Visual insights
Comprehensive bias checks
Reporting method
The challenge
Ensuring fairness and compliance in AI innovation
After integrating machine learning models into their operations, our client, a financial institution wanted to ensure that these models adhered to government-mandated fairness regulations. Compliance was particularly essential for protected classes such as gender and race. Balancing technological innovation and stringent regulatory requirements became paramount.
Key challenges
Building a system to test models’ pre-deployment and prevent compliance risks
Ensuring AI innovation aligns with strict fairness regulations in machine learning
The solution
Secure and scalable AI framework
AI evaluation tool
Tool development and code refinement
User-friendly, tailored design tool
Bias metrics reports with visuals
Secure and scalable tech
Secure internal data handling
Strong backend and UI
Ensure RAI principles
Implementation approach
1
Fairness and bias testing
Predefined compliance rules
Fairness metrics
Automated bias checks
2
Performance and optimization
Comprehensive UI annotations
Expert-driven insights
Refined code
3
Security and compliance
Secure model deployment
Continuous monitoring
Strong data security
The impact
Ensuring fair and transparent AI
Advanced reporting
Fairness insights
Enhanced transparency
AI-driven bias reports
Bias detection
Protected classes
Analysis done
Compliance support
Data handling
Bias checks across datasets
Consistent fairness
Robust evaluation
Looking ahead
Continuous improvement
Ongoing enhancements to AI fairness and compliance
Scalable integration
Expanding AI evaluation across more use cases
Proactive monitoring
Real-time output tracking to uphold ethical AI standards
