Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions ( TED), that provides meaningful explanations that match the mental model of the consumer. We illustrate the generality and effectiveness of this approach with two different examples, resulting in highly accurate explanations with no loss of prediction accuracy for these two examples.

Medicine has provided the field of artificial intelligence with a plethora of challenging and appealing problems to be solved, particularly in clinical diagnosis ("given a set of signs collected from a patient, select the best diagnosis") and in therapy advice ("given an established diagnosis, select the best course of actions for treatment"). Artificial intelligence, in turn, has offered promising technologies for problem solving in the medical domain [7]. The field of oncology has proven to be particularly fit for modelling and analysis based on artificial intelligence, at least prospectively [5, 3], due to two major reasons: 1. Symptoms in oncology are frequently difficult to identify before later stages of the disease, and cancer can be treated most effectively if identified at early stages of development. Signs of the disease can be diffuse and require high expertise to be selected, collected and analysed. Hence, technologies that can highlight evidence of cancer at early stages are most welcome and challenging at the same time.

What's more, even AIs based on mechanisms inspired by human biology, such as neural networks, have only a distant relationship with biological neurons in the brain. NN are examples more of the importance of reinforcement and self-organisation of controller networks than any similarity with biology. The first, naive, approach to AI is to think that it is necessary to create a synthetic human, or a synthetic brain to produce cognition: in fact, cognition does not need to be anthropomorphic at all. Second attempt at a definition: "The ability of a machine to achieve performance equal to or better than certain human cognitive processes." This definition is based on the final outcome, without presupposing imitation of biological mechanisms.

Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds (often implicitly). We focus on the challenging real-world problem of structured narrative extraction in the materials science domain, where language is highly specialized and suitable annotated data is not publicly available. We propose an approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A reinforcement-learning agent completes the game by understanding and executing the procedure correctly, in a text-based simulated lab environment. The framework is intended to be more broadly applicable to other domain-specific and data-scarce settings. We conclude with a discussion of challenges and interesting potential extensions enabled by the agent-based perspective.

The topic of causal explanation in artificial intelligence has gathered interest in recent years as researchers and practitioners aim to increase trust and understanding of intelligent decision-making and action. While different sub-fields have looked into this problem with a sub-field-specific view, there are few models that aim to capture explanation in AI more generally. One general model is based on structural causal models. It defines an explanation as a fact that, if found to be true, would constitute an actual cause of a specific event. However, research in philosophy and social sciences shows that explanations are contrastive: that is, when people ask for an explanation of an event -- the fact --- they (sometimes implicitly) are asking for an explanation relative to some contrast case; that is, "Why P rather than Q?". In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical AI problems: classification and planning. We believe that this model can be used to define contrastive explanation of other subfield-specific AI models.

In this work, we put into question the effectiveness of the evaluation methods currently used to measure the performance of latent factor models for the task of knowledge base completion. We argue that by focusing on a small subset of possible facts in the knowledge base, current evaluation practices are better suited for question answering tasks, rather than knowledge base completion, where it is also important to avoid the addition of incorrect facts into the knowledge base. We illustrate our point by showing how models with limited expressiveness achieve state-of-the-art performance, even while adding many incorrect (even nonsensical) facts to a knowledge base. Finally, we show that when using a simple evaluation procedure designed to also penalize the addition of incorrect facts, the general and relative performance of all models looks very different than previously seen. This indicates the need for more powerful latent factor models for the task of knowledge base completion.

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly.

Nowadays all industrial sectors are increasingly faced with the explosion in the amount of data. Therefore, it raises the question of the efficient use of this large amount of data. In this research work, we are concerned with process and product traceability data. In some sectors (e.g. pharmaceutical and agro-food), the collection and storage of these data are required. Beyond this constraint (regulatory and / or contractual), we are interested in the use of these data for continuous improvements of industrial performances. Two research axes were identified: product recall and responsiveness towards production hazards. For the first axis, a procedure for product recall exploiting traceability data will be propose. The development of detection and prognosis functions combining process and product data is envisaged for the second axis.

These devices, particularly the smart mobile phones have transformed over a period of time from merely communication tools to smart and highly personal devices enabling to assist the users in their variety of day-to-day situations in their daily life. In the real word, users' interest on "Mobile Phones" is more and more than other platforms like "Desktop Computer" or "Tablet Computer" over time [36]. People use mobile phones not only for voice communication between individuals but also for various activities such as applications (mobile apps) using, Internet browsing, emailing, using online social network, instant messaging etc [28]. Recent advances in the sensing capabilities of smart mobile phones make them enable to collect the rich contextual information and users' various activity records with mobile phones through the device logs. These historical mobile phone data are simply as the collection of the past contexts and user's activities with the mobile phones for these past contexts. These are phone call logs [39] having phone call activities, app usages logs [45] having various mobile application usages, mobile phone notification logs [22] having the responses with various notifications from different applications, web logs [13] having Internet browsing activities of the mobile phone users. The main characteristic of such kind of phone log data is that it contains the actual diverse activities of the users in different contexts in their real world life. Modeling smartphone user behaviors by developing various computational machine learning methods (rule-based learning) in order to analyze different behavioral patterns in different contexts, and eventually predict the next behaviors or detect strange behaviors utilizing such mobile phone data, can be used for build- 2 Iqbal H. Sarker*

There's no foolproof way to know if someone's verbally telling lies, but scientists have developed a tool that seems remarkably accurate at judging written falsehoods. Using machine learning and text analysis, they've been able to identify false robbery reports with such accuracy that the tool is now being rolled out to police stations across Spain. Computer scientists from Cardiff University and Charles III University of Madrid developed the tool, called VeriPol, specifically to focus on robbery reports. In their paper, published in the journal Knowledge-Based Systems earlier this year, they describe how they trained a machine-learning model on more than 1000 police robbery reports from Spanish National Police, including those that were known to be false. A pilot study in Murcia and Malaga in June 2017 found that, once VeriPol identified a report as having a high probability of being false, 83% of these cases were closed after the claimants faced further questioning.