---
_id: '14114'
abstract:
- lang: eng
  text: Algorithmic fairness is frequently motivated in terms of a trade-off in which
    overall performance is decreased so as to improve performance on disadvantaged
    groups where the algorithm would otherwise be less accurate. Contrary to this,
    we find that applying existing fairness approaches to computer vision improve
    fairness by degrading the performance of classifiers across all groups (with increased
    degradation on the best performing groups). Extending the bias-variance decomposition
    for classification to fairness, we theoretically explain why the majority of fairness
    methods designed for low capacity models should not be used in settings involving
    high-capacity models, a scenario common to computer vision. We corroborate this
    analysis with extensive experimental support that shows that many of the fairness
    heuristics used in computer vision also degrade performance on the most disadvantaged
    groups. Building on these insights, we propose an adaptive augmentation strategy
    that, uniquely, of all methods tested, improves performance for the disadvantaged
    groups.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dominik
  full_name: Zietlow, Dominik
  last_name: Zietlow
- first_name: Michael
  full_name: Lohaus, Michael
  last_name: Lohaus
- first_name: Guha
  full_name: Balakrishnan, Guha
  last_name: Balakrishnan
- first_name: Matthaus
  full_name: Kleindessner, Matthaus
  last_name: Kleindessner
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
- first_name: Bernhard
  full_name: Scholkopf, Bernhard
  last_name: Scholkopf
- first_name: Chris
  full_name: Russell, Chris
  last_name: Russell
citation:
  ama: 'Zietlow D, Lohaus M, Balakrishnan G, et al. Leveling down in computer vision:
    Pareto inefficiencies in fair deep classifiers. In: <i>2022 IEEE/CVF Conference
    on Computer Vision and Pattern Recognition</i>. Institute of Electrical and Electronics
    Engineers; 2022:10400-10411. doi:<a href="https://doi.org/10.1109/cvpr52688.2022.01016">10.1109/cvpr52688.2022.01016</a>'
  apa: 'Zietlow, D., Lohaus, M., Balakrishnan, G., Kleindessner, M., Locatello, F.,
    Scholkopf, B., &#38; Russell, C. (2022). Leveling down in computer vision: Pareto
    inefficiencies in fair deep classifiers. In <i>2022 IEEE/CVF Conference on Computer
    Vision and Pattern Recognition</i> (pp. 10400–10411). New Orleans, LA, United
    States: Institute of Electrical and Electronics Engineers. <a href="https://doi.org/10.1109/cvpr52688.2022.01016">https://doi.org/10.1109/cvpr52688.2022.01016</a>'
  chicago: 'Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner,
    Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in
    Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.” In <i>2022 IEEE/CVF
    Conference on Computer Vision and Pattern Recognition</i>, 10400–411. Institute
    of Electrical and Electronics Engineers, 2022. <a href="https://doi.org/10.1109/cvpr52688.2022.01016">https://doi.org/10.1109/cvpr52688.2022.01016</a>.'
  ieee: 'D. Zietlow <i>et al.</i>, “Leveling down in computer vision: Pareto inefficiencies
    in fair deep classifiers,” in <i>2022 IEEE/CVF Conference on Computer Vision and
    Pattern Recognition</i>, New Orleans, LA, United States, 2022, pp. 10400–10411.'
  ista: 'Zietlow D, Lohaus M, Balakrishnan G, Kleindessner M, Locatello F, Scholkopf
    B, Russell C. 2022. Leveling down in computer vision: Pareto inefficiencies in
    fair deep classifiers. 2022 IEEE/CVF Conference on Computer Vision and Pattern
    Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 10400–10411.'
  mla: 'Zietlow, Dominik, et al. “Leveling down in Computer Vision: Pareto Inefficiencies
    in Fair Deep Classifiers.” <i>2022 IEEE/CVF Conference on Computer Vision and
    Pattern Recognition</i>, Institute of Electrical and Electronics Engineers, 2022,
    pp. 10400–11, doi:<a href="https://doi.org/10.1109/cvpr52688.2022.01016">10.1109/cvpr52688.2022.01016</a>.'
  short: D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B.
    Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern
    Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.
conference:
  end_date: 2022-06-24
  location: New Orleans, LA, United States
  name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
  start_date: 2022-06-18
date_created: 2023-08-21T12:18:00Z
date_published: 2022-07-01T00:00:00Z
date_updated: 2023-09-11T09:19:14Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/cvpr52688.2022.01016
extern: '1'
external_id:
  arxiv:
  - '2203.04913'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2203.04913
month: '07'
oa: 1
oa_version: Preprint
page: 10400-10411
publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
  eissn:
  - 2575-7075
  isbn:
  - '9781665469470'
  issn:
  - 1063-6919
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2022'
...