At a demonstration of Amazon Web Services' new artificial intelligence image recognition tool last week, the deep learning analysis calculated with near certainty that a photo of speaker Glenn Gore depicted a potted plant. "It is very clever, it can do some amazing things but it needs a lot of hand holding still. AI is almost like a toddler. They can do some pretty cool things, sometimes they can cause a fair bit of trouble," said AWS' chief architect in his day two keynote at the company's summit in Sydney. Where the toddler analogy falls short, however, is that a parent can make a reasonable guess as to, say, what led to their child drawing all over the walls, and ask them why.

Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by considering probability assignments on arguments, allowing for a quantitative treatment of formal argumentation. In this paper, we regard the assignment as denoting the degree of belief that an agent has in an argument being acceptable. While there are various interpretations of this, an example is how it could be applied to a deductive argument. Here, the degree of belief that an agent has in an argument being acceptable is a combination of the degree to which it believes the premises, the claim, and the derivation of the claim from the premises. We consider constraints on these probability assignments, inspired by crisp notions from classical abstract argumentation frameworks and discuss the issue of probabilistic reasoning with abstract argumentation frameworks. Moreover, we consider the scenario when assessments on the probabilities of a subset of the arguments are given and the probabilities of the remaining arguments have to be derived, taking both the topology of the argumentation framework and principles of probabilistic reasoning into account. We generalise this scenario by also considering inconsistent assessments, i.e., assessments that contradict the topology of the argumentation framework. Building on approaches to inconsistency measurement, we present a general framework to measure the amount of conflict of these assessments and provide a method for inconsistency-tolerant reasoning.

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to retrieve (by derivation) multiple statements (sometimes assumed) or results from the literature.

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.

Growing concerns about how artificial intelligence (AI) makes decisions has inspired U.S. researchers to make computers explain their "thinking." "Computers are going to become increasingly important parts of our lives, if they aren't already, and the automation is just going to improve over time, so it's increasingly important to know why these complicated systems are making the decisions that they are," assistant professor of computer science at the University of California Irvine, Sameer Singh, told CTV's Your Morning on Tuesday. Singh explained that, in almost every application of machine learning and AI, there are cases where the computers do something completely unexpected. "Sometimes it's a good thing, it's doing something much smarter than we realize," he said. Such was the case with the Microsoft AI chatbot, Tay, which became racist in less than a day. Another high-profile incident occurred in 2015, when Google's photo app mistakenly labelled a black couple as gorillas.

As machine learning and AI become more ubiquitous, there are growing calls for the technologies to explain themselves in human terms. Despite being used to make life-altering decisions from medical diagnoses to loan limits, the inner workings of various machine learning architectures – including deep learning, neural networks and probabilistic graphical models – are incredibly complex and increasingly opaque. As these techniques improve, often by themselves, revealing their inner workings becomes more and more difficult. They have become a'black box', according to growing numbers of scientists, governments and concerned citizens. There are now calls for these systems to expose their decision-making process, and be'explainable' to non-experts: An approach known as explainable artificial intelligence or XAI.

Kathy Griffin is set to explain the reasoning behind her controversial photo shoot with a bloodied mask of President Trump and respond to alleged bullying from the Trump family on Friday, her attorney announced. Griffin and attorney Lisa Bloom said in a joint news release they will hold a press conference in Woodland Hills, Calif. at 9 a.m. It will be the first comments Griffin has made since she was relieved of her duties as CNN's New Year's Eve host. Proud to announce that I represent Kathy Griffin. We will be holding a press conference tomorrow morning.

Currently, much of machine learning is opaque, a genuine black box. Machines may be given an extraordinarily complex task and come up with what appears to be a reasonable solution, but they are largely incapable of explaining how or why they came up with that solution. That's why some of the smartest AI researchers in the industry are now hot on the trail of finding new ways to make machines understandable for humans. Much of that focus is on an emerging field known as Explainable AI (XAI), which in very simple terms is the ability of machines to explain their rationale, characterize the strengths and weaknesses of their decision-making process, and, most importantly, convey a sense of how they will behave in the future. This field of XAI is going to be hugely important, with a number of important social, legal and ethical implications.

We consider abstract-argumentation-theoretic coalition formability in this work. Taking a model from political alliance among political parties, we will contemplate profitability, and then formability, of a coalition. As is commonly understood, a group forms a coalition with another group for a greater good, the goodness measured against some criteria. As is also commonly understood, however, a coalition may deliver benefits to a group X at the sacrifice of something that X was able to do before coalition formation, which X may be no longer able to do under the coalition. Use of the typical conflict-free sets of arguments is not very fitting for accommodating this aspect of coalition, which prompts us to turn to a weaker notion, conflict-eliminability, as a property that a set of arguments should primarily satisfy. We require numerical quantification of attack strengths as well as of argument strengths for its characterisation. We will first analyse semantics of profitability of a given conflict-eliminable set forming a coalition with another conflict-eliminable set, and will then provide four coalition formability semantics, each of which formalises certain utility postulate(s) taking the coalition profitability into account.

We present the concept of malicious argumentation strategies that extends malicious argumentation tactics to manipulate the outcome of an argumentation based decision making process with resource limits. We give an example of such a strategy, Exhaust and Protract, and show in a decision making example how Exhaust and Protract can be used to change the result of the decision making process.