A non-discriminatory approach to ethical deep learning

Abstract

Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, nowadays they are used to solve an incredibly large variety of tasks. However, typical training strategies do not take into account lawful, ethical and discriminatory potential issues the trained ANN models could incur in. In this work we propose NDR, a non-discriminatory regularization strategy to prevent the ANN model to solve the target task using some discriminatory features like, for example, the ethnicity in an image classification task for human faces. In particular, a part of the ANN model is trained to hide the discriminatory information such that the rest of the network focuses in learning the given learning task. Our experiments show that NDR can be exploited to achieve non-discriminatory models with both minimal computational overhead and performance loss.

References in corpus (8)

• FaceNet: A Unified Embedding for Face Recognition and Clustering
• The Measure and Mismeasure of Fairness
• Learning Adversarially Fair and Transferable Representations
• Women also Snowboard: Overcoming Bias in Captioning Models
• Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
• Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
• Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
• Post-synaptic potential regularization has potential

Cited by in corpus (2)

• EnD: Entangling and Disentangling deep representations for bias correction
• Disentangling private classes through regularization