Fairness and bias in health data science

Date: Thursday, 24th July 2025
Time: 14:00 – 15:30 (Part 1)
Time: 16:00 – 17:30 (Part 2)

Mission and objectives


The aim of this tutorial is to cover the basic principles of fairness and bias in health data science and highlight the ways in which bias can arise throughout the research pipeline. This will be an interactive session with a substantial amount of group discussion. Participants will be encouraged to contribute perspectives drawn from their own work so that learnings can be shared across disciplines.

Intended Audience


This tutorial is suitable for participants with some exposure to data science / statistical analysis. Deep technical skills are not required.

Expected Outcomes


After this tutorial, participants will be able to:

  • Recognize how bias can emerge at various stages of a healthcare data analysis pipeline.
  • Understand the consequences of biased datasets, particularly in contexts such as genetic data or underreported medical conditions in health data science.
  • Gain a general understanding of methods for assessing algorithmic fairness.

Format and Schedule


TimeActivitySession
14:00 – 14:10Ice-breaker
14:10 – 14:401What’s ‘fair’ anyway? An introduction to bias and fairness in health data science
14:40 – 15:102Case studies: Fairness in the real world
15:10 – 15:302Case study presentations
Break
16:00 – 16:203Where bias creeps in: A tour of the data analysis pipeline
16:20 – 16:504Breakout groups: Designing solutions for fairness across the pipeline
16:50 – 17:054Lightning pitches
17:05 – 17:305Looking ahead: the future of fairness in health data science (Rotating panel discussion)

SPEAKER