We propose an information-theoretic bias measurement technique through a causal
interpretation of spurious correlation, which is effective to identify the feature-level …

Demographic bias is a significant challenge in practical face recognition systems. Several
methods have been proposed to reduce the bias, which rely on accurate demographic …

Despite the impressive prediction ability, machine learning models show discrimination
towards certain demographics and suffer from unfair prediction behaviors. To alleviate the …

Measuring algorithmic bias is crucial both to assess algorithmic fairness and to guide the
improvement of algorithms. Current bias measurement methods in computer vision are …

The idealization of a static machine-learned model, trained once and deployed forever, is
not practical. As input distributions change over time, the model will not only lose accuracy …

Current face recognition systems achieve high performance on several benchmark tests.
Despite this progress, recent works showed that these systems are strongly biased against …

Face recognition systems are widely deployed in safety-critical applications, including law
enforcement, yet they exhibit bias across a range of socio-demographic dimensions, such as …

As the deployment of automated face recognition (FR) systems proliferates, bias in these
systems is not just an academic question, but a matter of public concern. Media portrayals …

Fairness is becoming an increasingly crucial issue for computer vision, especially in the
human-related decision systems. However, achieving algorithmic fairness, which makes a …

Developing fair deep learning models for identity-sensitive applications (eg, face attribute
recognition) has gained increasing attention from the research community. Indeed, it has …