Enhanced PDF reports
Minimize regulatory and compliance risks
Expanded bias detection
Seamless external data integration
The challenge
Ensuring AI fairness: Balancing innovation with regulatory compliance
After deploying machine learning models, a leading financial institution encountered a critical challenge: ensuring compliance with government-mandated fairness standards, particularly for protected classes such as gender and race. Maintaining this balance between innovation and regulatory compliance became a top priority.
Key challenges
The institution required an innovative, regulation-compliant ML solution
A robust pre-deployment system ensured compliance by detecting biases and mitigating risks
The solution
Precision-engineered AI evaluation: A tailored solution for compliance and performance
Custom AI evaluation tool
Seamless data input
Protected class selection
Comprehensive reporting
Customizable model outputs
Data-driven decision making
Uncover bias
Enhance compliance
Improve model performance
Implementation approach
1
Secure and scalable architecture
Backend and API
Data management
Frontend and API
Security and deployment
2
Responsible AI framework
Bias detection
Regulatory compliance
Trust and explainability
3
Scalable and future-ready
Flexible and adaptable
Enterprise-grade security
Optimized for efficiency
The impact
Clear fairness and bias insights
Enhanced fairness insights
Detailed graphs and plots for protected class analysis
Insights into internal dataset fairness
Expanded bias assessment
Expanded bias evaluation from 5 to 15 protected classes
More comprehensive fairness assessment
Enhanced model transparency and inclusivity
Looking ahead
External data integration
Broadening analysis to include both internal and external datasets
Automated bias mitigation
AI-powered real-time bias detection and correction
Customizable reporting and compliance monitoring
Tailor-made reports with real-time compliance tracking
