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Scientists combat anti-Semitism with artificial intelligence – IAM Network

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BERLIN (AP) -- An international team of scientists have joined forces to combat the spread of anti-Semitism online with the help of artificial intelligence. The Alfred Landecker Foundation, which supports the team, said Monday that the project named Decoding Anti-Semitism includes discourse analysts, computational linguists and historians. They will develop a "highly complex, AI-driven approach to identifying online anti-Semitism." The team includes researchers from Berlin's Technical University, King's College in London and other scientific institutions in Europe and Israel. Computers will run through vast amounts of data and images that humans wouldn't be able to assess because of their sheer quantity.


How AI & Data Analytics Is Impacting Indian Legal System

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In a survey conducted by Gurugram-based BML Munjal University (School of Law) in July 2020, it was found that about 42% of lawyers believed that in the next 3 to 5 years as much as 20% of regular, day-to-day legal works could be performed with technologies such as artificial intelligence. The survey also found that about 94% of law practitioners favoured research and analytics as to the most desirable skills in young lawyers. Earlier this year, Chief Justice of India SA Bobde, in no uncertain terms, underlined that the Indian judiciary must equip itself with incorporating artificial intelligence in its system, especially in dealing with document management and cases of repetitive nature. With more industries and professional sectors embracing AI and data analytics, the legal industry, albeit in a limited way, is no exception. According to the 2020 report of the National Judicial Data Grid, over the last decade, 3.7 million cases were pending across various courts in India, including high courts, district and taluka courts.


Event Stream Processing: How Banks Can Overcome SQL and NoSQL Related Obstacles with Apache Kafka

#artificialintelligence

While getting to grips with open banking regulation, skyrocketing transaction volumes and expanding customer expectations, banks have been rolling out major transformations of data infrastructure and partnering with Silicon Valley's most innovative tech companies to rebuild the banking business around a central nervous system. This can also be labelled as event stream processing (ESP), which connects everything happening within the business - including applications and data systems - in real-time. ESP allows banks to respond to a series of data points – events - that are derived from a system that consistently creates data – the stream – to then leverage this data through aggregation, analytics, transformations, enrichment and ingestion. Further, ESP is instrumental where batch processing falls short and when action needs to be taken in real-time, rather than on static data or data at rest. However, handling a flow of continuously created data requires a special set of technologies.


Programming Fairness in Algorithms

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"Being good is easy, what is difficult is being just." "We need to defend the interests of those whom we've never met and never will." Note: This article is intended for a general audience to try and elucidate the complicated nature of unfairness in machine learning algorithms. As such, I have tried to explain concepts in an accessible way with minimal use of mathematics, in the hope that everyone can get something out of reading this. Supervised machine learning algorithms are inherently discriminatory. They are discriminatory in the sense that they use information embedded in the features of data to separate instances into distinct categories -- indeed, this is their designated purpose in life. This is reflected in the name for these algorithms which are often referred to as discriminative algorithms (splitting data into categories), in contrast to generative algorithms (generating data from a given category). When we use supervised machine learning, this "discrimination" is used as an aid to help us categorize our data into distinct categories within the data distribution, as illustrated below. Whilst this occurs when we apply discriminative algorithms -- such as support vector machines, forms of parametric regression (e.g.


Big Mother Can Be Just As Threatening As Big Brother

Slate

An expert on machine learning responds to Yudhanjaya Wijeratne's "The State Machine." The world of software has a long-held, pernicious myth that a system built from digital logic cannot have biases. A piece of code functions as an object of pure reason, devoid of emotion and all the messiness that entails. From this thesis flows an idea that has gained increasing traction in the worlds of both technology and science fiction: a perfectly rational system of governance built upon artificial intelligence. If software can't lie, and data can't inherently be wrong, then what could be more equitable and efficient than the rule of a machine-driven system?


Applications of Artificial Intelligence in Canadian Industries

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Artificial intelligence (AI) involves the simulation of human intelligence through programming machines or creating software to think similar to humans and mimic their actions. In other words, AI research seeks to develop technology that is capable of learning and problem solving the same way that a human would. Though the idea itself can be traced back to antiquity, AI has become increasingly popular in recent years, with ever-evolving applications across many Canadian industries. To this end, read on for IBISWorld's evaluation of how two up-and-coming ventures have the potential to affect the operations of different industries in Canada. In London, ON, a new AI tool called the Chronic Homelessness Artificial Intelligence model (CHAI) analyzes points, such as age, gender, family and shelter history, to assess the chance that a particular individual will become chronically homeless over the next six months.


AI, AI on the wall -- Who's the Fairest of them all?

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"A world perfectly fair in some dimensions would be horribly unfair in others." "Fairness" in Artificial Intelligence (AI) applications -- both as a concept and a practice -- is the focus of many organisations as they deploy new technologies for greater effectiveness and efficiencies. That machines are faster at processing large amounts of information and the notion that they are'more objective' than humans, appear to make them an obvious choice for progressivity and seemingly impartial actors in'fairer' decision-making. Yet, algorithmic based decisions have not come without their share of controversies -- Australia's recent'robo-debt' government intervention which wrongly pursued thousands of welfare recipients; the UK's'A-Levels fiasco' of downgrading graduating grades based on historical data, its controversial visa application streaming tool; and concerns about Clearview AI's facial recognition software for policing are raising new questions on the role of these technologies in society. Risk assessments are part of the fabric of modern society, but what we are dealing with here is not just'scaling up' human capacity for decision-making without the unwanted human biases and errors -- we are also extolling the'virtues of objectivity' under the guise of'fairness' (which is inherently subjective!) and failing to recognise the many inter-relationships that are being unraveled through the use of these algorithms in our daily lives.


Call for papers on law and artificial intelligence

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The book will consist of contributions based on some of Leiden University's SAILS research project's results and your contribution as a leading expert in this area. We invite contributions focusing on technological, legal, ethical, or social issues of the development and use of AI. Topics are not limited to those mentioned in the call for papers. This may concern best practices in regulating AI, in using AI in the legal domain, or any assessment frameworks for AI developments. All papers will be peer-reviewed by our program committee and other independent reviewers (where necessary) and will be published in an edited book with an ISBN.


Always Home Cam: Amazon's robot drone flying inside our homes seems like a bad idea

ZDNet

I actually had to double-check my calendar to make sure today wasn't April Fool's. Because watching the intro video of an indoor surveillance drone operated by Amazon seemed like just the sort of geeky joke you'd expect on April 1. But it isn't April Fools, and besides, Google has always been the one with the twisted sense of humor. Amazon has always been the one with the twisted sense of world domination. This was a serious press briefing.


EU challenges for an AI human-centric approach: lessons learnt from ECAI 2020

AIHub

During this period of progressive development and deployment of artificial intelligence, discussions around the ethical, legal, socio-economic and cultural implications of its use are increasing. What are the challenges and the strategy, and what are the values that Europe can bring to this domain? During the European Conference on AI (ECAI 2020), two special events in the format of panels discussed the challenges of AI made in the European Union, the shape of future research and industry, and the strategy to retain talent and compete with other world powers. This article collects some of the main messages from these two sessions, which included the participation of AI experts from leading European organisations and networks. Since the publication of European directives and guidance, such as the EC White Paper on AI and the Trustworthy AI Guidelines, Europe has been laying the foundation for the future vision of AI. The European strategy for AI builds on the well-known and accepted principles found in the Charter of Fundamental Rights of the European Commission and the Universal Declaration of Human Rights to define a human-centric approach, whose primary purpose is to enhance human capabilities and societal well-being.