Work

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

Year: 2022

Type: conference-paper

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.

Source: ACM Conference on Fairness, Accountability, and Transparency (FAccT)

DOI: 10.1145/3531146.3533074

Authors: Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun, Nicolas Usunier

Institution: Meta (United States)

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: green

OpenAlex ID: W4283170167

FWCI: 2.349

Cites: 40

Cited by: 24

Related to: 10