We addressed the problem of a lack of semantic representation for user-centric explanations and different explanation types in our Explanation Ontology (https://purl.org/heals/eo). Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelligence with the emergence of complex methods and an uptake in high-precision and user-facing settings. In this submission, we provide step-by-step guidance for system designers to utilize our ontology, introduced in our resource track paper, to plan and model for explanations during the design of their Artificial Intelligence systems. We also provide a detailed example with our utilization of this guidance in a clinical setting.

Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of the current state of Explainable AI (XAI), considered within the domain of Natural Language Processing (NLP). We discuss the main categorization of explanations, as well as the various ways explanations can be arrived at and visualized. We detail the operations and explainability techniques currently available for generating explanations for NLP model predictions, to serve as a resource for model developers in the community. Finally, we point out the current gaps and encourage directions for future work in this important research area.

Explainability has been a challenge in AI for as long as AI has existed. With the recently increased use of AI in society, it has become more important than ever that AI systems would be able to explain the reasoning behind their results also to end-users in situations such as being eliminated from a recruitment process or having a bank loan application refused by an AI system. Especially if the AI system has been trained using Machine Learning, it tends to contain too many parameters for them to be analysed and understood, which has caused them to be called `black-box' systems. Most Explainable AI (XAI) methods are based on extracting an interpretable model that can be used for producing explanations. However, the interpretable model does not necessarily map accurately to the original black-box model. Furthermore, the understandability of interpretable models for an end-user remains questionable. The notions of Contextual Importance and Utility (CIU) presented in this paper make it possible to produce human-like explanations of black-box outcomes directly, without creating an interpretable model. Therefore, CIU explanations map accurately to the black-box model itself. CIU is completely model-agnostic and can be used with any black-box system. In addition to feature importance, the utility concept that is well-known in Decision Theory provides a new dimension to explanations compared to most existing XAI methods. Finally, CIU can produce explanations at any level of abstraction and using different vocabularies and other means of interaction, which makes it possible to adjust explanations and interaction according to the context and to the target users.

In recent years there has been a cascade of research in attempting to make AI systems more interpretable by providing explanations; so-called Explainable AI (XAI). Most of this research has dealt with the challenges that arise in explaining black-box deep learning systems in classification and regression tasks, with a focus on tabular and image data; for example, there is a rich seam of work on post-hoc counterfactual explanations for a variety of black-box classifiers (e.g., when a user is refused a loan, the counterfactual explanation tells the user about the conditions under which they would get the loan). However, less attention has been paid to the parallel interpretability challenges arising in AI systems dealing with time series data. This paper advances a novel technique, called Native-Guide, for the generation of proximal and plausible counterfactual explanations for instance-based time series classification tasks (e.g., where users are provided with alternative time series to explain how a classification might change). The Native-Guide method retrieves and uses native in-sample counterfactuals that already exist in the training data as "guides" for perturbation in time series counterfactual generation. This method can be coupled with both Euclidean and Dynamic Time Warping (DTW) distance measures. After illustrating the technique on a case study involving a climate classification task, we reported on a comprehensive series of experiments on both real-world and synthetic data sets from the UCR archive. These experiments provide computational evidence of the quality of the counterfactual explanations generated.

Legal Judgment Prediction (LJP) is a longstanding and open topic in the theory and practice-of-law. Predicting the nature and outcomes of judicial matters is abundantly warranted, keenly sought, and vigorously pursued by those within the legal industry and also by society as a whole. The tenuous act of generating judicially laden predictions has been limited in utility and exactitude, requiring further advancement. Various methods and techniques to predict legal cases and judicial actions have emerged over time, especially arising via the advent of computer-based modeling. There has been a wide range of approaches attempted, including simple calculative methods to highly sophisticated and complex statistical models. Artificial Intelligence (AI) based approaches have also been increasingly utilized. In this paper, a review of the literature encompassing Legal Judgment Prediction is undertaken, along with innovatively proposing that the advent of AI Legal Reasoning (AILR) will have a pronounced impact on how LJP is performed and its predictive accuracy. Legal Judgment Prediction is particularly examined using the Levels of Autonomy (LoA) of AI Legal Reasoning, plus, other considerations are explored including LJP probabilistic tendencies, biases handling, actor predictors, transparency, judicial reliance, legal case outcomes, and other crucial elements entailing the overarching legal judicial milieu.

Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons, explanation and trust have been intertwined. However, the focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that produced more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility of machine learning explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.

We invite applications for a tenure-track position in computer science, focused on explainable artificial intelligence, and ability to collaborate with social sciences. DKE research lines include human-centered aspects of recommender systems, as well as a strong applied mathematics component such as dynamic game theory (differential, evolutionary, spatial and stochastic game theory). The position is supported by the large and growing Explainable and Reliable Artificial Intelligence (ERAI) group of DKE. The group consists of Associate & Assistant Professors, postdoctoral researchers, PhD candidates and master/bachelor students. The ERAI group works together closely on a day-to-day basis, to exchange knowledge, ideas, and research advancements.

The so-called "black-box" aspect of AI, usually referred to as the explainability problem, or X(AI) for short, arose slowly over the past few years. Still, with the rapid development in AI, it is now considered a significant problem. How can you trust a model if you cannot understand how it reaches its conclusions? For commercial benefits, for ethics concerns or regulatory considerations, X)(AI) is essential if users understand, appropriately trust, and effectively manage AI results. In researching this topic, I was surprised to find almost 400 papers on the subject.

In explainable artificial intelligence, there is increasing interest in understanding the behaviour of autonomous agents to build trust and validate performance. Modern agent architectures, such as those trained by deep reinforcement learning, are currently so lacking in interpretable structure as to effectively be black boxes, but insights may still be gained from an external, behaviourist perspective. Inspired by conceptual spaces theory, we suggest that a versatile first step towards general understanding is to discretise the state space into convex regions, jointly capturing similarities over the agent's action, value function and temporal dynamics within a dataset of observations. We create such a representation using a novel variant of the CART decision tree algorithm, and demonstrate how it facilitates practical understanding of black box agents through prediction, visualisation and rule-based explanation.

There are two reasons why. First, with machine learning in general and neural networks or deep learning in particular, there is often a trade-off between performance and explainability. The larger and more complex a model, the harder it will be to understand, even though its performance is generally better. Unfortunately, for complex situations with many interacting influences--which is true of many key areas of policy--machine learning will often be more useful the more of a black box it is. As a result, holding such systems accountable will almost always be a matter of post hoc monitoring and evaluation.