Intersectional Group Fairness in Machine Learning

  • The importance of fairness in AI
  • Why AI fairness is even more critical today
  • Why intersectional group fairness is critical to improving AI fairness

ML Models Prone to Error

  • Wavering performance over time, decaying due to changes in use behavior or system errors
  • Data Drift — Production data can change vs training data — performance impacted with shifting data
  • Data Integrity — Data errors are common in ML pipelines. It’s difficult to catch before metrics impact outcomes
  • Fairness/Bias — Models can add or amplify bias. Regulatory and brand risk
  • Transparency — Models are increasingly a blackbox which make them difficult to debug

Intersectional Group Fairness

Model Performance Management

Bias Detection in Modelling

  • Real-time Fairness — Measure fairness on production data as well as on static datasets
  • Integration into Modelling — Receive real-time fairness alerts and address the issue when they matter
  • Built-in Explainability — Understand the impact of features to specific fairness metrics
  • Risk Mitigation — Trackback to any past incident or analyze future predictions to minimize risk

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