Veronika Cheplygina
Machine learning (ML) competitions aim to improve healthcare algorithms, like detecting lung cancer in chest x-rays. Teams worldwide compete to develop such algorithms, driven by prizes or prestige. Competitions encourage innovation but often produce similar algorithms that excel in one accuracy metric but fail with diverse real-world data.
Relying on one accuracy metric is insufficient for measuring algorithm quality, for example for rare patient cases. It leads to many similar algorithms with high environmental cost. Moreover, competition can deter women and minorities from entering or staying in data science.
I propose competitions which look at multiple metrics across different patient subgroups, with applications in chest and cardiovascular disease. Using the latest ML techniques we will develop methods to improve evaluation of algorithm robustness while reducing the carbon footprint. We will also study how competitions affect women and other groups in data science.