BiGHT - Course Overview

Building intelligent Global Health and Humanitarian Response Technologies (BiGHT)

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Summary

CS-467 Course Curriculum - Fall 2026 - EPFL IC

Summary

This course teaches the principles and practice of designing, building, and rigorously evaluating trustworthy, representative, and contextually appropriate AI systems for high-stakes decision making in global health and humanitarian response.

Lecturers: Prof. Mary-Anne Hartley, Prof. Kristina Keitel, Dr. Lars Klein, Dr. David Sasu, Tim Arni, Yusuf Kesmen, Fabrice Nemo

Coordinator: Fabrice Nemo

TAs: Yusuf Kesmen, Jérémy Baffou, Xavier Theimer-Lienhard

Course Overview

The course is organized into three thematic blocks. Each teaching week pairs one context lecture with one engineering lecture.

Block	Weeks	Goal
Volatile Contexts	1-4	Understand the environments in which AI must operate, and why technology often fails in humanitarian and clinical settings.
High-stakes Decisions	5, 6, 8	Understand how clinicians make decisions under uncertainty and how AI can safely support human expertise.
Trustworthy Evidence	7, 10-13	Design, evaluate, and deploy AI systems that are safe, effective, and ready for real-world use.

Week	Date	Theme	Context lecture	Engineering lecture
1	Sep 9	Inequitable Inaccuracy	Tutti Fratelli: The Principles That Created a Movement	Course Logistics
2	Sep 16	Deciding Under Fire	Deciding Under Fire: Triage, Uncertainty & Care	Building for Broken Environments
3	Sep 23	The Last Mile	Care at the Fringe: Community Medicine	The Last Mile: Getting Intelligence to the Edge
4	Sep 30	Building at the Speed of Infection	Ebola: Engineering Trust	Listening to the World: Epidemic Intelligence at Scale
5	Oct 7	Models Without Patients	The Machine That Was Right and Was Never Used	From Technological Implementation to Clinical Use
6	Oct 14	Reasoning Against Ourselves	The Confident Fool: How Expertise Breeds Error	Debiasing the Machine
7	Oct 28	The Fragility of Accuracy	How We Learned to Count What We Were Losing	When 99% Accuracy Is Dangerous
8	Nov 4	Advice at the Wrong Time	Why Doctors Ignore Good Advice	Integration Without Interruption
9	Nov 11	Midterm Presentations	Midterm presentations	Midterm presentations
10	Nov 18	Causal Evidence in Practice	The Man Who Asked Medicine to Prove Itself	Causal Inference: Beyond Correlation
11	Nov 25	Designing for Failure	The Holes in the Cheese	Uncertainty as Architecture
12	Dec 2	Responsibility in Clinical AI	First, Do No Harm - Then, Prove It	Governing What You Cannot Fully Understand
13	Dec 9	Systems That Reach People	Why Good Ideas Die at the Door	Shipping to Scale: The MLOps of Global Health AI
14	Dec 16	Final Presentations	Final Presentations	Final Presentations

Thematic Blocks

Block I: Volatile Contexts (Weeks 1-4)

Goal: Understand the environments in which AI must operate, and why technology often fails in humanitarian and clinical settings.

Context themes

Humanitarian systems
Community medicine
Global health
Epidemics and outbreaks

Engineering themes

Participatory design
ML infrastructure
Federated learning
Privacy
Reproducibility
Edge computing

Block II: High-stakes Decisions (Weeks 5, 6, 8)

Goal: Understand how clinicians make decisions under uncertainty and how AI can safely support human expertise.

Context themes

Clinical reasoning
Diagnosis and triage
Evidence-based medicine
Clinical workflows

Engineering themes

Data analysis
Bayesian reasoning
Causal inference
RAG
Clinical decision support

Block III: Trustworthy Evidence (Weeks 7, 10-13)

Goal: Design, evaluate, and deploy AI systems that are safe, effective, and ready for real-world use.

Context themes

Clinical trials
Implementation science
Regulation and governance
Health systems

Engineering themes

Validation
Benchmarking
Safety
Deployment
Monitoring
Technical communication

Organization

Item	Description
Weekly workload	2h lectures, 1h project studio, ~12h project development
Presentations	Midterm and final project presentations
Quizzes	6 in-class quizzes, about 30 minutes each
Field trip	Optional fully funded field trip to the ICRC museum, date TBD

Weekly format

Session	Purpose
Context lecture	What is the context of the problem we are solving?
Engineering lecture	Translate context concepts into engineering principles that address them
Studio	Ask TAs if you need help for your project

In-class Quizz Schedule

Quizz #	Time
1	Week 3: September 23, 13.15
2	Week 5: October 7, 13.15
3	Week 7: October 28, 13.15
4	Week 9: November 11, 13.15
5	Week 11: November 25, 13.15
6	Week 13: December 9, 13.15

Assessment

Component	Weight
Milestone 1: Technical Design and Reproducible Repository	10%
Milestone 2: Proof Of Concept	20%
Midterm oral presentation	10%
Final presentation with a live demo	10%
Project report and code	30%
In-class tests and quizzes (top 5 of the 6 quizzes)	20%