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.

The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need methods for exploration, debugging, and validation. New methods and tools for this purpose are being developed within the eXplainable Artificial Intelligence (XAI) subdomain of machine learning. In this work (1) we present the taxonomy of methods for model explanations, (2) we identify and compare 27 packages available in R to perform XAI analysis, (3) we present an example of an application of particular packages, (4) we acknowledge recent trends in XAI. The article is primarily devoted to the tools available in R, but since it is easy to integrate the Python code, we will also show examples for the most popular libraries from Python.

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The most useful and accurate AI models are also more complex, and the more complex a model is, the more challenging it is to comprehend and trust. Why did it make that prediction? AI is not infallible, and it increasingly operates in an opaque way. This severely limits the adoption of advanced AI models in critical settings. The goal of Explainable AI (XAI) is to develop techniques to help users better understand and trust AI models.