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This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard ...

• Challenges in face recognition: Privacy, demographic bias, and generalization.
• Synthetic data offer privacy and large-scale databases with desired characteristics.
• FRCSyn-onGoing: first challenge investigates face recognition and ...

The investigation of demographic bias in facial analysis applications is a topic of growing interest with achievements in face recognition and gender classification. State-of-the-art convolutional neural networks (CNN) and traditional machine ...

Text classification enables higher efficiency on text data queries in information retrieval. However, unintended demographic bias can impair text toxicity classification. Thus, we propose a novel debiasing framework utilizing Adversarial Learning ...

Demographic classification is essential in fairness assessment in recommender systems or in measuring unintended bias in online networks and voting systems. Important fields like education and politics, which often lay a foundation for the future ...

Deep learning has catalysed progress in tasks such as face recognition and analysis, leading to a quick integration of technological solutions in multiple layers of our society. While such systems have proven to be accurate by standard evaluation ...

One of the critical challenges in machine learning applications is to have fair predictions. There are numerous recent examples in various domains that convincingly show that algorithms trained with biased datasets can easily lead to erroneous or ...

Many publications in sentiment mining provide new techniques for improved accuracy in extracting features and corresponding sentiments in texts. For the external validity of these sentiment reports, i.e., the applicability of the results to target ...