Navigating the Ethical Frontier: AI in Legal Practice

As Artificial Intelligence rapidly reshapes the legal landscape, attorneys face a critical dual challenge: harnessing the unprecedented efficiency of AI tools while strictly adhering to immutable professional and ethical obligations. In the comprehensive course Navigating the Ethical Frontier: AI in Legal Practice, industry experts David Sanker and Kellen Sheeran provide a vital roadmap for practitioners seeking to integrate these technologies without compromising their duty of care.

This course moves beyond the hype to address the practical realities of Generative AI, machine learning, and AI agents within the practice of law. From the fundamental mechanics of how Large Language Models (LLMs) operate to the specific dangers of "hallucinations" and deep fakes, the content is designed to elevate a lawyer’s technical competence—now a requirement under ABA Model Rule 1.1. The presentation offers a deep dive into the patchwork of emerging regulations, including New York’s bias audits, Colorado’s high-risk AI statutes, and California’s transparency acts, ensuring that legal professionals remain compliant across jurisdictions.

Key areas of focus include the "black box" nature of AI decision-making, the imperative of data confidentiality, and the evolving standards for intellectual property regarding AI-generated works. By examining real-world case studies of litigation failures and successful implementation strategies, this course equips attendees with the knowledge to draft effective internal AI policies and vet third-party vendors properly.

Key Learning Outcomes:

• Technological Competence & Oversight: Understand the distinctions between public and enterprise AI models, and apply the duty of supervision (Rules 5.1/5.3) to algorithmic assistants.
• Risk Management & Verification: distinct strategies to identify and mitigate AI hallucinations, shifting the professional standard from "trust but verify" to "verify then trust."
• Ethical Duties & Confidentiality: Navigate the complexities of Rule 1.6 regarding client data security, encryption standards, and the risks of using open-source platforms.
• Intellectual Property Landscape: Analyze copyright infringement risks, the copyrightability of AI-generated content, and the impact of prompt engineering on ownership.
• Bias, Fairness, & Legislation: Identify potential biases in training data and navigate the growing list of state and federal regulations targeting automated decision-making tools.
• Practical Implementation: Learn best practices for billing, client disclosure, and establishing a "human in the loop" workflow to ensure accuracy and accountability.