Add to ORKGThe performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes. We address this problem by introducing fairness-aware regularization losses based on batch estimates of Demographic Parity, Equalized Odds, and a novel Intersection-over-Union measure. The experiments performed on facial and medical images from CelebA, UTKFace, and the SIIM-ISIC melanoma classification challenge show the effectiveness of our proposed fairness losses for bias mitigation as they improve model fairness while maintaining high classification performance. To the best of our knowledge, our work is the first attempt to incorporate these types of losses in a...
We propose a discrimination-aware learning method to improve both the accuracy and fairness of biase...
The concerns regarding ramifications of societal bias targeted at a particular identity group (for e...
The central goal of Algorithmic Fairness is to develop AI-based systems which do not discriminate su...
Fairness is crucial when training a deep-learning discriminative model, especially in the facial dom...
Fairness is crucial when training a deep-learning discriminative model, especially in the facial dom...
Fairness is crucial when training a deep-learning discriminative model, especially in the facial dom...
Deep learning is becoming increasingly ubiquitous in medical research and applications while involvi...
Trustworthiness, and in particular Algorithmic Fairness, is emerging as one of the most trending top...
Trustworthiness, and in particular Algorithmic Fairness, is emerging as one of the most trending top...
Important decisions are increasingly based directly on predictions from classifiers; for example, ma...
Computer vision models have known performance disparities across attributes such as gender and skin ...
Fairness in machine learning is getting rising attention as it is directly related to real-world app...
Recognition of expressions of emotions and a ect from facial images is a well-studied research probl...
We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associate...
We propose a discrimination-aware learning method to improve both the accuracy and fairness of biase...
We propose a discrimination-aware learning method to improve both the accuracy and fairness of biase...
The concerns regarding ramifications of societal bias targeted at a particular identity group (for e...
The central goal of Algorithmic Fairness is to develop AI-based systems which do not discriminate su...
Fairness is crucial when training a deep-learning discriminative model, especially in the facial dom...
Fairness is crucial when training a deep-learning discriminative model, especially in the facial dom...
Fairness is crucial when training a deep-learning discriminative model, especially in the facial dom...
Deep learning is becoming increasingly ubiquitous in medical research and applications while involvi...
Trustworthiness, and in particular Algorithmic Fairness, is emerging as one of the most trending top...
Trustworthiness, and in particular Algorithmic Fairness, is emerging as one of the most trending top...
Important decisions are increasingly based directly on predictions from classifiers; for example, ma...
Computer vision models have known performance disparities across attributes such as gender and skin ...
Fairness in machine learning is getting rising attention as it is directly related to real-world app...
Recognition of expressions of emotions and a ect from facial images is a well-studied research probl...
We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associate...
We propose a discrimination-aware learning method to improve both the accuracy and fairness of biase...
We propose a discrimination-aware learning method to improve both the accuracy and fairness of biase...
The concerns regarding ramifications of societal bias targeted at a particular identity group (for e...
The central goal of Algorithmic Fairness is to develop AI-based systems which do not discriminate su...