BIODS 240: Race, Data, Algorithms, and Health

Autumn 2020

About the Course

Taught by Drs. Chiara Sabatti and James Zou, this course studies the interplay between race, data and algorithms in healthcare. The particular viewpoint we want to take is to understand the role of data, data analysis and algorithms in supporting equitable delivery of health care to members of all races. Topics as "representative data", "machine bias", "algorithmic fairness" are going to be central to the discussion. However, we want to stress the uniqueness of the "medicine/health care" viewpoint. For example, while in contexts as loan applications, it is normative that race information (or its proxies) not to be included among the variables used for decision, in healthcare, information on race is routinely collected in the attempt to provide "best" care. One of the goals of the class will be to understand what are the differences between biological populations and social environments that a doctor needs to be aware of as opposed to overly emphasized or imaginary ones. This will provide a context to understand the challenges that data collection and analysis faces to support equitable care.

Course Calendar & Lecture Recordings

Links for course lectures will be posted as soon as they are available. Speakers may opt out of sharing their lecture recordings; in that case, recordings will be listed below as "Unavailable." 

Date Speaker Talk Title Recording
9/17 Carlos D. Bustamante The Problem: Precision Health at Scale for All Access here
9/24 Barbara Koenig Race in a Genomic Age: The Ethical Dimensions of a Molecular Understanding of Human Difference Access here
10/1 David Rehkopf Understanding the landscape of disparities across time and place Access here
10/8 David Rehkopf How to study race as a social construct Access here
10/15 Chiara Sabatti Historical perspective & eugenics Unavailable
10/22 Dan Ho The modern equal protection doctrine: law and regulations and their impact on healthcare across races Unavailable
10/29 James Zou Observational data; reflecting a racist status quo or true differences?  
11/5 Serena Yeung Image analysis across races in medicine  
11/12 Chiara Sabatti & James Zou What objective function should we specify for an algorithm to be fair?  
11/19 Deendayal Dinakarpandian Am I the Problem or the Solution?  A Personal Charter for Wiser Informatics  

DBDS on Diversity

We are committed to our historical and ongoing mission to use biomedical data science to improve human health. A cornerstone of this mission is diversity, reflected in embracing a breadth of complementary research interests, research styles, and a diverse and inclusive community. DBDS recognizes that we have significant work to do in shaping our future as we work towards achieving justice, equity, diversity and inclusion throughout our work and operations, our research and activities, and our professional relationships and partnerships.

Stanford's Land Acknowledgment Statement

Stanford sits on the ancestral land of the Muwekma Ohlone Tribe. This land was and continues to be of great importance to the Ohlone people. Consistent with our values of community and inclusion, we have a responsibility to acknowledge, honor, and make visible the University’s relationship to Native peoples.

This acknowledgment has been developed in collaboration with the Muwekma Ohlone Tribe.