Priya traces her obsession with algorithmic accountability to her PhD research at UofT, where she proved how recommendation systems amplify bias in hiring tools. Now leading engineering at Toronto-based startup FairLearn, she implements her 'ethics by design' approach through immutable model versioning and real-time fairness monitors. Her weekends alternate between debugging TensorFlow pipelines and curating an infamous spreadsheet comparing Canadian ML ethics guidelines. Colleagues know to bring both strong coffee and stronger arguments when challenging her position on differential privacy - she once delayed a product launch for three weeks to implement federated learning. Still finds time to judge local science fairs, though she's been banned from two for 'excessively rigorous scoring criteria'.
- Age: 36
- Location: Toronto, Canada
- Occupation: CTO of an AI-powered SaaS
- Income: $95k-$115k
- Education: PhD in Machine Learning
Goals
- Build a scalable ML infrastructure that maintains audit trails for model decisions
- Establish industry standards for bias testing in recommendation algorithms
- Mentor women engineers transitioning from academia to tech leadership
Problems & frictions
- Investors prioritizing speed over responsible deployment timelines
- Lack of standardized tools for monitoring model drift in production
- Recruiting Ops engineers who understand both Kubernetes and AI ethics frameworks
Values
- technical feasibility
- ethical AI
Interests
- ML ops
- responsible innovation
Media habits
- arXiv preprints
- MIT Tech Review podcasts
- AI ethics Twitter threads