Stephen Pfohl
@stephenpfohl.bsky.social
Research scientist at Google. Previously Stanford Biomedical Informatics. Researching #fairness #equity #robustness #transparency #causality #healthcare
Excited to share that our paper, “Understanding challenges to the interpretation of evaluations of algorithmic fairness” has been accepted to NeurIPS 2025! You can read the paper now on arXiv: arxiv.org/abs/2506.04193.
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Disaggregated evaluation across subgroups is critical for assessing the fairness of machine learning models, but its uncritical use can mislead practitioners. We show that equal performance across sub...
arxiv.org
October 28, 2025 at 12:36 AM
Excited to share that our paper, “Understanding challenges to the interpretation of evaluations of algorithmic fairness” has been accepted to NeurIPS 2025! You can read the paper now on arXiv: arxiv.org/abs/2506.04193.
Reposted by Stephen Pfohl
Causal inference iceberg!
What's missing?
What's missing?
February 26, 2025 at 1:40 PM
Causal inference iceberg!
What's missing?
What's missing?
Check out our new paper "Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations" jointly published in NEJM AI and The Lancet Digital Health, led by @jaldmn.bsky.social @xiaoliu.bsky.social
December 18, 2024 at 6:51 PM
Check out our new paper "Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations" jointly published in NEJM AI and The Lancet Digital Health, led by @jaldmn.bsky.social @xiaoliu.bsky.social