Explainable AI or XAI is a sub-category of AI where the decisions made by the model can be interpreted by humans, as opposed to "black box" models. As AI moves from correcting our spelling and targeting ads to driving our cars and diagnosing patients, the need to verify and justify the conclusions being reached is beginning to be prioritised. To begin to delve into the field, lets look at one simple XAI model: the decision tree. Decision trees can be easily read and even mimic a human approach to decision making by breaking the choice into many small sub-choices. A simple example is how one may evaluate local universities when the leave high school.

Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.

The assignment of weights to attacks in a classical Argumentation Framework allows to compute semantics by taking into account the different importance of each argument. We represent a Weighted Argumentation Framework by a non-binary matrix, and we characterize the basic extensions (such as w-admissible, w- stable, w-complete) by analysing sub-blocks of this matrix. Also, we show how to reduce the matrix into another one of smaller size, that is equivalent to the original one for the determination of extensions. Furthermore, we provide two algorithms that allow to build incrementally w-grounded and w-preferred extensions starting from a w-admissible extension.

SCIENCE fiction is littered with examples of intelligent computers, from HAL 9000 in "2001: A Space Odyssey" to Eddie in "The Hitchhiker's Guide to the Galaxy". One thing such fictional machines have in common is a tendency to go wrong, to the detriment of the characters in the story. Eddie obsesses about trivia, and thus puts the spacecraft he is in charge of in danger of destruction. In both cases, an attempt to build something useful and helpful has created a monster. Successful science fiction necessarily plays on real hopes and fears.

Last summer, the Defense Science Board's report on autonomy found that investing in artificial intelligence (AI) warfare is a crucial part of maintaining the United States' national security and military capability. As the report reads, "It should not be a surprise when adversaries employ autonomy against U.S. forces." In other words, AI warfare is likely on the horizon; it's just a matter of who gets there first. This immediately sparks dystopian and apocalyptic reactions from most people, who may envision a Terminator-esque system that will at some point choose to overthrow its human masters. The report concludes that "autonomy will deliver substantial operational value across an increasingly diverse array of DoD missions, but the DoD must move more rapidly to realize this value." Meaning that while the value of autonomy is clear from a military perspective, the Department of Defense has to devote more money and time to realize its full potential -- and do so quickly.

Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths. We collect two new datasets to define and evaluate this task, and propose a novel model which can provide joint textual rationale generation and attention visualization. Our datasets define visual and textual justifications of a classification decision for activity recognition tasks (ACT-X) and for visual question answering tasks (VQA-X). We quantitatively show that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision. We also qualitatively show cases where visual explanation is more insightful than textual explanation, and vice versa, supporting our thesis that multimodal explanation models offer significant benefits over unimodal approaches.

Rienstra 2012) to indicate whether all and only these arguments Within the field of artificial intelligence, abstract argumentation are active, or with each spanning subtree of the argument frameworks have emerged as a useful methodology graph (Hunter 2014) to indicate that all and only the to represent and evaluate nonmonotonic logics. They allow attacks contained in that subtree are active. In all these models, to create a simple, directed graph from a defeasible knowledge an interesting question is to determine the probability base that consists of only arguments (nodes) and attacks for a set of arguments to be acceptable. A different branch (directed edges), then to identify sets of "acceptable" of research on probabilistic argumentation uses probabilities arguments in that graph, and finally to interpret these arguments' to represent the epistemic state of arguments, attacks, or sets conclusions as models in the knowledge base. In this of arguments, i.e., the belief in those elements (in terms of framework, when evaluating which arguments are acceptable acceptance). Although technically similar, this approach has in the graph, the internal structure of arguments is neglected, a completely different purpose than ours, which is the representation which accounts for the simplicity of the formalism.

Currently, much of machine learning is opaque, just like a "black box." However, in order for humans to understand, trust and effectively manage the emerging AI systems, an AI needs to be able to explain its decisions and conclusions. In this paper, I propose an argumentation-based approach to explainable AI, which has the potential to generate more comprehensive explanations than existing approaches.

End-to-end argument mining has enabled the development of new automated essay scoring (AES) systems that use argumentative features (e.g., number of claims, number of support relations) in addition to traditional legacy features (e.g., grammar, discourse structure) when scoring persuasive essays. While prior research has proposed different argumentative features as well as empirically demonstrated their utility for AES, these studies have all had important limitations. In this paper we identify a set of desiderata for evaluating the use of argument mining for AES, introduce an end-to-end argument mining system and associated argumentative feature sets, and present the results of several studies that both satisfy the desiderata and demonstrate the value-added of argument mining for scoring persuasive essays.

Text entailment, the task of determining whether a piece of text logically follows from another piece of text, has become an important component for many natural language processing tasks, such as question answering and information retrieval. For entailments requiring world knowledge, most systems still work as a "black box," providing a yes/no answer that doesn't explain the reasoning behind it. We propose an interpretable text entailment approach that, given a structured definition graph, uses a navigation algorithm based on distributional semantic models to find a path in the graph which links text and hypothesis. If such path is found, it is used to provide a human-readable justification explaining why the entailment holds. Experiments show that the proposed approach present results comparable to some well-established entailment algorithms, while also meeting Explainable AI requirements, supplying clear explanations which allow the inference model interpretation.