Responsible AI

Last Updated: December 2024

RiskAI embeds governance-by-design to help financial institutions deploy AI that is safe, explainable, and compliant. Our approach aligns with international standards and regulatory frameworks to ensure AI systems are developed and deployed responsibly.

EU AI Act OECD AI Principles NIST AI RMF ISO 42001 MAS FEAT

1. Our Approach to Responsible AI

RiskAI believes that responsible AI is not just a compliance requirement but a fundamental business imperative. We help organizations build AI governance frameworks that ensure:

AI systems are developed and deployed ethically
Decisions are transparent and explainable
Risks are identified and mitigated proactively
Compliance with regulatory requirements
Stakeholder trust and confidence

2. Core Principles

Human-Centric AI

AI systems should augment human capabilities and decision-making, not replace them. We ensure human oversight and control over AI systems while maintaining human accountability for outcomes.

Transparency & Explainability

AI decisions must be understandable and explainable to stakeholders. Our platform provides clear documentation of AI system behavior, decision logic, and performance metrics.

Fairness & Non-Discrimination

AI systems should not perpetuate or amplify biases. We help organizations implement fairness testing, bias detection, and mitigation strategies to ensure equitable outcomes.

Privacy & Data Protection

AI systems must respect privacy rights and data protection principles. We ensure compliance with GDPR, CCPA, and other privacy regulations throughout the AI lifecycle.

Robustness & Safety

AI systems must be reliable, secure, and safe. We help organizations implement testing, validation, and monitoring to ensure AI systems perform as intended under various conditions.

Accountability

Clear lines of responsibility and accountability must be established for AI systems. We help organizations define roles, responsibilities, and governance structures.

3. Governance Framework

3.1 AI Governance Structure

Our platform helps organizations establish comprehensive AI governance structures including:

AI Ethics Committee and oversight bodies
Clear roles and responsibilities for AI development and deployment
Risk assessment and management processes
Compliance monitoring and reporting mechanisms
Stakeholder engagement and communication strategies

3.2 Risk Management

We implement a systematic approach to AI risk management:

Risk identification and assessment frameworks
Risk mitigation strategies and controls
Ongoing monitoring and evaluation
Incident response and recovery procedures
Regular risk reviews and updates

4. Compliance & Standards

4.1 EU AI Act Compliance

Our platform helps organizations comply with the EU AI Act requirements:

Risk-based classification of AI systems
Transparency and documentation requirements
Human oversight and control mechanisms
Accuracy, robustness, and cybersecurity measures
Conformity assessment and CE marking

4.2 International Standards

We align with key international AI governance standards:

OECD AI Principles: International policy framework for trustworthy AI
NIST AI Risk Management Framework: Comprehensive approach to AI risk management
ISO 42001: AI Management System standard
ISO 23894: Risk management for AI systems

5. Technical Implementation

5.1 Explainable AI (XAI)

Our platform orchestrates with explainable AI (XAI) systems to ensure outcomes are documented, auditable, and supported by transparent decision processes. It also enables real-time monitoring and automated escalations when needed.

Seamless integration with model interpretability and transparency tools
Decision explanation and justification through connected XAI frameworks
Comprehensive audit trails and decision documentation
Real-time performance monitoring with automated alerts and escalation workflows

5.2 Bias Detection & Mitigation

We help organizations identify and address AI bias:

Integration with automated bias detection in training data and models
Fairness metrics and evaluation frameworks
Bias mitigation techniques and strategies

5.3 Robustness & Reliability

Our platform ensures AI system robustness and reliability by assisting with:

Integration with automated adversarial testing and validation
Error handling and graceful degradation
Fallback mechanisms and human oversight triggers

6. Stakeholder Engagement

6.1 Internal Stakeholders

We help organizations engage internal stakeholders:

Executive leadership and board oversight
Risk management and compliance teams
AI development and data science teams
Business units and end users
Legal and ethics teams

6.2 External Stakeholders

Our platform supports external stakeholder engagement:

Regulatory authorities and compliance reporting
Customers and end users
Industry partners and vendors
Civil society and advocacy groups
Academic and research communities

7. Monitoring & Continuous Improvement

7.1 Performance Monitoring

Real-time AI system performance tracking
Key performance indicators (KPIs) and metrics
Anomaly detection and alerting
Regular performance reviews and assessments
Continuous improvement recommendations

7.2 Compliance Monitoring

Automated compliance checking and validation
Regulatory requirement tracking and updates
Compliance reporting and documentation
Audit preparation and support
Policy and procedure updates

9. Our Commitment

RiskAI is committed to advancing responsible AI practices in the financial services industry. We believe that responsible AI is essential for:

Building trust and confidence in AI systems
Ensuring sustainable and ethical AI adoption
Protecting stakeholders and society
Enabling innovation while managing risks
Creating long-term value for organizations

10. Contact Information

For questions about our Responsible AI approach, please contact:

AI Ethics & Governance Team:
Email: info@riskai.tech

Last Updated: December 2024

Our Responsible AI approach is continuously evolving to reflect the latest developments in AI governance, ethics, and regulatory requirements.