Skip to main content
King Abdullah University of Science and Technology
Computer Science
CS
Computer Science
  • Study
    • Prospective Students
    • Current Students
  • Research
    • Research Areas
    • Research Groups
  • People
    • All People
    • Faculty
    • Affiliate Faculty
    • Instructional Faculty
    • Research Scientists
    • Research Staff
    • Postdoctoral Fellows
    • Administrative Staff
    • Alumni
    • Students
  • News
  • Events
  • About
  • CEMSE Division
  • Apply

risk prediction

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).

Computer Science (CS)

Footer

  • A-Z Directory
    • All Content
    • Browse Related Sites
  • Site Management
    • Log in

© 2024 King Abdullah University of Science and Technology. All rights reserved. Privacy Notice