Biased Embeddings from Wild Data: Measuring, Understanding and Removing

With the latest wave of learning models taking advantage of advances in deep learning [21], [22], [23], Artificial Intelligence (AI) systems are gaining widespread publicity, coupled with a drive from industry to incorporate intelligence into all manner of processes that handle our private and personal data, giving them a central position in our modern-day society. This development has lead to demand for fairer AI, where we wish to establish trust in the automated intelligent systems by ensuring that systems represent us fairly and transparently. However, there has been growing concern about potential biases in learning systems [1], [6] which can be difficult to analyse or query for explanations of their predictions, leading to an increasing number of studies investigating the way blackbox systems represent knowledge and make decisions [7], [9], [11], [19], [20]. Indeed, principled methods are now required that allow us to measure, understand and remove biases in our data in order for these systems to be truly accepted as a prominent part of our lives. In the domain of text, many modern approaches often begin by embedding the input text data into an embedding space that is used as the first layer in a subsequent deep network [4], [14]. These word embeddings have been shown to contain the same biases [3], due to the source data from which they are trained.