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.

The mining of justifications to be recommended to visitors of deliberation for a used in decision making by constituents raises specific challenges. Graph-based representations can improve our understanding of the problem and enable reasoning with the available data. The addressed technical problem consists in recommending sets of texts containing comprehensive arguments supporting or opposing poll alternatives, as mined from submissions of opinions in electronic deliberative polls. A graphical framework is proposed to enable the development of techniques for identification of relevant/encompassing arguments in debates following the Alternative-Based Information System (ABIS) model, a competitor of the IBIS model. Bipolar argumentation frameworks are extended with votes, enhance relations and argument coalitions, proposing the BAPDF family of frameworks.

Explainable AI – or XAI – is a relatively new research area that hopes to'open the black box' on deep learning neural networks, complex algorithms and probabilistic graphical models. Artificial intelligence systems that can explain their decision making process in human terms are now the subject of intense research by software and cloud vendor Oracle, the company's senior vice-president of data-driven applications revealed to Computerworld yesterday. "One thing we don't make a big call out to is that we have a dedicated research team at Oracle called Oracle labs, mostly PhD computer scientists. And we have a lot of research going on that we don't tend to advertise very much in those research groups looking into that specific research area," said Clive Swan on the fringes of Oracle's Modern Business Experience event in Sydney. "It remains a big area of academic research. That problem is…very difficult academically to solve in some cases, and frankly varies from algorithm to algorithm."

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.

An inconsistent knowledge base can be abstracted as a set of arguments and a defeat relation among them. There can be more than one consistent way to evaluate such an argumentation graph. Collective argument evaluation is the problem of aggregating the opinions of multiple agents on how a given set of arguments should be evaluated. It is crucial not only to ensure that the outcome is logically consistent, but also satisfies measures of social optimality and immunity to strategic manipulation. This is because agents have their individual preferences about what the outcome ought to be. In the current paper, we analyze three previously introduced argument-based aggregation operators with respect to Pareto optimality and strategy proofness under different general classes of agent preferences. We highlight fundamental trade-offs between strategic manipulability and social optimality on one hand, and classical logical criteria on the other. Our results motivate further investigation into the relationship between social choice and argumentation theory. The results are also relevant for choosing an appropriate aggregation operator given the criteria that are considered more important, as well as the nature of agents' preferences.

Artificial Intelligence offers a lot of advantages for organisations by creating better and more efficient organisations, improving customer services with conversational AI and reducing a wide variety of risks in different industries. Although we are only at the beginning of the AI revolution that is upon us, we can already see that artificial intelligence will have a profound effect on our lives. As a result, AI governance and Explainable AI are becoming increasingly important, if we want to reap the benefits of artificial intelligence. Data governance and ethics have always been important and a few years ago, I developed ethical guidelines for organisations to follow, if they want to get started with big data. Such ethical guidelines are becoming more important, especially now since algorithms are taking over more and more decisions.

As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learning.

With the news that Zelda: Breath of the Wild will feature DLC, it has been met with a very mixed response by fans. In a recent interview, Nintendo's Bill Trinen does his best to explain the reasoning behind this decision. The main takeaway from Trinen's explanation (starting at 41:42 in the below video) is that Breath of the Wild is obviously a very large game in terms of its content and production. The DLC in that sense is a byproduct of that, as the complexity of developing an open world game means there is more left to do. So this DLC release, in part, is Nintendo's attempt at including absolutely everything it can from Breath of the Wild's production. In all honesty, this is entirely understandable.