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Case Studies

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Charting the path to responsible innovation

Charting the path to responsible innovation

Charting the path to responsible innovation

How a leading financial institution ensured AI fairness with a tailored solution

How a leading financial institution ensured AI fairness with a tailored solution

Enhanced visual insights

Comprehensive bias checks

Improved reporting

The challenge

Ensuring fairness and compliance in AI innovation

After integrating machine learning models into their operations, our clienta leading 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

9 months for tool development, 2 months for 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 via graphs and plots

  • Enhanced transparency

  • AI-driven bias reports

Bias detection

  • 15 protected classes

  • Deeper analysis

  • Stronger compliance

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