Managing Legal Risk During AI Implementation

Managing Legal Risk During AI Implementation

According to a Risk Management Magazine article by Chinh H. Pham and Samuel S. Stone, intellectual property attorneys at Greenberg Traurig, integrating artificial intelligence (AI) and emerging technologies is crucial for companies aiming to stay competitive. However, there are regulatory challenges and legal risk during AI implementation. 

The article notes three key principles for managing legal risk during AI implementation:

1. Aligning AI with Business Goals: Companies should establish teams of executives, legal experts, and technologists to ensure that AI projects align with business objectives. Defining the role of AI—whether for internal productivity, customer-facing products, or service enhancements—is critical. Misalignment often leads to wasted resources and missed opportunities.
2. Prioritizing Risk Management: A risk assessment should identify legal, ethical, and operational risks. Key considerations include data privacy, security, ethical concerns like AI bias, liability issues, and intellectual property ownership. Conducting compliance evaluations and documenting processes can mitigate potential legal and regulatory risks while prioritizing risk mitigation helps shape AI development.
3. Building Trust in AI Models: Organizations must build trust in AI by fostering a culture of compliance and ethical behavior. Training employees on the legal and ethical implications of AI is essential. Ensuring the “explainability” of AI decisions enhances user confidence. Additionally, organizations should consider insurance to cover risks from AI system failures. Human oversight remains important for quality control, minimizing errors, and aligning AI with organizational values.

These strategies help organizations implement AI responsibly, minimize risks, and increase stakeholder trust, ensuring the successful adoption of AI.