In [3] we presented a prototype of a system that enables users to explore arguments for a given topic. This involves these steps: 1. Argument identification. In the first step, arguments concerning a given topic are identified in a given text and attacking and supporting relationships between the propositions are established. The result is an argumentation graph. In the future we hope to use argumentation mining techniques to automate this step. At this time, this is done manually by marking up some text.

The effort aims to better understand how a machine-learning model comes to a logical conclusion.

In existing literature, while approximate approaches based on Monte-Carlo simulation technique have been proposed to compute the semantics of probabilistic argumentation, how to improve the efficiency of computation without using simulation technique is still an open problem. In this paper, we address this problem from the following two perspectives. First, conceptually, we define specific properties to characterize the subgraphs of a PrAG with respect to a given extension, such that the probability of a set of arguments E being an extension can be defined in terms of these properties, without (or with less) construction of subgraphs. Second, computationally, we take preferred semantics as an example, and develop algorithms to evaluate the efficiency of our approach. The results show that our approach not only dramatically decreases the time for computing p(E^\sigma), but also has an attractive property, which is contrary to that of existing approaches: the denser the edges of a PrAG are or the bigger the size of a given extension E is, the more efficient our approach computes p(E^\sigma). Meanwhile, it is shown that under complete and preferred semantics, the problems of determining p(E^\sigma) are fixed-parameter tractable.

Crowd workers are human and thus sometimes make mistakes. In order to ensure the highest quality output, requesters often issue redundant jobs with gold test questions and sophisticated aggregation mechanisms based on expectation maximization (EM). While these methods yield accurate results in many cases, they fail on extremely difficult problems with local minima, such as situations where the majority of workers get the answer wrong. Indeed, this has caused some researchers to conclude that on some tasks crowdsourcing can never achieve high accuracies, no matter how many workers are involved. This paper presents a new quality-control workflow, called MicroTalk, that requires some workers to Justify their reasoning and asks others to Reconsider their decisions after reading counter-arguments from workers with opposing views. Experiments on a challenging NLP annotation task with workers from Amazon Mechanical Turk show that (1) argumentation improves the accuracy of individual workers by 20%, (2) restricting consideration to workers with complex explanations improves accuracy even more, and (3) our complete MicroTalk aggregation workflow produces much higher accuracy than simpler voting approaches for a range of budgets.

To gain intuition and reasoning of a model is to have understanding and trust--transparency. When you strike a nail with a hammer, it's pretty predictable what might happen: the nail could get hit, the hammer could miss, or very rarely, the hammer's head may fly off of the handle. When you replace the hammer with a black box that works correctly 99.999% of the time, but for 0.001%, something completely unpredictable happens, then there's a problem with volatility because that unpredictable event may have unacceptable consequences. I think explainable AI could help with intuitive and more fine-grained risk analysis, and that's certainly a good thing in high-stakes applications such as defense.

The goal of Explainable AI (XAI) is to create a suite of new or modified machine learning techniques that produce explainable models that, when combined with effective explanation techniques, enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems.

Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions and actions to human users. The Department of Defense is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI--especially explainable machine learning--will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.

The U.S. Defense Advanced Research Projects Agency (DARPA) has launched a program that will create the technology to make new generations of artificial intelligence (AI) systems "explainable." DARPA'S Explainable AI (XAI) program aims to create new machine learning methods to produce more explainable models and combine them with explanation techniques. And why the need to understand AI? That's because explainable AI -- especially explainable machine learning -- will be essential if future American warfighters are to understand, appropriately trust and effectively manage an emerging generation of AI "partners" such as battlefield robots and machines. XAI is vital because continued advances in AI promise to produce autonomous systems that will perceive, learn, decide and act on their own. The effectiveness of these AI systems, however, is limited by the machine's current inability to explain their decisions and actions to human users.

In a normative environment an agent's actions are not only directed by its goals but also by the norms imposed on the agent. However, the potential conflicts within and between the agent's goals and norms makes decision-making in these frameworks a challenging task. The questions we are addressing in this paper are: (i) how should an agent act in a normative environment? We propose a solution in which a normative planning problem serves as the basis for a practical reasoning approach based on argumentation. The properties of the best plan(s) with respect to goal achievement and norm compliance are mapped to arguments that are used to explain why a plan is justified, using an existing proof dialogue game.

This paper studies two issues concerning relevance in structured argumentation in the context of the ASPIC framework, arising from the combined use of strict and defeasible inference rules. One issue arises if the strict inference rules correspond to classical logic. A longstanding problem is how the trivialising effect of the classical Ex Falso principle can be avoided while satisfying consistency and closure postulates. In this paper, this problem is solved by disallowing chaining of strict rules, resulting in a variant of the ASPIC framework called ASPIC*, and then disallowing the application of strict rules to inconsistent sets of formulas. Another issue is minimality of arguments.