidence is not inherently problematic. 

The issue is not whether AI was used. 

The issue is how it was used—and whether that use was disclosed. 

The Bad: Synthetic Deception 

The same technology that can clarify truth can also distort it. 

This is where the risks become significant. 

AI can now generate or alter: 

  • Deepfake audio 
  • Synthetic video 
  • Fabricated screenshots 
  • Fake text messages 
  • Altered documents 
  • Synthetic exhibits 
  • AI-generated images 

And increasingly, these manipulations are becoming more sophisticated and harder to detect. 

What once required advanced technical skill can now be done with widely available consumer tools. 

That dramatically lowers the barrier to deception. 

The concern is no longer theoretical. 

Courts and litigators are already encountering questions surrounding fabricated or manipulated digital evidence. 

The issue becomes especially dangerous when AI-generated content is introduced without disclosure. 

That raises immediate questions: 

  • Was the use disclosed? 
  • Was evidence altered? 
  • Was content manipulated? 
  • Were ethical obligations violated? 

These questions go directly to admissibility and credibility. 

The Rise of the Liar’s Dividend 

The emergence of synthetic media creates a second, more complicated problem. 

As AI-generated content becomes easier to create, authentic evidence becomes easier to deny. 

This concept is often referred to as the “liar’s dividend.” 

It creates a troubling new dynamic in litigation. 

A damaging voicemail can be dismissed as fake.
A legitimate video can be challenged as AI-generated.
A genuine screenshot can suddenly require expert validation. 

The result is a growing erosion of trust in digital evidence itself. 

This is where the litigation landscape changes dramatically. 

The burden is shifting. 

Authentic evidence increasingly requires stronger validation. 

Routine evidence that may have been accepted at face value just a few years ago may now demand forensic authentication. 

That means: 

  • Increased burden on forensic validation  
  • Erosion of trust 
  • Greater scrutiny of digital evidence 
  • Higher evidentiary burdens 

This may become one of AI’s most disruptive effects on modern litigation.  

The Authentication Arms Race 

The defensibility standard is changing. 

Courts increasingly need to ask not only whether evidence is relevant, but whether it is authentic. 

That changes the questions litigators and investigators must ask. 

Today, legal teams may need to evaluate: 

  • Was AI involved? 
  • What role did AI play? 
  • Was content altered? 
  • Was AI use disclosed? 
  • Is the metadata trustworthy? 
  • Can the output be authenticated? 
  • Was human oversight applied? 

These are no longer edge-case questions. 

They are rapidly becoming central to digital evidence workflows. 

Forensic investigators increasingly need to perform: 

  • Metadata analysis 
  • Source verification 
  • AI detection workflows 
  • Provenance tracking 
  • Validation testing 
  • Chain-of-custody review 

Authentication is becoming both more technical and more important. 

What This Means for Litigation Teams 

This shift has significant implications for counsel, litigation support teams, and E-discovery professionals. 

The old workflow often looked like this:
Collect → Review → Produce → Argue 

That model is changing. 

Now, teams increasingly need to think:
Collect → Authenticate → Validate → Review → Produce → Defend 

That additional layer matters. 

Questions of provenance, authenticity, and AI disclosure may become central much earlier in the case lifecycle. 

The most effective legal teams will be the ones that adapt quickly. 

That means: 

  • Preserving native files 
  • Retaining metadata 
  • Documenting AI involvement 
  • Validating source authenticity early 
  • Escalating suspicious evidence for forensic review 

In many matters, early validation may significantly reduce downstream risk. 

Looking Ahead 

AI-generated evidence is not inherently good or bad. 

The real issue is transparency. 

And increasingly, defensibility. 

AI can help explain truth. AI can also distort truth. 

The same technology that improves communication can also undermine trust. 

That is the challenge facing modern litigation. 

The courtroom is entering an era where proving authenticity may become just as important as proving facts. 

In Part Three of this series, we examine the next evolution of this challenge: what happens when AI is no longer just creating evidence but actively helping conceal misconduct, fabricate narratives, and bury the truth.