Evidence map›Paper›PMID 38867889›Full record

ArticleAdvances in neural information processing systems2023

Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors.

Beepul Bharti, Paul Yi, Jeremias Sulam

Abstract read
In one paragraph

Article in Advances in neural information processing systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Beepul BhartiJohns Hopkins University.
Paul YiUniversity of Maryland.
Jeremias SulamJohns Hopkins University.

Funding

SCH: Quantifying and mitigating demographic biases of machine learning in real world radiologyR01CA287422 · NCI · JOHNS HOPKINS UNIVERSITY · PI SULAM, JEREMIAS · 2023 to 2025
$889k
NCI NIH HHS R01 CA287422
6 · The paper itself

Abstract

As the use of machine learning models in real world high-stakes decision settings continues to grow, it is highly important that we are able to audit and control for any potential fairness violations these models may exhibit towards certain groups. To do so, one naturally requires access to sensitive attributes, such as demographics, biological sex, or other potentially sensitive features that determine group membership. Unfortunately, in many settings, this information is often unavailable. In this work we study the well

Identifiers

PMID38867889
PMCPMC11167624

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.