The adoption of machine learning in high-stakes applicatio ns such as healthcare and law has lagged in part because predictions are not accomp anied by explanations comprehensible to the domain user, who often holds ult imate responsibility for decisions and outcomes. In this paper, we propose an appr oach to generate such explanations in which training data is augmented to inc lude, in addition to features and labels, explanations elicited from domain use rs. A joint model is then learned to produce both labels and explanations from the inp ut features. This simple idea ensures that explanations are tailored to the compl exity expectations and domain knowledge of the consumer. Evaluation spans multipl e modeling techniques on a simple game dataset, an image dataset, and a chemi cal odor dataset, showing that our approach is generalizable across domains a nd algorithms. Results demonstrate that meaningful explanations can be reli ably taught to machine learning algorithms, and in some cases, improve modeling ac curacy.

There's no question that Artificial Intelligence (AI) is quickly becoming more prevalent in organisations today. As a result of the burgeoning adoption, AI systems are becoming more complex, and the reasoning behind AI decisions more intricate and less transparent. Machine learning (ML) in particular allows AI systems to develop new rule sets that become increasingly complex over time. Why is this an issue? In many circumstances, it probably isn't – if I get into my driverless car, arrive at my destination safe and on time, does it really matter which road it took to get there?

Upon closer inspection, the satellite had noticed that an area that should have been shrouded in forest, was now barren. Within hours, a call had been made to a global conservation group, who mounted a legal case against the logging companies operating in the area. That process, historically, could have taken months of observing and recording changes. What's more, in remote areas such as the Ussuri Taiga in Russia's Far East, policing illegal logging operations have historically had little impact on the extraction of timber. But thanks to artificial intelligence (AI) and satellites, the ability to observe and respond to changes has become much faster.

Artificial intelligence (AI) has become an area of strategic importance and a key driver of economic development. It can bring solutions to many societal challenges from treating diseases to minimising the environmental impact of farming. However, socio-economic, legal and ethical impacts have to be carefully addressed. It is essential to join forces in the European Union to stay at the forefront of this technological revolution, to ensure competitiveness and to shape the conditions for its development and use (ensuring respect of European values). The Commission is increasing its annual investments in AI by 70% under the research and innovation programme Horizon 2020.

Artificial Intelligence (AI) is basically intelligence demonstrated outside the human mind, essentially by machines. Machine Learning (ML) is a way of achieving AI and can be defined as the ability of computers to learn using statistical techniques without being specially programmed. Both the terms are symbiotic but also mutually exclusive in their own right with different definitions. We are not unfamiliar with the concept of AI, which has been time and again explored and exploited by popular media. Movies have gone as far as to show us a world dominated by AI-enabled machines and robots, and these movies, more often than not, have ended up portraying negative repercussions of an AI-enabled society. This has more or less shaped up the general feeling revolving around AI in the society.

Artificial intelligence software capable of interpreting images, matching faces and analysing patterns of communication is being piloted by UK police forces to speed up examination of mobile phones seized in crime investigations. Cellebrite, the Israeli-founded and now Japanese-owned company behind some of the software, claims a wider rollout would solve problems over failures to disclose crucial digital evidence that have led to the collapse of a series of rape trials and other prosecutions in the past year. However, the move by police has prompted concerns over privacy and the potential for software to introduce bias into processing of criminal evidence. As police and lawyers struggle to cope with the exponential rise in data volumes generated by phones and laptops in even routine crime cases, the hunt is on for a technological solution to handle increasingly unmanageable workloads. Some forces are understood to have backlogs of up to six months for examining downloaded mobile phone contents.

Artificial intelligence (AI) is disrupting every industry, and the recruitment market is no exception. By lowering the cost of prediction, AI offers cheaper, faster, more efficient ways to connect people to jobs, as well as the promise of unlocking human potential. This is a big opportunity. In a world where most people are unhappy with their careers and many organizations complain about their talent gaps - for example, a recent ManpowerGroup report noted that 40% of global companies are experiencing critical talent shortages, the highest figure in a decade - technology can help us bridge the gap between supply and demand and make the job market less inefficient, just like dating apps have managed in the market of love. However, as with any technological innovation it is important to understand the ethical implications of using AI for attracting and selecting employees. Even if AI can improve our ability to match people to the right jobs - and, if we are looking for the kind of evidence we have historically demanded from traditional hiring tools (i.e., peer-reviewed scientific journal articles), the jury is still out - we need to ensure that the use of AI in recruitment is ethical.

Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal "before/after" event knowledge across sentences in narrative stories. The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order. We explored narratology principles and built a weakly supervised approach that identifies 287k narrative paragraphs from three large text corpora. We then extracted rich temporal event knowledge from these narrative paragraphs. Such event knowledge is shown useful to improve temporal relation classification and outperform several recent neural network models on the narrative cloze task.

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.

Cyberwar may not feel like "real" war -- the kind we've known and loathed for eons and the very same we perversely reenact in video games. But some military and legal experts say cyberwar is as real as it gets. David Petraeus, a retired US General and (some say disgraced) former intelligence chief says the internet has created an entirely distinct domain of warfare, one which he calls "netwar." And that's the kind being waged by terrorists. Then there's another kind, and technically any hacker with enough computer skills can do it -- whatever the motivation.