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Research & Ethics

Responsible AI Development Framework

Authored a bias mitigation framework adopted by the university research board, ensuring 100% compliance with new data privacy regulations.

AI Ethics Fairness Scale and Neural Network visual representation

The Issue

As AI models are increasingly deployed in sensitive areas (hiring, lending), the risk of algorithmic bias has grown. The institution lacked a standardized protocol for auditing datasets and models for fairness.

The Solution

I developed a comprehensive "Responsible AI" checklist and technical auditing toolkit.

  • integrated Python libraries like Fairlearn and AIF360 into the development workflow.
  • Drafted policy documents outlining data lineage requirements.
  • Created a "Model Card" template for transparent documentation of model limitations.

The Impact

100%Compliance
AdoptedBy Board
RiskMitigated

Tools

Fairlearn AIF360 Python

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