UC Davis Law professor Peter Lee discusses the growing use of synthetic data to train AI models and its advantages over real-world data in addressing technical limitations and legal issues like privacy, bias, and copyright infringement. Professor Lee argues that while synthetic data offers promising solutions through unlimited, high-quality training content, it also poses significant risks including model collapse, new biases, and enabling dangerous AI applications, requiring careful regulation and responsible deployment.

Leading experts at an NYU webinar discussed three major constitutional challenges to the National Labor Relations Board (NLRB) in light of recent Supreme Court decisions: restrictions on presidential removal of Board members, the status of administrative law judges, and potential jury trial requirements. While panelists predicted the Supreme Court may be reluctant to completely invalidate the NLRB's structure, they acknowledged growing judicial skepticism toward administrative agency independence, with potential implications for labor relations and administrative governance more broadly.

Rutgers Law adjunct lecturer David S. Kemp argues that despite ChatGPT’s limitations in producing accurate legal research, these shortcomings can be leveraged as teaching tools in law schools. By encouraging students to use AI-generated text as a starting point and then to verify its content using reliable sources, educators can enhance students’ research skills, critical thinking, and ethical responsibility.