Regulated Environments and Workloads
- Some industries require extra level of Governance and Compliance:
- Financial services
- Healthcare
- Aerospace
- Example:
- Reporting regularly to federal agencies
- Regulated outcome: mortgage and credit applications
- If you need to comply with regulatory frameworks (audit, archival, special security requirements…), then you have a regulated workload!
- 💡 E.g. sensitive data usually leads to regulated workloads!
Importance of Governance & Compliance
- Build Trust
- Ensure responsible and trustworthy AI practices
- Foster public trust and confidence in the responsible deployment of AI
- Mitigate risks
- Bias, privacy violations, unintended consequences…
- Protect from potential legal and reputation risks
- Support management, optimization, and scaling in regulated environments
- Establish clear policies, guidelines, and oversight mechanisms
- Ensure AI systems align with legal and regulatory requirements
- AWS Tools for Governance (see AWS Security Services for details):
Governance Framework
- Example approach:
- Establish an AI Governance Board or Committee
- Team should include representatives from various departments, such as legal, compliance, data privacy, and Subject Matter Experts (SMEs) in AI development
- Define Roles and Responsibilities of governance board
- e.g. oversight, policy-making, risk assessment, decision-making processes…
- Implement Policies and Procedures
- They should address the entire AI lifecycle (from data management to model deployment and monitoring)
Governance Strategies
- Policies – principles, guidelines, and responsible AI considerations
- Data management, model training, output validation, safety, and human oversight
- Intellectual property, bias mitigation, and privacy protection
- Review Cadence – combination of technical, legal, and responsible AI review
- Clear timeline: monthly, quarterly, annually…
- Include Subject Matter Experts (SMEs), legal and compliance teams and end-users
- Review Strategies
- Technical reviews on model performance, data quality, algorithm robustness
- Non-technical reviews on policies, responsible AI principles, regulatory requirements
- Testing and validation procedure for outputs before deploying a new model
- Clear decision-making frameworks to make decisions based on review results