Fairness Indicators for Systematic Assessments of Visual Feature
Extractors
- URL: http://arxiv.org/abs/2202.07603v1
- Date: Tue, 15 Feb 2022 17:45:33 GMT
- Title: Fairness Indicators for Systematic Assessments of Visual Feature
Extractors
- Authors: Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun,
Nicolas Usunier
- Abstract summary: We propose three fairness indicators, which aim at quantifying harms and biases of visual systems.
Our indicators use existing publicly available datasets collected for fairness evaluations.
These indicators are not intended to be a substitute for a thorough analysis of the broader impact of the new computer vision technologies.
- Score: 21.141633753573764
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Does everyone equally benefit from computer vision systems? Answers to this
question become more and more important as computer vision systems are deployed
at large scale, and can spark major concerns when they exhibit vast performance
discrepancies between people from various demographic and social backgrounds.
Systematic diagnosis of fairness, harms, and biases of computer vision systems
is an important step towards building socially responsible systems. To initiate
an effort towards standardized fairness audits, we propose three fairness
indicators, which aim at quantifying harms and biases of visual systems. Our
indicators use existing publicly available datasets collected for fairness
evaluations, and focus on three main types of harms and bias identified in the
literature, namely harmful label associations, disparity in learned
representations of social and demographic traits, and biased performance on
geographically diverse images from across the world.We define precise
experimental protocols applicable to a wide range of computer vision models.
These indicators are part of an ever-evolving suite of fairness probes and are
not intended to be a substitute for a thorough analysis of the broader impact
of the new computer vision technologies. Yet, we believe it is a necessary
first step towards (1) facilitating the widespread adoption and mandate of the
fairness assessments in computer vision research, and (2) tracking progress
towards building socially responsible models. To study the practical
effectiveness and broad applicability of our proposed indicators to any visual
system, we apply them to off-the-shelf models built using widely adopted model
training paradigms which vary in their ability to whether they can predict
labels on a given image or only produce the embeddings. We also systematically
study the effect of data domain and model size.
Related papers
- Synthetic Counterfactual Faces [9.132161819463043]
We build a generative AI framework to construct targeted, counterfactual, high-quality synthetic face data.
Our pipeline has many use cases, including face recognition systems sensitivity evaluations and image understanding system probes.
We showcase the efficacy of our face generation pipeline on a leading commercial vision model.
arXiv Detail & Related papers (2024-07-18T22:22:49Z) - Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes [72.13373216644021]
We study the societal impact of machine learning by considering the collection of models that are deployed in a given context.
We find deployed machine learning is prone to systemic failure, meaning some users are exclusively misclassified by all models available.
These examples demonstrate ecosystem-level analysis has unique strengths for characterizing the societal impact of machine learning.
arXiv Detail & Related papers (2023-07-12T01:11:52Z) - Fairness meets Cross-Domain Learning: a new perspective on Models and
Metrics [80.07271410743806]
We study the relationship between cross-domain learning (CD) and model fairness.
We introduce a benchmark on face and medical images spanning several demographic groups as well as classification and localization tasks.
Our study covers 14 CD approaches alongside three state-of-the-art fairness algorithms and shows how the former can outperform the latter.
arXiv Detail & Related papers (2023-03-25T09:34:05Z) - Stable Bias: Analyzing Societal Representations in Diffusion Models [72.27121528451528]
We propose a new method for exploring the social biases in Text-to-Image (TTI) systems.
Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts.
We leverage this method to analyze images generated by 3 popular TTI systems and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents.
arXiv Detail & Related papers (2023-03-20T19:32:49Z) - Towards Reliable Assessments of Demographic Disparities in Multi-Label
Image Classifiers [11.973749734226852]
We consider multi-label image classification and, specifically, object categorization tasks.
Design choices and trade-offs for measurement involve more nuance than discussed in prior computer vision literature.
We identify several design choices that look merely like implementation details but significantly impact the conclusions of assessments.
arXiv Detail & Related papers (2023-02-16T20:34:54Z) - Causal Reasoning Meets Visual Representation Learning: A Prospective
Study [117.08431221482638]
Lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models.
Inspired by the strong inference ability of human-level agents, recent years have witnessed great effort in developing causal reasoning paradigms.
This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods.
arXiv Detail & Related papers (2022-04-26T02:22:28Z) - Towards a Fairness-Aware Scoring System for Algorithmic Decision-Making [35.21763166288736]
We propose a general framework to create data-driven fairness-aware scoring systems.
We show that the proposed framework provides practitioners or policymakers great flexibility to select their desired fairness requirements.
arXiv Detail & Related papers (2021-09-21T09:46:35Z) - Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units
and a Unified Framework [83.21732533130846]
The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2.
It presents the design of two classes of deep neural networks trained with these databases.
A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
arXiv Detail & Related papers (2021-03-29T17:36:20Z) - Through the Data Management Lens: Experimental Analysis and Evaluation
of Fair Classification [75.49600684537117]
Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness.
We contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, and stability.
Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance.
arXiv Detail & Related papers (2021-01-18T22:55:40Z) - Gender Slopes: Counterfactual Fairness for Computer Vision Models by
Attribute Manipulation [4.784524967912113]
Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices.
Recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups.
We propose to use an encoder-decoder network developed for image manipulation to synthesize facial images varying in the dimensions of gender and race.
arXiv Detail & Related papers (2020-05-21T02:33:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.