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Stephen Pfohl

@stephenpfohl.bsky.social
801 followers 1.2K following 18 posts

Research scientist at Google. Previously Stanford Biomedical Informatics. Researching #fairness #equity #robustness #transparency #causality #healthcare

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Stephen Pfohl @stephenpfohl.bsky.social · 12/04/2026
The most ignored instructions for ML conference review has got to be the "Please use sparingly" designation for weak accept/reject recommendations
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Stephen Pfohl @stephenpfohl.bsky.social · 28/10/2025
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.
arxiv.org
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...
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Reposted by Stephen Pfohl
Julia M. Rohrer @dingdingpeng.the100.ci · 26/02/2025
Causal inference iceberg! What's missing?
Ice berg meme template. From top to bottom:
Correlation does not imply causation
Third variable adjustment
Just run an experiment
Confounding
Collider bias
Selective mortality
Observational longitudinal data
Mediation analysis is messed up
Posttreatment bias in experiments
Generalization as a causal inference problem
Missing data as a causal inference problem
Measurement as a causal inference problem
Causal foundations of applied probability and statistics
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Stephen Pfohl @stephenpfohl.bsky.social · 18/12/2024
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
ai.nejm.org
Tackling Algorithmic Bias and Promoting Transparency in Health Datasets: The STANDING Together Consensus Recommendations
Without careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. O...
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