The Ethics of AI: Implications for Attorneys and Mediators
Course Description:
As artificial intelligence (AI) continues to revolutionize industries, legal professionals—including attorneys and mediators—must navigate the ethical implications of AI-driven decision-making, legal analysis, and dispute resolution. This CLE course explores how AI intersects with legal practice, mediation, and professional ethics, focusing on compliance with ethical standards, bias mitigation, and risk allocation.
Participants will gain insight into the benefits and risks of using AI in legal settings, how AI can assist in mediation, and the ethical duties lawyers and mediators must uphold when incorporating AI into their practice.
Learning Objectives:
- Define AI, machine learning (ML), deep learning (DL), and generative AI (GAI) in a legal context.
- Understand AI’s role in legal research, dispute resolution, and client representation.
- Identify ethical challenges AI presents in mediation and legal practice.
- Examine the duty of competence, confidentiality, and bias mitigation under professional conduct rules.
- Evaluate risk allocation and liability when AI tools fail.
Agenda
I. Introduction to AI in Legal and Mediation Practice
- What is AI? Overview of ML, DL, and GAI.
- How AI is changing the legal profession and mediation.
- The balance between technological advancement and ethical responsibility.
II. AI Applications in Law and Mediation
- AI in legal research, contract drafting, predictive analytics, and dispute resolution.
- AI-driven mediation tools: Enhancing negotiations and case evaluation.
- Case examples of AI’s effectiveness and limitations in legal and mediation settings.
III. Ethical Challenges of AI for Lawyers and Mediators
- Rules of Professional Conduct: Competence (Model Rule 1.1), Confidentiality (Model Rule 1.6), and Supervision (Model Rules 5.1 & 5.3).
- Duty of Candor (Model Rule 3.3(a))
- Model Standards of Conduct for Mediators: Impartiality, conflicts of interest, quality of the process, and self-determination.
- Data reliability, algorithmic bias, and their impact on legal fairness.
IV. AI Risk Allocation and Legal Liability
- Who is responsible when AI fails? Lawyer, mediator, developer, or client?
- Privacy concerns, data security, and compliance with HIPAA, GDPR, and other regulations.
- Strategies for mitigating AI-related risks in legal and mediation settings.
V. Best Practices and Final Considerations
- Due diligence before implementing AI tools.
- Ensuring transparency, fairness, and accountability in AI-assisted decision-making.
- Key takeaways and Q&A.
Originally recorded October 29, 2024.
Web broadcasts available on-demand are not accredited in Missouri and may only be utilized by lawyers for self-study. Up to six self-study hours may be reported by lawyers in one compliance year and may not include ethics or diversity, inclusion, cultural competency or explicit and implicit bias credit.
This program is pending CLE approval in GA, NC, FL, and TN.
