A Survey on Bias in Deep NLP

• Published in: Applied sciences Vol. 11; no. 7; p. 3184
• Main Authors: Garrido-Muñoz , Ismael, Montejo-Ráez , Arturo, Martínez-Santiago , Fernando, Ureña-López , L. Alfonso
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2021
• Subjects: Artificial intelligence; Automation; Bias; biased models; Cognition & reasoning; Decision making; deep learning; Flaw detection; Gender; Language; Learning algorithms; Machine learning; Natural language processing; Neural networks; Ontology; Probability distribution; Semantics; Stereotypes; Transfer learning
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11073184

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as “pre-training”), versatile and performing models are released continuously for every new network design. These networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have been found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. In addition, available resources are identified and a strategy to deal with bias in deep NLP is proposed.