"Let's not use society as a test-bed for technologies that we're not sure yet how they're going to change society," warned Carly Kind, director at the Ada Lovelace Institute, an artificial intelligence (AI) research body based in the U.K. "Let's try to think through some of these issues -- move slower and fix things, rather than move fast and break things." Kind was speaking as part of a recent panel discussion at Digital Frontrunners, a conference in Copenhagen that focused on the impact of AI and other next-gen technologies on society. The "move fast and break things" ethos embodied by Facebook's rise to internet dominance is one that has been borrowed by many a Silicon Valley startup: develop and swiftly ship an MVP (minimal viable product), iterate, learn from mistakes, and repeat. These principles are relatively harmless when it comes to developing a photo-sharing app, social network, or mobile messaging service, but in the 15 years since Facebook came to the fore, the technology industry has evolved into a very different beast. Large-scale data breaches are a near-daily occurrence, data-harvesting on an industrial level is threatening democracies, and artificial intelligence (AI) is now permeating just about every facet of society -- often to humans' chagrin.

The document, called'Malta the Ultimate AI Launchpad: A Strategy and Vision for Artificial Intelligence in Malta 2030', aims to ensure that benefits brought about by this next wave of innovation delivers benefits across all segments of Maltese society. Speaking at the Summit, Muscat highlighted that this year's summit doubled in size, from 4,500 people registering last year, to 10,000 this year. "This summit is one of the best opportunities for Malta to showcase our progress, our achievements and our dedication in areas of emerging technology to the world," Muscat said. "We made great strides compared to other countries as we were ambitious in our initiatives." He said other jurisdictions were sometimes reluctant to regulate tech, like Blockchain, unlike Malta.

Using publicly available traffic camera data in New York City, we quantify time-dependent patterns in aggregate pedestrian foot traffic. These patterns exhibit repeatable diurnal behaviors that differ for weekdays and weekends but are broadly consistent across neighborhoods in the borough of Manhattan. Weekday patterns contain a characteristic 3-peak structure with increased foot traffic around 9:00am, 12:00-1:00pm, and 5:00pm aligned with the "9-to-5" work day in which pedestrians are on the street during their morning commute, during lunch hour, and then during their evening commute. Weekend days do not show a peaked structure, but rather increase steadily until sunset. Our study period of June 28, 2017 to September 11, 2017 contains two holidays, the 4th of July and Labor Day, and their foot traffic patterns are quantitatively similar to weekend days despite the fact that they fell on weekdays. Projecting all days in our study period onto the weekday/weekend phase space (by regressing against the average weekday and weekend day) we find that Friday foot traffic can be represented as a mixture of both the 3-peak weekday structure and non-peaked weekend structure. We also show that anomalies in the foot traffic patterns can be used for detection of events and network-level disruptions. Finally, we show that clustering of foot traffic time series generates associations between cameras that are spatially aligned with Manhattan neighborhood boundaries indicating that foot traffic dynamics encode information about neighborhood character.

We're certainly not going to run out of potential applications for artificial intelligence in the legal field any time soon, particularly as developments in AI enable us to apply it to more sophisticated aspects of lawyers' work.It's not just new entrants that are responding to demand from innovators in the legal field. Current, well-known providers of legal tech are also venturing into AI-powered products, either through acquisition or developing their own tools. This is a sign that the industry is changing and companies that have traditionally provided services to lawyers are moving to meet this new demand.

This year, Europe's banks plan to make technology investments worth in aggregate $77 billion, according to consulting firm Celent. That compares with $105 billion for their U.S. rivals. Faster, more seamless trading systems have long been a priority, but tech spending has shifted across business lines and from back office to front office. It can cover everything from maintaining decades-old systems to cutting-edge artificial intelligence. Unfortunately, European lenders are more focused on patching old systems.

Researchers from the University of Cornell discovered that artificial intelligence systems designed to identify offensive "hate speech" flag comments purportedly made by minorities "at substantially higher rates" than remarks made by whites. Several universities maintain artificial intelligence systems designed to monitor social media websites and report users who post "hate speech." In a study published in May, researchers at Cornell discovered that systems "flag" tweets that likely come from black social media users more often, according to Campus Reform. The study's authors found that, according to the AI systems' definition of abusive speech, "tweets written in African-American English are abusive at substantially higher rates." The study also revealed that "black-aligned tweets" are "sexist at almost twice the rate of white-aligned tweets."

A lawyer would have to customize the type of information that need to be extracted from scanned documents, and the software will then convert it to searchable text. The software will summarize the extracted documents into a report that can be shared and downloaded in different formats.

Gordon's wine bar is reached through a discreet side-door, a few paces from the slipstream of London theatregoers and suited professionals powering towards their evening train. A steep staircase plunges visitors into a dimly lit cavern, lined with dusty champagne bottles and faded newspaper clippings, which appears to have had only minor refurbishment since it opened in 1890. "If Miss Havisham was in the licensing trade," an Evening Standard review once suggested, "this could have been the result." The bar's Dickensian gloom is a selling point for people embarking on affairs, and actors or politicians wanting a quiet drink – but also for pickpockets. When Simon Gordon took over the family business in the early 2000s, he would spend hours scrutinising the faces of the people who haunted his CCTV footage. "There was one guy who I almost felt I knew," he says. "He used to come down here the whole time and steal." The man vanished for a six-month stretch, but then reappeared, chubbier, apparently after a stint in jail.

Over the course of the next five days, we will be bringing you a wide range of content dedicated to the technology that has surely more potential than any other to transform government and public services. Today we will be making an introduction to artificial intelligence, looking at the journey the public sector has so far taken with the technology, and where it has led. Tomorrow we will profile some existing use cases, then later in the week we will move on to looking at the ethical, legal, and technical challenges, the respective roles of the various stakeholders and, finally, we will examine what the future may hold. AI Week – which is being run by PublicTechnology in association with UiPath – will bring our readers an array of features, interviews, analysis and case studies. From Wednesday, you will also be able to view an exclusive webinar discussion in which an expert panel of public- and private-sector representatives will debate all the major issues.

--Machine learning models are widely adopted in scenarios that directly affect people. The development of software systems based on these models raises societal and legal concerns, as their decisions may lead to the unfair treatment of individuals based on attributes like race or gender . Data preparation is key in any machine learning pipeline, but its effect on fairness is yet to be studied in detail. In this paper, we evaluate how the fairness and effectiveness of the learned models are affected by the removal of the sensitive attribute, the encoding of the categorical attributes, and instance selection methods (including cross-validators and random undersampling). We used the Adult Income and the German Credit Data datasets, which are widely studied and known to have fairness concerns. We applied each data preparation technique individually to analyse the difference in predictive performance and fairness, using statistical parity difference, disparate impact, and the normalised prejudice index. The results show that fairness is affected by transformations made to the training data, particularly in imbalanced datasets. Removing the sensitive attribute is insufficient to eliminate all the unfairness in the predictions, as expected, but it is key to achieve fairer models. Additionally, the standard random undersampling with respect to the true labels is sometimes more prejudicial than performing no random undersampling. Software systems based on machine learning (ML) are being used at an increasingly higher rate and on a multitude of scenarios that have a significant impact on people's lives. Their ubiquity raises several legal and societal concerns, as decisions based on the output of ML models may introduce or perpetuate historical bias against some individuals, based on their intrinsic characteristics, such as race, gender or age. The use of automated decision-making systems is often appealing due to the gains associated with it, and might even be perceived as a step towards the eradication of personal bias from the process. Nevertheless, many are the risks associated with a careless adoption of decisions supported by these systems. In this context, fairness emerges as a key property in terms of the reliability and trustworthiness of software systems based on ML. These receive nowadays increased attention from regulatory institutions, with the recently approved European Union General Data Protection Regulation (GDPR) demanding organisations to handle personal data in a privacy-preserving, fair and transparent manner [1].