Tutorial 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.
Schedule: TBC
SPEAKERS
Dr Brieuc Lehmann
Dr Brieuc Lehmann is an Assistant Professor in Statistical Science at University College London (UCL). His research focuses on developing statistical methods to address underrepresentation in biomedical datasets, ensuring they reflect diverse populations and promote health equity. Brieuc collaborates with interdisciplinary teams across academia, industry, and public health organizations, with a particular focus on genomics and clinical trials. Brieuc is also a co-founder of Data Science for Health Equity, an interdisciplinary community-of-practice working to ensure that the latest data-driven innovations improve
everyone’s health.