Eliminating Latent Discrimination: Train Then Mask

How can we control for latent discrimination in predictive models? How can we provably remove it? Such questions are at the heart of algorithmic fairness and its impacts on society. In this paper, we define a new operational fairness criteria, inspired by the well-understood notion of omitted variable-bias in statistics and econometrics. Our notion of fairness effectively controls for sensitive features and provides diagnostics for deviations from fair decision making. We then establish analytical and algorithmic results about the existence of a fair classifier in the context of supervised learning. Our results readily imply a simple, but rather counter-intuitive, strategy for eliminating latent discrimination. In order to prevent other features proxying for sensitive features, we need to include sensitive features in the training phase, but exclude them in the test/evaluation phase while controlling for their effects. We evaluate the performance of our algorithm on several real-world datasets and show how fairness for these datasets can be improved with a very small loss in accuracy

Spam on the Internet: can it be eradicated or is it here to stay?

A discussion of the rise in unsolicited bulk e-mail, its effect on tertiary education, and some of the methods being used or developed to combat it. Includes an examination of block listing, protocol change, economic and computational solutions, e-mail aliasing, sender warranted e-mail, collaborative filtering, rule-based and statistical solutions, and legislation

Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics

Mouse dynamics is a potential means of authenticating users. Typically, the authentication process is based on classical machine learning techniques, but recently, deep learning techniques have been introduced for this purpose. Although prior research has demonstrated how machine learning and deep learning algorithms can be bypassed by carefully crafted adversarial samples, there has been very little research performed on the topic of behavioural biometrics in the adversarial domain. In an attempt to address this gap, we built a set of attacks, which are applications of several generative approaches, to construct adversarial mouse trajectories that bypass authentication models. These generated mouse sequences will serve as the adversarial samples in the context of our experiments. We also present an analysis of the attack approaches we explored, explaining their limitations. In contrast to previous work, we consider the attacks in a more realistic and challenging setting in which an attacker has access to recorded user data but does not have access to the authentication model or its outputs. We explore three different attack strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based; we show that they are able to evade the functionality of the authentication models, thereby impacting their robustness adversely. We show that imitation-based attacks often perform better than surrogate-based attacks, unless, however, the attacker can guess the architecture of the authentication model. In such cases, we propose a potential detection mechanism against surrogate-based attacks.Comment: Accepted in 2019 International Joint Conference on Neural Networks (IJCNN). Update of DO

Acoustic Scene Classification

This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship (EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council