We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive performance, our system explains to the clinician on which relevant electronic health records (EHRs) data the prediction is grounded. Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these parameters, such as Early Warning Scores (EWS). The predictive performance of EWSs yields a tradeoff between sensitivity and specificity that can lead to negative outcomes for the patient. Previous work on EHR-trained AI systems offers promising results with high levels of predictive performance in relation to the early, real-time prediction of acute critical illness. However, without insight into the complex decisions by such system, clinical translation is hindered. In this letter, we present our xAI-EWS system, which potentiates clinical translation by accompanying a prediction with information on the EHR data explaining it.

Explainable AI or shortly XAI is a domain that deals with maintaining transparency to the decision making capability of complex machine learning models and algorithms. In this article, we will take a look at such a tool that is built for the purpose of making AI explainable. A simple way to understand this concept is to compare the decision-making process of humans with that of the machines. How do we humans come to a decision? We often make decisions whether they are small insignificant decisions like what outfit to wear for an event, to highly complex decisions that involve risks such as investments or loan approvals.

In computational models of argumentation, the justification of statements has drawn less attention than the construction and justification of arguments. As a consequence, significant losses of sensitivity and expressiveness in the treatment of statement statuses can be incurred by otherwise appealing formalisms. In order to reappraise statement statuses and, more generally, to support a uniform modelling of different phases of the argumentation process we introduce multi-labelling systems, a generic formalism devoted to represent reasoning processes consisting of a sequence of labelling stages. In this context, two families of multi-labelling systems, called argument-focused and statement-focused approach, are identified and compared. Then they are shown to be able to encompass several prominent literature proposals as special cases, thereby enabling a systematic comparison evidencing their merits and limits. Further, we show that the proposed model supports tunability of statement justification by specifying a few alternative statement justification labellings, and we illustrate how they can be seamlessly integrated into different formalisms.

Artificial intelligence is set to transform global productivity, working patterns, and lifestyles and create enormous wealth. Research firm Gartner expects the global AI economy to increase from about $1.2 trillion last year to about $3.9 Trillion by 2022, while McKinsey sees it delivering global economic activity of around $13 trillion by 2030. AI techniques, especially Deep Learning (DL) models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another -- image classification, object detection, object tracking, pose recognition, video analytics, synthetic picture generation -- just to name a few. They are being used in -- healthcare, I.T. services, finance, manufacturing, autonomous driving, video game playing, scientific discovery, and even the criminal justice system. However, they are like anything but classical Machine Learning (ML) algorithms/techniques.

Counterfactual explanations help users understand why machine learned models make certain decisions, and more specifically, how these decisions can be changed. In this work, we frame the problem of finding counterfactual explanations -- the minimal perturbation to an input such that the prediction changes -- as an optimization task. Previously, optimization techniques for generating counterfactual examples could only be applied to differentiable models, or alternatively via query access to the model by estimating gradients from randomly sampled perturbations. In order to accommodate non-differentiable models such as tree ensembles, we propose using probabilistic model approximations in the optimization framework. We introduce a novel approximation technique that is effective for finding counterfactual explanations while also closely approximating the original model. Our results show that our method is able to produce counterfactual examples that are closer to the original instance in terms of Euclidean, Cosine, and Manhattan distance compared to other methods specifically designed for tree ensembles.

Artificial intelligence is set to transform global productivity, working patterns, and lifestyles and create enormous wealth.

Google has started offering a new service for "explainable AI" or XAI, as it is fashionably called. We take a look at the intent.

There is a problem with artificial intelligence. It can be amazing at churning through gigantic amounts of data to solve challenges that humans struggle with. But understanding how it makes its decisions is often very difficult to do, if not impossible. That means when an AI model works it is not as easy as it should be to make further refinements, and when it exhibits odd behaviour it can be hard to fix. But at an event in London this week, Google's cloud computing division pitched a new facility that it hopes will give it the edge on Microsoft and Amazon, which dominate the sector.

We are the Department of Data Science and Knowledge Engineering (DKE) at Maastricht University, the Netherlands: an international community of 50 researchers at various stages of their career, embedded in the Faculty of Science and Engineering (FSE). Our department has nearly 30 years' experience with research and teaching in the fields of Artificial Intelligence, Computer Science and Mathematics, and we do so in a highly collaborative and cross-disciplinary manner. To strengthen our team, we are looking for a full professor who will work on AI systems that are able to explain the decisions and actions they recommend or take in a human-understandable way. Our department is growing rapidly. This position is one of multiple job openings: you are more than welcome to browse through our other vacancies.