AI Governance Checklist

Comprehensive AI strategy aligned with business objectives

Document outlining AI vision, objectives, use cases, and success metrics

Cross-functional committee overseeing AI initiatives

Committee with representatives from IT, legal, compliance, business units, and ethics

Ethical principles and guidelines for AI development and deployment

Framework addressing fairness, transparency, accountability, and human oversight

Systematic process for identifying and evaluating AI-related risks

Process covering technical, ethical, legal, and business risks

Clear policies governing how AI can be used within the organization

Policy covering acceptable use, prohibited applications, and approval processes

Policies for data collection, processing, and usage in AI systems

Governance covering data quality, lineage, privacy, and retention for AI

Framework ensuring AI systems comply with relevant regulations

Compliance with GDPR, CCPA, industry-specific regulations, and emerging AI laws

Guidelines for evaluating and managing third-party AI services

Policy covering vendor assessment, contracts, and ongoing monitoring

Systematic approach to managing AI models from development to retirement

MLOps practices including versioning, testing, deployment, and monitoring

Continuous monitoring of AI system performance and behavior

Monitoring for accuracy, bias, drift, and unexpected behaviors

Tools and processes to identify and address algorithmic bias

Regular bias testing across different demographic groups and use cases

Capability to explain AI model decisions and predictions

Tools and techniques for model interpretability and decision transparency

Systematic testing of AI models before full deployment

Framework for controlled testing and gradual rollout of AI systems

Robust security measures for AI training and operational data

Encryption, access controls, and secure data handling throughout AI lifecycle

Techniques to protect individual privacy in AI systems

Implementation of differential privacy, federated learning, or data anonymization

Protection against adversarial attacks and model theft

Security measures including model encryption, access controls, and attack detection

Plan for responding to AI-related security or ethical incidents

Procedures for incident detection, response, communication, and remediation

Comprehensive documentation of AI systems and their capabilities

Documentation covering purpose, data sources, algorithms, limitations, and risks

Standardized documentation for each AI model

Model cards documenting intended use, performance metrics, and known limitations

Assessments of AI system impact on stakeholders and society

Regular assessments covering social, economic, and environmental impacts

Clear communication about AI use to relevant stakeholders

Communication plans for employees, customers, and other affected parties

Training programs on AI ethics and responsible AI practices

Regular training for all employees involved in AI development or deployment

Technical training on AI governance tools and processes

Training on bias detection, model monitoring, and governance workflows

AI governance awareness training for leadership team

Executive training on AI risks, opportunities, and governance responsibilities