AI audits and assessments by LegalAIR: A comprehensive approach
Objective
To ensure that AI systems are developed and used responsibly and ethically, in line with the organisation’s objectives, legal requirements and industry standards.
Key Considerations
- Establish a multidisciplinary audit team: Assemble a team with diverse expertise in AI development, data science, legal compliance and ethics to ensure comprehensive evaluation of AI systems.
- Define the scope and objectives: Create a clear overview of the AI system’s purpose, architecture and components to be monitored, and identify key risks and compliance gaps to be addressed.
- Assess data quality and integrity: Evaluate the quality, relevance, and adequacy of data used by AI systems, and identify potential biases, inaccuracies, or incompleteness.
- Evaluate AI model performance: Review the performance measures, benchmarking techniques and validation methods used in the development of AI systems and verify the accuracy, reliability and stability of AI models.
- Review documentation and governance: Ensure clear documentation of the design, development and deployment of AI systems and evaluate governance structures, policies and procedures for compliance with legal requirements and industry standards.
- Identify and mitigate risks: Assess potential risks of AI systems, including privacy, security, ethical and reputational risks, and implement strategies and safeguards for risk mitigation.
- Report and monitor: Document audit findings, recommendations and action plans and ensure ongoing monitoring and regular audits to ensure continuous compliance and improvement.
Recommendations
- Follow AI auditing frameworks and regulations: Adhere to relevant AI auditing guidelines and regulations, such as IIA’s AI Auditing Framework.
- Maintain transparency and accountability: Ensure open communication about the audit process and findings, and establish clear roles and responsibilities within the audit team.
- Prioritise high-impact AI systems: Focus on auditing AI systems that have a significant impact on decision-making, customer interactions or data processing.
- Use interpretable and explainable AI: Implement AI models that are transparent, explainable and interpretable to ensure accountability and confidence in AI decision-making.
- Continuously review and update: Review and update AI systems, policies and procedures regularly to ensure they remain compliant with changing regulations and industry standards.
By taking this comprehensive approach to AI audits and assessments, organisations can ensure that their AI systems are developed and deployed responsibly and ethically, in line with the organisation’s objectives, legal requirements and industry standards.