Responsible AI Compass (RAIC): Set of key questions they orgs should consider to ensure responsible AI usage.

[Neetuj]

Purpose

• Ethical AI Use: Maintain trust and integrity in financial services.
• Risk Mitigation: Address bias, privacy, and security concerns.
• Regulatory Compliance: Align with financial regulations.
• Innovation: Encourage responsible AI-driven innovation.

Objectives

1. Fairness: Prevent discrimination and ensure equitable access with trustworthy explainability.
2. Transparency: Provide clear explanations for AI decisions and gain customer trust
3. Privacy: Protect sensitive financial data and have customer's voice/preferences in mind.
4. Accountability: Establish clear responsibility for AI outcomes.
5. Compliance: Meet regulatory standards like GDPR and AML, EU AI Act etc .
6. Workforce Adaptation: Train employees for responsible AI integration.

Requirements

1. Ethical Guidelines: Tailored to financial sector needs.
2. Model Standards: Ensure accuracy and reliability for responsible use.
3. Risk Framework: Assess and mitigate AI-related risks focused on ethical and responsible use.
4. Stakeholder Engagement: personas defined for customers, regulators, and employees etc and clear guidance on how need to be aware of what.
5. Evaluation Metrics: Track AI impact (and drift from from intended use).
6. Training Programs: Equip staff with necessary AI skills to understand and ensure responsible AI use.

[Neetuj]

Charting a rough roadmap towards the goal

Research and Benchmarking:

Gather information on existing frameworks and best practices for responsible AI.
Identify key ethical, legal, and technical considerations relevant to your organization.
Stakeholder Identification and Engagement:

Identify key personas and stakeholders involved in AI development and deployment.
Engage with these stakeholders to understand their perspectives and needs.
Framework Development:

Define the core principles and objectives of the RAIC guide.
Develop a structured outline, including key sections and questions for each persona.
Content Creation:

Draft detailed questions and guidelines for each persona.
Ensure the content is clear, actionable, and aligned with organizational goals.
Review and Validation:

Conduct workshops or review sessions with stakeholders to gather feedback.
Refine the guide based on input and ensure it addresses all relevant concerns.
Implementation Planning:

Develop a plan for rolling out the RAIC guide within the organization.
Identify resources, tools, and training needed to support implementation.
Communication and Training:

Create communication materials to introduce the RAIC guide to the organization.
Develop training programs to educate stakeholders on using the guide effectively.
Monitoring and Evaluation:

Establish metrics and processes for evaluating the effectiveness of the RAIC guide.
Set up a feedback loop for continuous improvement and updates to the guide.
Documentation and Accessibility:

Ensure the guide is well-documented and easily accessible to all relevant parties.
Consider creating a digital platform or repository for the guide and related resources.
Continuous Improvement:

Regularly review and update the guide to reflect new insights, technologies, and regulations.
Foster a culture of learning and adaptation to ensure ongoing responsible AI adoption.

[lucaborella89]

@Neetuj finos/ai-governance-framework#93 fyi