Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and
concern among software engineers. To tackle this issue, extensive research has been …

Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI)
algorithms present promising solutions to a variety of technological and non-technological …

Computer Vision (CV) has achieved remarkable results, outperforming humans in several
tasks. Nonetheless, it may result in significant discrimination if not handled properly. Indeed …

Abstract Machine learning models are known to perpetuate and even amplify the biases
present in the data. However, these data biases frequently do not become apparent until …

Face recognition (FR) systems have a growing effect on critical decision-making processes.
Recent works have shown that FR solutions show strong performance differences based on …

Fairness in visual recognition is becoming a prominent and critical topic of discussion as
recognition systems are deployed at scale in the real world. Models trained from data in …

We present a framework for training GANs with explicit control over generated facial images.
We are able to control the generated image by settings exact attributes such as age, pose …

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 …

Vision models often fail systematically on groups of data that share common semantic
characteristics (eg, rare objects or unusual scenes), but identifying these failure modes is a …

Although progress has been made for text-to-image synthesis, previous methods fall short of
generalizing to unseen or underrepresented attribute compositions in the input text. Lacking …