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Predictive AI models can provide insights on future events, while biometrics aid in identification, and AI transcription services convert audio into written transcripts for court evidence. These are only some examples of AI-generated evidence.

Judges face challenges in evaluating the admissibility of such evidence with concerns related to reliability, transparency, interpretability, and bias in such evidence. This challenge becomes even more salient with the use of generative AI systems, which are contributing to misinformation and disinformation at scale. An example of such AI generated content is the image showing the pope wearing a white, puffy jacket, which seemed to be genuine.

Now, imagine if an image portrayed a political leader engaging in criminal activity. In such a scenario, how would a lawyer or judge demonstrate the authenticity of such an image? How can a judge determine that the image is AI-generated and not real? Moreover, in addition to the numerous risks that affect the authenticity and reliability of evidence, the opacity in AI algorithms hampers transparency, while bias in training data can lead to discriminatory outcomes. The absence of standard guidelines on how to verify AI-generated evidence complicates the decision-making process.

Self-driving cars present another real-world example of the challenges related to electronic evidence. For instance, there is uncertainty around how a drowsiness detector’s data could be used in inquisitorial or adversarial justice systems to determine liability for an accident? How will this data be made available for criminal investigation? Would machine data based on human-machine interaction count as evidence? We must assess the accuracy and limitations of the AI system's data, determine responsibility in the event of accidents or disputes, and understand the reasoning behind the system's decisions.

Judges play a crucial role in assessing the admissibility of AI-generated evidence, and they must learn to navigate the intricacies of AI to make well-informed decisions regarding its admissibility. Judges should develop an understanding of the algorithm, the specific data used for training, AI principles, biases and the potential misuse of AI systems, like deepfake.

Recognizing the need for discussion and capacity building on this topic, UNESCO organized a webinar titled "The Admissibility Challenge: AI-Generated Evidence in the Courtroom". In collaborating with the Inter-American Court of Human Rights (Costa Rica), the National Judicial College (United States), Lawyers Hub (Kenya), and the Center for Communication Governance, National Law University (India), the discussion tackled the complexities surrounding the admissibility of AI-generated evidence.

The event brought together around 400 participants and a diverse panel of experts who engaged in insightful discussions on this pressing topic. The speakers included Isabela Ferrari, Federal Judge of Brazil, Prof. Sabine Gless, an expert in criminal law and criminal procedure, Judge(retd.) Paul Grimm, Professor at Duke Law, Dr. Andrew Rens, Senior Research Fellow with expertise in information communication technology, and Stephen Mason, expert on electronic signatures and electronic evidence.