text generation

Mitigating Bias in Predictions from Machine Learning Models

Ricardo Henao Associate Professor, Bioengineering

Oct 9, 11:30 - 12:30

B9 L2 H2 H2

machine learning risk prediction text generation

The increasing popularity of machine learning models in real-world automated and decision support systems has underscored the need for assessing and then mitigating biases that may manifest, often spuriously, in their predictions either at the population, sub-population, or individual level. These biases can be assessed in terms of calibration, performance stratification, fairness metrics, prediction interval coverages, etc., and are mainly due to poor model specification (e.g., overparameterization without regularization or loss/likelihood mismatch) or data collection issues (e.g., population misrepresentation or unmeasured confounders).