How to Conduct a Good Risk Assessment for AI Deployment

A good AI deployment risk assessment is a systematic process to identify and mitigate potential harms before launch. It evaluates technical performance, ethical concerns, and operational impacts across the AI lifecycle.

Key principles include involving a cross-functional team (technical, legal, ethical, domain experts), establishing clear assessment scope and criteria based on intended use and context, and prioritizing potential harms like bias, safety failures, privacy breaches, and security vulnerabilities. Thorough documentation of findings, underlying assumptions, and data sources is essential for accountability and review. The process must be iterative, reflecting model and deployment environment changes.

Conduct the assessment by first defining the AI's purpose, deployment context, and stakeholders. Identify potential risks in development data, model behavior, integration, and real-world use. Evaluate each risk's severity and likelihood. Develop mitigation strategies (e.g., bias testing, security controls) and assign responsibility. Finally, implement ongoing monitoring for continuous risk management. This proactive approach reduces legal, reputational, and operational risks, fostering responsible innovation and stakeholder trust.