How we bank, where we bank and who we bank with are changing dramatically. These incredible shifts are being driven by increased customer expectations and the power of disruptive technology to meet them.If evidence were needed for such an assertion, the rise of Buy Now Pay Later (BNPL) would be a good place to start. Consumers today are used to fast, seamless, personalized experiences from global platforms like Netflix or Amazon. It's an experience that's also expected from banking: an intuitive and embeddable journey through everyday transactions. BNPL provides such an experience.

This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: decision-tree (fully transparent, interpretable, white-box model), a random forest (a semi-interpretable, grey-box model), and a neural network (a fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in five different datasets (COMPAS, Adult, German, Diabetes, and Breast Cancer). Our findings indicate that: (1) Different machine learning models have no impact on the generation of counterfactual explanations; (2) Counterfactual algorithms based uniquely on proximity loss functions are not actionable and will not provide meaningful explanations; (3) One cannot have meaningful evaluation results without guaranteeing plausibility in the counterfactual generation process. Algorithms that do not consider plausibility in their internal mechanisms will lead to biased and unreliable conclusions if evaluated with the current state-of-the-art metrics; (4) A qualitative analysis is strongly recommended (together with a quantitative analysis) to ensure a robust analysis of counterfactual explanations and the potential identification of biases.

Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system's predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health applications are based on tabular and time series data, respectively, and XAI is not commonly analysed on these data types, while computer vision and Natural Language Processing (NLP) are the reference applications. To provide an overview of XAI methods that are most suitable for tabular and time series data in the healthcare domain, this paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality. Specifically, we identify clinical validation, consistency assessment, objective and standardised quality evaluation, and human-centered quality assessment as key features to ensure effective explanations for the end users. Finally, we highlight the main research challenges in the field as well as the limitations of existing XAI methods.

"Black box" artificial intelligence (AI) systems are designed to automate decision-making, mapping a user's features into a class predicting individual behavioral traits such as credit risk, health status, and so on, without revealing why. This is problematic, not only because of the lack of transparency, but also because of potential biases inherited by algorithms from human prejudices or any hidden elements in the training data that may result in unfair or incorrect decisions. As AI continues to proliferate, there is an increasing need for technology companies to demonstrate the ability to trace back through the decision-making process, a functionality called explainable AI. This would essentially help them understand why a certain prediction or decision was made, what the important factors were in making that prediction or decision, and how confident the model is in that prediction or decision. To help instill user confidence that operational decisions are built on a foundation of fairness and transparency, Diveplane claims its products are designed around three principles: predict, explain and show.

Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users understand not only why ML models make certain predictions, but also how these predictions can be changed. In this thesis, we examine the explainability of ML models from three vantage points: algorithms, users, and pedagogy, and contribute several novel solutions to the explainability problem.

The first industrial revolution witnessed the birth of steam- and water-powered technology, and now, almost two centuries later, humanity has come a long way and is now experiencing its fourth industrial revolution, otherwise known as industry 4.0. To ramp up the speed of the revolution, experts believe that AI and XAI will play an essential role. Industry 4.0 is centre around enhancing industrial efficiency, utilising technologies such as the internet of things (IoT), cloud computing, cyber-physical systems, and AI. AI is the predominant driver of industry 4.0, automating intelligent systems to self-monitor, interpret, diagnose, and analyse by themselves. AI systems such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision (CV) will enable industries to estimate their maintenance needs and become more efficient.

A semantic tableau method, called an argumentation tableau, that enables the derivation of arguments, is proposed. First, the derivation of arguments for standard propositional and predicate logic is addressed. Next, an extension that enables reasoning with defeasible rules is presented. Finally, reasoning by cases using an argumentation tableau is discussed.

Explainability techniques for data-driven predictive models based on artificial intelligence and machine learning algorithms allow us to better understand the operation of such systems and help to hold them accountable. New transparency approaches are developed at breakneck speed, enabling us to peek inside these black boxes and interpret their decisions. Many of these techniques are introduced as monolithic tools, giving the impression of one-size-fits-all and end-to-end algorithms with limited customisability. Nevertheless, such approaches are often composed of multiple interchangeable modules that need to be tuned to the problem at hand to produce meaningful explanations. This paper introduces a collection of hands-on training materials -- slides, video recordings and Jupyter Notebooks -- that provide guidance through the process of building and evaluating bespoke modular surrogate explainers for tabular data. These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation.

Recent advances in machine learning have led to growing interest in Explainable AI (xAI) to enable humans to gain insight into the decision-making of machine learning models. Despite this recent interest, the utility of xAI techniques has not yet been characterized in human-machine teaming. Importantly, xAI offers the promise of enhancing team situational awareness (SA) and shared mental model development, which are the key characteristics of effective human-machine teams. Rapidly developing such mental models is especially critical in ad hoc human-machine teaming, where agents do not have a priori knowledge of others' decision-making strategies. In this paper, we present two novel human-subject experiments quantifying the benefits of deploying xAI techniques within a human-machine teaming scenario. First, we show that xAI techniques can support SA ($p<0.05)$. Second, we examine how different SA levels induced via a collaborative AI policy abstraction affect ad hoc human-machine teaming performance. Importantly, we find that the benefits of xAI are not universal, as there is a strong dependence on the composition of the human-machine team. Novices benefit from xAI providing increased SA ($p<0.05$) but are susceptible to cognitive overhead ($p<0.05$). On the other hand, expert performance degrades with the addition of xAI-based support ($p<0.05$), indicating that the cost of paying attention to the xAI outweighs the benefits obtained from being provided additional information to enhance SA. Our results demonstrate that researchers must deliberately design and deploy the right xAI techniques in the right scenario by carefully considering human-machine team composition and how the xAI method augments SA.

Explainable Artificial Intelligence (XAI) methods are intended to help human users better understand the decision making of an AI agent. However, many modern XAI approaches are unintuitive to end users, particularly those without prior AI or ML knowledge. In this paper, we present a novel XAI approach we call Responsibility that identifies the most responsible training example for a particular decision. This example can then be shown as an explanation: "this is what I (the AI) learned that led me to do that". We present experimental results across a number of domains along with the results of an Amazon Mechanical Turk user study, comparing responsibility and existing XAI methods on an image classification task. Our results demonstrate that responsibility can help improve accuracy for both human end users and secondary ML models.