Machine learning algorithms are starting to exceed human performance in many narrow and specific domains, such as image recognition and certain types of medical diagnoses. We increasingly rely on machine learning algorithms to make decisions on a wide range of topics, from what we collectively spend billions of hours watching to who gets the job. But machine learning algorithms cannot explain the decisions they make. How can we justify putting these systems in charge of decisions that affect people's lives if we don't understand how they're arriving at those decisions? This desire to get more than raw numbers from machine learning algorithms has led to a renewed focus on explainable AI: algorithms that can make a decision or take an action, and tell you the reasons behind it.

We are living in an era that is showing massive growth in data and computing power. We have seen a lot of progress in machine learning and deep learning, but there is an ever-growing need for more intelligent, more explainable AI. Most people's perception of artificial intelligence boils down to either science fiction, or what we call conventional AI. The foundations of conventional AI are numerical techniques like data analytics, including statistical analysis, modeling, and machine learning. This has been the primary approach to AI over the past few decades with significant success by numerous companies in many industries.

Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the pred iction and behavior of machine learning models. An instance of expl anations are counterfactual explanations which provide an intuitive an d useful explanations of machine learning models. In this survey we review model-specific methods for efficientl y computing counterfactual explanations of many different machine learning models and propose methods for models that have not been considered in l iterature so far.

As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a human interpretable manner. It has recently become apparent that a high-fidelity explanation of a black box ML model may not accurately reflect the biases in the black box. As a consequence, explanations have the potential to mislead human users into trusting a problematic black box. In this work, we rigorously explore the notion of misleading explanations and how they influence user trust in black box models. More specifically, we propose a novel theoretical framework for understanding and generating misleading explanations, and carry out a user study with domain experts to demonstrate how these explanations can be used to mislead users. Our work is the first to empirically establish how user trust in black box models can be manipulated via misleading explanations.

Achieving explainable modelling is sometimes considered synonymous with restricting the choice of AI model to specific family of models that are considered inherently explainable. We will review this family of AI models. However, our discussion goes far beyond the conventional explainable model families and includes more recent and novel approaches such as joint prediction and explanation, hybrid models, and more. Ideally we can avoid the black-box problem from the beginning by developing a model that is explainable by design. The traditional approach to achieve explainable modelling is to adopt from a specific family of models that are considered explainable.

While the philosophical literature has extensively studied how decisions relate to arguments, reasons and justifications, decision theory almost entirely ignores the latter notions and rather focuses on preference and belief. In this article, we argue that decision theory can largely benefit from explicitly taking into account the stance that decision-makers take towards arguments and counter-arguments. To that end, we elaborate a formal framework aiming to integrate the role of arguments and argumentation in decision theory and decision aid. We start from a decision situation, where an individual requests decision support. In this context, we formally define, as a commendable basis for decision-aid, this individual's deliberated judgment, popularized by Rawls. We explain how models of deliberated judgment can be validated empirically. We then identify conditions upon which the existence of a valid model can be taken for granted, and analyze how these conditions can be relaxed. We then explore the significance of our proposed framework for decision aiding practice. We argue that our concept of deliberated judgment owes its normative credentials both to its normative foundations (the idea of rationality based on arguments) and to its reference to empirical reality (the stance that real, empirical individuals hold towards arguments and counter-arguments, on due reflection). We then highlight that our framework opens promising avenues for future research involving both philosophical and decision theoretic approaches, as well as empirical implementations.

In this Q&A on Explainable AI, Andrea Brennen speaks with In-Q-Tel's Peter Bronez about descriptive vs. prescriptive models, "white box" vs. "black box" explanation techniques, and why some models are easier to explain than others. Peter also discusses the reproducibility crisis in Psychology and why good experiment design is so important. Peter is a VP on the technical staff at IQT. Could you tell me about your experience with machine learning and AI? PETER: As an undergraduate, I studied econometrics and operations research, so my exposure to machine learning was in the context of designing models of the world that you could test mathematically -- basically, doing hypothesis testing using statistics. Afterwards, I worked at the Department of Defense and used a lot of the same techniques. From there, I went to the private sector and [worked on] social media and data mining in marketing applications, trying to create mathematical models to categorize people, activities, and messages in order to understand them better.

Z Advanced Computing, Inc. (ZAC), an AI (Artificial Intelligence) software startup, is developing its Smart Home product line through a paid-pilot for smart appliances for BSH Home Appliances, the largest manufacturer of home appliances in Europe and one of the largest in the world. BSH Home Appliances Corporation is a subsidiary of the Bosch Group, originally a joint venture between Robert Bosch GmbH and Siemens AG. ZAC Smart Home product line uses ZAC Explainable-AI Image Recognition. ZAC is the first to apply Explainable-AI in Machine Learning. "You cannot do this with other techniques, such as Deep Convolutional Neural Networks," said Dr. Saied Tadayon, CTO of ZAC.

In this paper, we propose a new method for computing general allocators directly from completeness conditions. A general allocator is an abstraction of all complete labelings for an argumentation framework. Any complete labeling is obtained from a general allocator by assigning logical constants to variables. We proved the existence of the general allocators in our previous work. However, the construction requires us to enumerate all complete labelings for the framework, which makes the computation prohibitively slow. The method proposed in this paper enables us to compute general allocators without enumerating complete labelings. It also provides the solutions of local allocation that yield semantics for subsets of the framework. We demonstrate two applications of general allocators, stability, and a new concept for frameworks, termed arity. Moreover, the method, including local allocation, is applicable to broad extensions of frameworks, such as argumentation frameworks with set-attacks, bipolar argumentation frameworks, and abstract dialectical frameworks.

Director of Ministry Intelligence at the American Bible Society Dr. John Farquhar Plake provides free bibles to Kanye fans. It's no secret Kanye West's "Jesus is King" album set the internet on fire over the past week. Fox News reported that Google searches for "Jesus" and "Christian beliefs" significantly spiked since the album's release on Oct. 25, and it has sparked both social media and real-life conversations among countless Christians and skeptics. After listening to the songs and watching several of West's interviews where he explains the reasoning behind his drastic change of behavior and newly professed "sonship" to the Kingdom of God, I'm convinced God is using him to inspire both believers and non-believers alike. In his appearance on "Jimmy Kimmel Live!" West was asked if he now considers himself a "Christian music artist."