The trustworthiness of machine learning has emerged as a critical topic in the field,
encompassing various applications and research areas such as robustness, security …

Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for
ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have …

Neural networks are often prone to bias toward spurious correlations inherent in a dataset,
thus failing to generalize unbiased test criteria. A key challenge to resolving the issue is the …

Deep learning models have achieved great success in automating skin lesion diagnosis.
However, the ethnic disparity in these models' predictions, where lesions on darker skin …

The importance of addressing fairness and bias in artificial intelligence (AI) systems cannot
be over-emphasized. Mainstream media has been awashed with news of incidents around …

In medical image diagnosis, fairness has become increasingly crucial. Without bias
mitigation, deploying unfair AI would harm the interests of the underprivileged population …

Fairness has been a critical issue that affects the adoption of deep learning models in real
practice. To improve model fairness, many existing methods have been proposed and …

The soaring number of personal mobile devices and public cameras poses a threat to
fundamental human rights and ethical principles. For example, the stolen of private …

Fairness is a fundamental requirement for trustworthy and human-centered Artificial
Intelligence (AI) system. However, deep neural networks (DNNs) tend to make unfair …

Abstract Facial Attribute Classification (FAC) holds substantial promise in widespread
applications. However FAC models trained by traditional methodologies can be unfair by …