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A Step Toward More Inclusive People Annotations for Fairness

Authors:

Candice Schumann,

Vittorio Ferrari,

Caroline PantofaruAuthors Info & Claims

AIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

Pages 916 - 925

https://doi.org/10.1145/3461702.3462594

Published: 30 July 2021 Publication History

Abstract

The Open Images Dataset contains approximately 9 million images and is a widely accepted dataset for computer vision research. As is common practice for large datasets, the annotations are not exhaustive, with bounding boxes and attribute labels for only a subset of the classes in each image. In this paper, we present a new set of annotations on a subset of the Open Images dataset called the MIAP (More Inclusive Annotations for People) subset, containing bounding boxes and attributes for all of the people visible in those images. The attributes and labeling methodology for the MIAP subset were designed to enable research into model fairness. In addition, we analyze the original annotation methodology for the person class and its subclasses, discussing the resulting patterns in order to inform future annotation efforts. By considering both the original and exhaustive annotation sets, researchers can also now study how systematic patterns in training annotations affect modeling.

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Kondratyuk DYu LGu XLezama JHuang JSchindler GHornung RBirodkar VYan JChiu MSomandepalli KAkbari HAlon YCheng YDillon JGupta AHahn MHauth AHendon DMartinez AMinnen DSirotenko MSohn KYang XAdam HYang MEssa IWang HRoss DSeybold BJiang LSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)VideoPoetProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693075(25105-25124)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693075
Fernández Llorca DFrau PParra IIzquierdo RGómez E(2024)Attribute annotation and bias evaluation in visual datasets for autonomous drivingJournal of Big Data10.1186/s40537-024-00976-911:1Online publication date: 27-Sep-2024
https://doi.org/10.1186/s40537-024-00976-9
Zhang YJiang LTurk GYang D(2024)Auditing Gender Presentation Differences in Text-to-Image ModelsProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694710(1-10)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3689904.3694710
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Index Terms

A Step Toward More Inclusive People Annotations for Fairness
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
2. Social and professional topics
  1. User characteristics

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Information & Contributors

Information

Published In

cover image ACM Conferences

AIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

July 2021

1077 pages

ISBN:9781450384735

DOI:10.1145/3461702

Program Chairs:
Marion Fourcade
University of California Berkeley, USA
,
Benjamin Kuipers
University of Michigan, USA
,
Seth Lazar
Australian National University, Australia
,
Deirdre Mulligan
University of California Berkeley, USA

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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New York, NY, United States

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Published: 30 July 2021

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AIES '21: AAAI/ACM Conference on AI, Ethics, and Society

May 19 - 21, 2021

Virtual Event, USA

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Overall Acceptance Rate 61 of 162 submissions, 38%

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https://dl.acm.org/doi/10.5555/3692070.3693075
Fernández Llorca DFrau PParra IIzquierdo RGómez E(2024)Attribute annotation and bias evaluation in visual datasets for autonomous drivingJournal of Big Data10.1186/s40537-024-00976-911:1Online publication date: 27-Sep-2024
https://doi.org/10.1186/s40537-024-00976-9
Zhang YJiang LTurk GYang D(2024)Auditing Gender Presentation Differences in Text-to-Image ModelsProceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization10.1145/3689904.3694710(1-10)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3689904.3694710
Srinivasan HSchumann CSinha AMadras DOlanubi GBeutel ARicco SChen J(2024)Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People ImagesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658940(797-821)Online publication date: 3-Jun-2024
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