Governance for AI

Importance of Governance and Compliance

AI governance focuses on effectively managing, scaling, and optimizing an organization’s AI initiatives.
Strong governance is essential to building trust and ensuring that AI systems are developed and used responsibly.
A well-defined governance framework helps:
- Ensure responsible, ethical, and trustworthy AI practices
- Mitigate risks such as bias, privacy violations, and unintended consequences
- Establish clear policies, guidelines, and oversight mechanisms
- Ensure compliance with legal, regulatory, and industry requirements
- Reduce potential legal, financial, and reputational risks
- Foster public and stakeholder confidence in AI-driven solutions

A typical governance approach includes the following elements:
AI Governance Board or Committee
- Composed of representatives from legal, compliance, data privacy, and AI subject matter experts (SMEs)
- Provides cross-functional oversight and accountability
Defined Roles and Responsibilities
- Clearly outline responsibilities for oversight, policy development, risk assessment, and decision-making
- Establish escalation paths and approval processes
Policies and Procedures
- Develop comprehensive policies covering the full AI lifecycle
- Address data management, model development, deployment, monitoring, and retirement

AWS provides several services that help implement and enforce governance controls:
AWS Config – Monitor and assess configuration compliance
Amazon Inspector – Identify security vulnerabilities
AWS Audit Manager – Automate audit evidence collection
AWS Artifact – Access compliance reports and agreements
AWS CloudTrail – Track and log API activity
AWS Trusted Advisor – Optimize security, performance, and cost

Policies
- Establish principles and guidelines that promote responsible AI, including:
- Data management and model training standards
- Output validation, safety controls, and human oversight
- Intellectual property protection
- Bias mitigation and privacy protection
Review Cadence
- Combination of technical, legal and responsible AI review
- Conduct regular reviews (monthly, quarterly, or annually)
- Involve technical teams, legal and compliance stakeholders, SMEs, and end-users
Review Strategies
- Technical Reviews: model performance, data quality, robustness, and reliability
- Non-Technical Reviews: alignment with policies, responsible AI principles, and regulations
- Validate and test outputs before deploying new or updated models
- Clear decision-making framework to make decisions based on review results
Transparency Standards
- Clearly document AI model capabilities, limitations, and intended use cases
- Publish information about training data and key design decisions where appropriate
- Provide mechanisms for user feedback and issue reporting
Team Training and Enablement
- Train teams on AI governance policies and best practices
- Educate on responsible AI, bias mitigation, and ethical considerations
- Encourage cross-functional collaboration and knowledge sharing
- Implement ongoing training and certification programs

Responsible AI Practices
- Adopt responsible AI frameworks addressing fairness, transparency, accountability, and bias
- Continuously monitor AI and GenAI systems for unintended outcomes
- Provide regular education and awareness programs for teams
Governance Structure and Roles
- Establish a data governance council or committee
- Define roles and responsibilities for data owners, data stewards, and data custodians
- Provide training and support to AI and ML practitioners
Data Sharing and Collaboration
- Use formal data-sharing agreements to ensure secure internal data access
- Data virtualization or federation to give access to data without compromising ownership
- Promote a culture of data-driven decision-making and collaborative governance

Data Lifecycle: collection, processing, storage, consumption, and archival
Data Logging: capture inputs, outputs, performance metrics, and system events
Data Residency: manage where data is stored and processed to meet regulatory and privacy requirements
Data Monitoring: ensure data quality, detect anomalies, and identify data drift
Data Analysis: apply statistical methods and visualizations for insights
Data Retention: balance regulatory obligations, historical training needs, and storage costs

Data lineage enhances transparency, traceability, and accountability across AI systems.
Source Citation:
- Attributing and acknowledging the sources of the data
- What are the datasets, databases and other sources we are using
- What are relevant licenses, terms of use and other permissions
Document Data Origin:
- Details of the collection process
- Methods used to clean and curate the data
- Pre-processing and transformation to the data
Data Cataloging: organization and documentation of datasets
It is helpful to have data lineage for data transparency, traceability and accountability