An \textit{abstract argumentation framework} ({\sc af} for short) is a directed graph $(A,R)$ where $A$ is a set of \textit{abstract arguments} and $R\subseteq A \times A$ is the \textit{attack} relation. Let $H=(A,R)$ be an {\sc af}, $S \subseteq A$ be a set of arguments and $S^+ = \{y \mid \exists x\in S \text{ with }(x,y)\in R\}$. Then, $S$ is a \textit{stable extension} in $H$ if and only if $S^+ = A\setminus S$. In this paper, we present a thorough, formal validation of a known backtracking algorithm for listing all stable extensions in a given {\sc af}.

Prior studies have found that providing explanations about robots' decisions and actions help to improve system transparency, increase human users' trust of robots, and enable effective human-robot collaboration. Different users have various preferences about what should be included in explanations. However, little research has been conducted for the generation of personalized explanations. In this paper, we present a system for generating personalized explanations of robotic planning via user feedback. We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. Our user study results show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering, and conflict detection and resolution.

Machine learning algorithms can produce impressive results in classification, prediction, anomaly detection, and many other hard problems. Understanding what the results are based on is often complicated, since many algorithms are black boxes with little visibility into their inner working. Explainable AI is a term referring to techniques for providing human-understandable explanations of ML algorithm outputs. Explainable AI is interesting for many reasons, including being able to reason about the algorithms used, the data we have to train them, and to understand better how to test the system using such algorithms. LIME, or Local Interpretable Model-Agnostic Explanations is one technique that seems to have gotten attention lately in this area. The idea of LIME is to give it a single datapoint, and the ML algorithm to use, and it will try to build understandable explanation for the output of the ML algorithm for that specific datapoint. Such as "because this person was found to be sneezing and coughing (datapoint features), there is a high probability they have a flu (ML output)".

In Machine Learning (ML) models used for supporting decisions in high-stakes domains such as public policy, explainability is crucial for adoption and effectiveness. While the field of explainable ML has expanded in recent years, much of this work does not take real-world needs into account. A majority of proposed methods use benchmark ML problems with generic explainability goals without clear use-cases or intended end-users. As a result, the effectiveness of this large body of theoretical and methodological work on real-world applications is unclear. This paper focuses on filling this void for the domain of public policy. We develop a taxonomy of explainability use-cases within public policy problems; for each use-case, we define the end-users of explanations and the specific goals explainability has to fulfill; third, we map existing work to these use-cases, identify gaps, and propose research directions to fill those gaps in order to have practical policy impact through ML.

Currently AI models are often developed with only predictive performance in mind. Thus, the majority of the XAI literature is dedicated to explaining pre-developed models. This bias of focus along with the recent popularity of XAI research has resulted in development of numerous and diverse post-hoc explainability methods. It's challenging to understand this vast body of literature because of the numerous approaches to XAI. In order to make sense of the post-hoc explainability methods, we propose a taxonomy or a way of breaking down these methods that shows their common structure, organized around four key aspects: the target, what is to be explained about the model; the drivers, what is causing the thing you want explained; the explanation family, how the explanation information about the drivers causing the target is communicated to the user; and the estimator, the computational process of actually obtaining the explanation. For instance, the popular Local Interpretable Model-agnostic Explanations (LIME) approach provides explanation for an instance prediction of a model, the target, in terms of input features, the drivers, using importance scores, the explanation family, computed through local perturbations of the model input, the estimator.

While deliberate misinformation and deception are by no means new societal phenomena, the recent rise of fake news5 and information silos2 has become a growing international concern, with politicians, governments and media organizations regularly lamenting the issue. A remedy to this situation, we argue, could be found in using technology to empower people's ability to critically assess the quality of information, reasoning, and argumentation through technological means. Recent empirical findings suggest "false news spreads more than the truth because humans, not robots, are more likely to spread it."10 Thus, instead of continuing to focus on ways of limiting the efficacy of bots, educating human users to better recognize fake news stories could prove more effective in mitigating the potentially devastating social impact misinformation poses. While technology certainly contributes to the distribution of fake news and similar attacks on reasonable decision-making and debate, we posit that technology--argument technology in particular--can equally be employed to counterbalance these deliberately misleading or outright false reports made to look like genuine news.

This paper presents Abduction and Argumentation as two principled forms for reasoning, and fleshes out the fundamental role that they can play within Machine Learning. It reviews the state-of-the-art work over the past few decades on the link of these two reasoning forms with machine learning work, and from this it elaborates on how the explanation-generating role of Abduction and Argumentation makes them naturally-fitting mechanisms for the development of Explainable Machine Learning and AI systems. Abduction contributes towards this goal by facilitating learning through the transformation, preparation, and homogenization of data. Argumentation, as a conservative extension of classical deductive reasoning, offers a flexible prediction and coverage mechanism for learning -- an associated target language for learned knowledge -- that explicitly acknowledges the need to deal, in the context of learning, with uncertain, incomplete and inconsistent data that are incompatible with any classically-represented logical theory.

Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not only have a highly robust and accurate model, but be able to generate useful explanations to garner a user's trust in the automated system. Regrettably, the recent research regarding eXplainable AI (XAI) in financial text classification has received little to no attention, and many current methods for generating textual-based explanations result in highly implausible explanations, which damage a user's trust in the system. To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models in the domain of FinTech. Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current state-of-the-art and human performance, but it also generates counterfactual explanations which are significantly more plausible based on human trials.

Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine-learning-based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations, a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently-proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.

This might be the first time you hear about Explainable Artificial Intelligence, but it is certainly something you should have an opinion about. Explainable AI (XAI) refers to the techniques and methods to build AI applications that humans can understand "why" they make particular decisions. In other words, if we can get explanations from an AI system about its inner logic, this system is considered as an XAI system. Explainability is a new property that started to gain popularity in the AI community, and we will talk about why that happened in recent years. Let's dive into the technical roots of the problem, first.