The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box.

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

On May 25, 2018, the General Data Protection Regulation (GDPR) comes into effect across the EU, requiring sweeping changes to how organizations handle personal data. And GDPR standards have real teeth: For most violations, organizations have to pay a penalty of up to €20 million or 4 percent of global revenue, whichever is greater. With the Cambridge Analytica scandal fresh on people's minds, many hope that GDPR will become a model for a new standard of data privacy around the world. We've already heard some industry leaders calling for Facebook to apply GDPR standards to its business in non-EU countries, even though the law doesn't require it. But privacy is only one aspect of the debate around the use of data-driven systems.

Recent use includes the assassination with VX of Kim Jong Nam, half-brother of North Korean leader Kim Jong Un, in Kuala Lumpur airport in February 2017 and the attempted murder of Sergei Skripal, a 66-year-old former Russian double agent, and his daughter with a Novichok nerve agent in March in England.

The assignment of weights to attacks in a classical Argumentation Framework allows to compute semantics by taking into account the different importance of each argument. We represent a Weighted Argumentation Framework by a non-binary matrix, and we characterize the basic extensions (such as w-admissible, w-stable, w-complete) by analysing sub-blocks of this matrix. Also, we show how to reduce the matrix into another one of smaller size, that is equivalent to the original one for the determination of extensions. Furthermore, we provide two algorithms that allow to build incrementally w-grounded and w-preferred extensions starting from a w-admissible extension.

We study invariant local expansion operators for admissible sets in Abstract Argumentation Frameworks (AFs). Accordingly, we introduce in the future work section also the invariant local expansion for conflict free sets and we derive a definition of robustness for AFs in terms of the number of times such operators can be applied without producing any change in the chosen semantics.

Argumentative persuasion usually employs one of the three persuasion strategies: Ethos, Pathos or Logos. Several approaches have been proposed to model persuasive agents, however, none of them explored how the choice of a strategy impacts the mental states of the debaters and the argumentation process. We conducted a field experiment with real debaters to assess the impact of the mental engagement and emotions of the participants, as well as of the persuasiveness power of the arguments exchanged during the debate. Our results show that the Pathos strategy is the most effective in terms of mental engagement.

The overall team performance will obviously get much better as we click on at least two of those cylinders. When we get some of our guys back in the next week, we're confident our offense is going to perform better. It's incumbent upon us, with our bullpen, to get back to what we were doing last year. We're confident we have the guys down there to perform way better than we have.

There is an increasing demand for algorithms to explain their outcomes. So far, there is no method that explains the rankings produced by a ranking algorithm. To address this gap we propose LISTEN, a LISTwise ExplaiNer, to explain rankings produced by a ranking algorithm. To efficiently use LISTEN in production, we train a neural network to learn the underlying explanation space created by LISTEN; we call this model Q-LISTEN. We show that LISTEN produces faithful explanations and that Q-LISTEN is able to learn these explanations. Moreover, we show that LISTEN is safe to use in a real world environment: users of a news recommendation system do not behave significantly differently when they are exposed to explanations generated by LISTEN instead of manually generated explanations.

The notion that we should understand how artificial intelligences make decisions is gaining increasing currency. As we face a future in which important decisions affecting the course of our lives may be made by artificial intelligence (AI), the idea that we should understand how AIs make decisions is gaining increasing currency. Which hill to position a 20-year-old soldier on, who gets (or does not get) a home mortgage, which treatment a cancer patient receives … such decisions, and many more, already are being made based on an often unverifiable technology. "The problem is that not all AI approaches are created equal," says Jeff Nicholson, a vice president at Pega Systems Inc., makers of AI-based Customer Relationship Management (CRM) software. "Certain'black box' approaches to AI are opaque and simply cannot be explained."