We hope our work will be of use to practitioners who seek to improve and reform traffic enforcement systems, and support ongoing efforts to improve equity broadly.
Full paper: https://t.co/3DkeElkv7j
Analysis code: https://t.co/kKFXP8yVJk
Our new paper in PNAS Nexus finds that, while drivers themselves speed as much in white neighborhoods as non-white neighborhoods, speeding enforcement varies a great deal.
Paper (https://t.co/3DkeElkv7j) + thread:
w/@jgaeb1, Justin Kaashoek, Lisa Pinals, @samrmadden, and @5harad
We hope our work will be of use to practitioners constructing datasets to train models, and broadly support ongoing efforts to make statistical models more equitable.
Paper! with @rosemariesays , Bobbie Chern, @scorbettdavies , @mbogen , @tvr2c , @5harad. We propose a strategy to construct more equitable datasets for training ML models.
Paper: https://t.co/lz9sfGwXKQ
Thread: 1/
We show that in both settings, our adaptive sampling strategy obtains near-optimal policies, while allowing model-builders to efficiently prioritize traditionally underserved groups and avoid unintended consequences of heuristics such as representative and equal sampling.