Over the last 12 months or so there's been incredible excitement about artificial intelligence and all of the amazing things it can do for us--everything from driving cars to making pizza (super-cool video!). But -- and this is a big "but" -- artificial intelligence comes with many challenges, including trying to decipher what these models have learned, and thus their decision criteria. In my last post, I discussed how regulations such as Europe's General Data Protection Regulation will demand Explainable AI. This is a field of science that attempts to remove the black box and deliver AI performance while also providing an explanation as to the "how" and "why" a model derives its decisions. FICO has been pioneering Explainable AI for over 25 years; one of our recent Explainable AI patent filings replaced a patent awarded back in 1998.

Argumentation is an active area of modern artificial intelligence (AI) research, with connections to a range of fields, from computational complexity theory and knowledge representation and reasoning to philosophy and social sciences, as well as application-oriented work in domains such as legal reasoning, multi-agent systems, and decision support. Argumentation frameworks (AFs) of abstract argumentation have become the graph-based formal model of choice for many approaches to argumentation in AI, with semantics defining sets of jointly acceptable arguments, i.e., extensions. Understanding the dynamics of AFs has been recently recognized as an important topic in the study of argumentation in AI. In this work, we focus on the so-called extension enforcement problem in abstract argumentation as a recently proposed form of argumentation dynamics. We provide a nearly complete computational complexity map of argument-fixed extension enforcement under various major AF semantics, with results ranging from polynomial-time algorithms to completeness for the second level of the polynomial hierarchy. Complementing the complexity results, we propose algorithms for NP-hard extension enforcement based on constraint optimization under the maximum satisfiability (MaxSAT) paradigm. Going beyond NP, we propose novel MaxSAT-based counterexample-guided abstraction refinement procedures for the second-level complete problems and present empirical results on a prototype system constituting the first approach to extension enforcement in its generality.

The answer to questions of trust and bias in AI is largely seen in the focus on Explainable AI. Although traditionally viewed as "black boxes", AI and machine learning systems are not ontologically inscrutable.

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.