As artificial intelligence continues to reshape products, platforms, and decision-making, disputes involving AI systems have become increasingly technical and consequential. Litigation involving machine learning models, conversational systems, computer vision, and data-driven platforms often turns on how these technologies are designed, trained, evaluated, and deployed in practice.
DOAR represents senior researchers and practitioners from leading research institutions and industries who serve as artificial intelligence expert witnesses in matters involving the development, performance, and real-world use of AI systems. They understand the full technical lifecycle of modern AI, from model development and training through evaluation, deployment, and system integration across real-world products and platforms.
Our artificial intelligence experts provide clear, grounded testimony and analysis. Drawing on their experience as researchers, engineers, and interdisciplinary specialists, they help legal teams analyze alleged AI technical failures, evaluate AI system capabilities and limitations, and present complex AI technologies in terms that judges and juries can readily understand.
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We represent experts who are world-class academics and industry executives who were carefully selected for their knowledge, experience, and ability to communicate effectively.
Expertise in modern conversational AI systems, including large language models, chatbots, retrieval-augmented generation (RAG), prompting strategies, and grounding techniques that shape how AI systems generate and deliver responses.
Knowledge of speech recognition systems, audio signal processing, and digital signal processing (DSP), including how audio data is captured, processed, and interpreted by machine learning models.
Background in non-LLM AI approaches such as planning systems, optimization algorithms, and classical artificial intelligence methods used across industrial and research applications.
Experience with visual AI systems and generative models used for image analysis, synthesis, and pattern recognition across computer vision and non-text AI applications.
Experience with the infrastructure that supports AI development and deployment, including data pipelines, analytics platforms, distributed computing environments, and benchmarking methodologies used to evaluate system performance.
Research and applied expertise in AI safety, responsible AI development, and the psychological and behavioral impacts of AI systems, including perspectives from psychiatry and cognitive science.
Knowledge of the hardware and computing environments that power AI systems, including GPUs, accelerators, specialized AI chips, and large-scale computing infrastructure.
Success at the ITC depends not just on the legal merits, but on how effectively parties can organize technical complexity, align expert-driven narratives, and present a clear, disciplined case under significant time pressure. Let’s explore how venue nuances and current industry trends are impacting proceedings seen before the Commission.
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