We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations. Our proposed family of measures corresponds to the long-established formulations of cardinal social welfare in economics. We come to this proposal by taking the perspective of a rational, risk-averse individual who is going to be subject to algorithmic decision making and is faced with the task of choosing between several algorithmic alternatives behind a Rawlsian veil of ignorance. The convex formulation of our measures allows us to integrate them as a constraint into any convex loss minimization pipeline. Our empirical analysis reveals interesting trade-offs between our proposal and (a) prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of individual fairness. Furthermore and perhaps most importantly, our work provides both theoretical and empirical evidence suggesting that a lower-bound on our measures often leads to bounded inequality in algorithmic outcomes; hence presenting the first computationally feasible mechanism for bounding individual-level (un)fairness.

Jeremy Howard was pioneering ways for deep learning to help physicians interpret medical data better when the challenge he was tackling suddenly hit close to home. When Jeremy Howard's wife, Rachel, was diagnosed with a brain cyst while she was pregnant with their first child three years ago, Jeremy and Rachel did what comes naturally to data scientists like them. It contained: possible treatments, their known likelihood of success and failure, the value they assigned to different outcomes, and the potential problems if things went wrong. When most of us fall ill, we find ourselves thrust into a world of frantic Googling, confusing choices, and fear of the unknown. We place trust in our doctors to know what's best. When it comes to making decisions, from investing money, to raising children, to taking medicine, he uses probabilities, priors and statistics.

In the majority of cases, you should compare the strength of a few algorithms before choosing the final model. The decision is not a trivial one, but boils down to finding an algorithm that balances strong performance with accomplishing the task in the most straightforward way. You can compare the strength of algorithms by handing your data to each of them and scoring their performance on the task with a technique called cross-validation. The metric used to score performance will vary by context, but some common ones are R-squared for regression, and accuracy, precision, and recall for classification. For classification tasks, quantifying the cost of both a false positive prediction and a false negative prediction can help you to choose the most appropriate metric.

As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) systems widely used today. It is not practical to build an agreed upon test set to benchmark malware detection systems on pure classification performance. Instead we tackle the problem by creating a new testing methodology, where we evaluate the change in performance on a set of known benign & malicious files as adversarial modifications are performed. The change in performance combined with the evasion techniques then quantifies a system's robustness against that approach. Through these experiments we are able to show in a quantifiable way how purely ML based systems can be more robust than AV products at detecting malware that attempts evasion through modification, but may be slower to adapt in the face of significantly novel attacks.

This dissertation investigates the use of one-sided classification algorithms in the application of separating hazardous chlorinated solvents from other materials, based on their Raman spectra. The experimentation is carried out using a new one-sided classification toolkit that was designed and developed from the ground up. In the one-sided classification paradigm, the objective is to separate elements of the target class from all outliers. These one-sided classifiers are generally chosen, in practice, when there is a deficiency of some sort in the training examples. Sometimes outlier examples can be rare, expensive to label, or even entirely absent. However, this author would like to note that they can be equally applicable when outlier examples are plentiful but nonetheless not statistically representative of the complete outlier concept. It is this scenario that is explicitly dealt with in this research work. In these circumstances, one-sided classifiers have been found to be more robust that conventional multi-class classifiers. The term "unexpected" outliers is introduced to represent outlier examples, encountered in the test set, that have been taken from a different distribution to the training set examples. These are examples that are a result of an inadequate representation of all possible outliers in the training set. It can often be impossible to fully characterise outlier examples given the fact that they can represent the immeasurable quantity of "everything else" that is not a target. The findings from this research have shown the potential drawbacks of using conventional multi-class classification algorithms when the test data come from a completely different distribution to that of the training samples.

We propose a method for maximizing a partial area under a receiver operating characteristic (ROC) curve (pAUC) for binary classification tasks. In binary classification tasks, accuracy is the most commonly used as a measure of classifier performance. In some applications such as anomaly detection and diagnostic testing, accuracy is not an appropriate measure since prior probabilties are often greatly biased. Although in such cases the pAUC has been utilized as a performance measure, few methods have been proposed for directly maximizing the pAUC. This optimization is achieved by using a scoring function. The conventional approach utilizes a linear function as the scoring function. In contrast we newly introduce nonlinear scoring functions for this purpose. Specifically, we present two types of nonlinear scoring functions based on generative models and deep neural networks. We show experimentally that nonlinear scoring fucntions improve the conventional methods through the application of a binary classification of real and bogus objects obtained with the Hyper Suprime-Cam on the Subaru telescope.

In the financial services industry, innovations such as faster payments, together with global regulations and regulatory bodies1 raise a significant question: Do existing risk-rule-based money laundering detection systems really work? Financial institutions may also wonder about the effectiveness of fraud detection, the danger of false positives, structural costs, and the impact on customer transaction experience. According to Gartner, "if money laundering were an economy, it would be the fifth largest in the world." Strikingly, the UN also estimates that the amount of money laundered annually is equivalent to 2-5% of the global GDP. Another report from the Heritage Foundation suggests that complying with anti-money laundering (AML) rules, which require hiring the right talent needed to file suspicious activity reports, costs U.S. companies alone as much as $8 billion a year.

Temenos (SIX:TEMN), the banking software company, expanded its financial crime mitigation product to include an AI-based Suspicious Activity Prevention solution protecting banks and their customers from fraud. Demand for financial compliance and the increasing levels of financial crime are putting huge pressure on banks. Their legacy processes have grown so complex with a high level of manual work for screening alerts and other fraud mitigation activities. Each manual step is inefficient and prone to errors. High level of false positive rates exacerbate this problem.

This paper proposes an inexpensive way to learn an effective dissimilarity function to be used for $k$-nearest neighbor ($k$-NN) classification. Unlike Mahalanobis metric learning methods that map both query (unlabeled) objects and labeled objects to new coordinates by a single transformation, our method learns a transformation of labeled objects to new points in the feature space whereas query objects are kept in their original coordinates. This method has several advantages over existing distance metric learning methods: (i) In experiments with large document and image datasets, it achieves $k$-NN classification accuracy better than or at least comparable to the state-of-the-art metric learning methods. (ii) The transformation can be learned efficiently by solving a standard ridge regression problem. For document and image datasets, training is often more than two orders of magnitude faster than the fastest metric learning methods tested. This speed-up is also due to the fact that the proposed method eliminates the optimization over "negative" object pairs, i.e., objects whose class labels are different. (iii) The formulation has a theoretical justification in terms of reducing hubness in data.

Artificial neural networks tend to learn only what they need for a task. A manipulation of the training data can counter this phenomenon. In this paper, we study the effect of different alterations of the training data, which limit the amount and position of information that is available for the decision making. We analyze the accuracy and robustness against semantic and black box attacks on the networks that were trained on different training data modifications for the particular example of morphing attacks. A morphing attack is an attack on a biometric facial recognition system where the system is fooled to match two different individuals with the same synthetic face image. Such a synthetic image can be created by aligning and blending images of the two individuals that should be matched with this image.