ICML 2025

Multiaccuracy and Multicalibration via Proxy Groups
Beepul Bharti 1 2 Mary Versa Clemens-Sewall 3 Paul Yi 4 Jeremias Sulam 1 2 5
Abstract
As the use of predictive machine learning algo-
rithms increases in high-stakes decision-making,
it is imperative that these algorithms are fair
across sensitive groups. However, measuring and
enforcing fairness in real-world applications can
be challenging due to missing or incomplete sensi-
tive group information. Proxy-sensitive attributes
have been proposed as a practical and effective so-
lution in these settings, but only for parity-based
fairness notions. Knowing how to evaluate and
control for fairness with missing sensitive group
data for newer, different, and more flexible frame-
works, such as multiaccuracy and multicalibra-
tion, remains unexplored. In this work, we ad-
dress this gap by demonstrating that in the ab-
sence of sensitive group data, proxy-sensitive at-
tributes can provably be used to derive actionable
upper bounds on the true multiaccuracy and mul-
ticalibration violations, providing insights into a
predictive model’s potential worst-case fairness
violations. Additionally, we show that adjusting
models to satisfy multiaccuracy and multicalibra-
tion across proxy-sensitive attributes can signifi-
cantly mitigate these violations for the true, but
unknown, sensitive groups. Through several ex-
periments on real-world datasets, we illustrate that
approximate multiaccuracy and multicalibration
can be achieved even when sensitive group data
is incomplete or unavailable.
1Department of Biomedical Engineering, Johns Hopkins Uni-
versity, Baltimore, USA 2Mathematical Institute of Data Science,
Johns Hopkins University, Baltimore, USA 3Department of Ap-
plied Mathematics & Statistics, Johns Hopkins University, Bal-
timore, USA 4St. Jude Children’s Research Hospital, Arlington,
USA 5Department of Computer Science, Johns Hopkins Univer-
sity, Baltimore, USA. Correspondence to: Beepul Bharti <bb-
harti1@jhu.edu>, J...