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Most Americans are recorded 238 TIMES a week by security cameras, study reveals

Daily Mail - Science & tech

The typical American is recorded by security cameras 238 times a week, according to a new report from Safety.com. That figure includes surveillance video taken at work, on the road, in stores and in the home. The study found that Americans are filmed 160 times while driving, as there are about an average of 20 cameras on a span of 29 miles. And the average employee has been spotted by surveillance cameras at 40 times a week. However, for those who frequently travel or work in highly patrolled areas the number of times they are captured on film skyrockets to more than 1,000 times a week.


AI Experts Rank Deepfakes and 19 Other AI-Based Crimes By Danger Level

#artificialintelligence

A new report published by University College London aimed to identify the many different ways that AI could potentially assist criminals over the next 15 years. The report had 31 different AI experts take 20 different methods of using AI to carry out crimes and rank these methods based on various factors. The AI experts ranked the crimes according to variables like how easy the crime would be to commit, the potential societal harm the crime could do, the amount of money a criminal could make, and how difficult the crime would be to stop. According to the results of the report, Deepfakes posed the greatest threat to law-abiding citizens and society generally, as their potential for exploitation by criminals and terrorists is high. The AI experts ranked deepfakes at the top of the list of potential AI threats because deepfakes are difficult to identify and counteract.


AI Researchers Condemn Predictive Crime Software, Citing Racial Bias, Flawed Methods

#artificialintelligence

A coalition of more than 1,000 researchers, academics, and experts in artificial intelligence condemned soon-to-be-published research claims of software purportedly able to predict whether one will become a criminal. A coalition of more than 1,000 researchers, academics, and experts in artificial intelligence condemned soon-to-be-published research claims of predictive crime software. The opponents sent an open letter to the publisher Springer, asking that it reconsider publishing the controversial research. Harrisburg University researchers Roozbeh Sadeghian and Jonathan W. Korn claim their facial recognition software can forecast whether a person will become a criminal, but the coalition expressed doubts on their findings, citing "unsound scientific premises, research, and methods, which numerous studies spanning our respective disciplines have debunked over the years." The letter from the coalition said, "The uncritical acceptance of default assumptions inevitably leads to discriminatory design in algorithmic systems, reproducing ideas which normalize social hierarchies and legitimize violence against marginalized groups."


It's The Data, Stupid! Why AI Might Get It Wrong.

#artificialintelligence

In the last few weeks there were a lot of discussions around clearview.ai. The company that scrapes image data from Facebook and other social sites and uses facial recognition to identify people. As the New York Times reported they claim that "the app helped identify [...] a person [..] whose face appeared in the mirror of someone else's gym photo". Much of the public outrage was about the privacy aspect. Little however was about the fact that those algorithms might just be wrong.


The Impact Of AI On Bank Transactions And Fraud Prevention

#artificialintelligence

In all banking systems, Identity and Access Management (IAM), Advertising Manager, and Identity Management (ID management) are essential since they allow us to know exactly who the person trying to enter a system is. Due to the leakage of information from user data, it is key to keep the information confidential, fully protected, because criminals are looking for this data to make possible attacks on the system or simply to steal information. Artificial intelligence allows the incorporation of a second identification factor for user access. In this model, there is a first identification factor pointing to what'is known', and then a second element seeks to verify with something that is with us, such as a token. Advances like these, minimize the risks of fraud for improper access or identity theft.


Here is why Face and Image Recognition Gaining Prominence

#artificialintelligence

Do you remember watching crime shows where investigating teams used to hire sketch artists to draw the image/face of criminal described by witnesses? And they would then hunt for the person to lock him up. But one might wonder today, are these tactics still common in detecting crime or criminals? With the rise in Artificial Intelligence enabled Face and Image Recognition technologies, the days of sketching criminal are long gone. The process of identifying or verifying the identity of a person using their face has made investigations a lot easier today.


What Is Adversarial Artificial Intelligence And Why Does It Matter? - Liwaiwai

#artificialintelligence

Artificial intelligence (AI) is quickly becoming a critical component in how government, business and citizens defend themselves against cyber attacks. Starting with technology designed to automate specific manual tasks, and advancing to machine learning using increasingly complex systems to parse data, breakthroughs in deep learning capabilities will become an integral part of the security agenda. Much attention is paid to how these capabilities are helping to build a defence posture. But how enemies might harness AI to drive a new generation of attack vectors, and how the community might respond, is often overlooked. Ultimately, the real danger of AI lies in how it will enable attackers.


The In-depth 2020 Guide to E-commerce Fraud Detection

#artificialintelligence

It is hard to underestimate the role of E-commerce in a world where most communications happen on the web and our virtual environment is full of advertisements with attractive products and services to buy. Meanwhile, it is obvious that many criminals are trying to take advantage of it, using scams and malware to compromise users' data. The level of E-commerce fraud is high, according to the statistics. With E-commerce sales estimated to reach $630 billion (or more) in 2020, an estimated $16 billion will be lost because of fraud. Amazon accounts for almost a third of all E-commerce deals in the United States; Amazon's sales numbers increase by about 15% to 20% each year. From 2018 to 2019, E-commerce spending increased by 57% -- the third time in U.S. history that the money spent shopping online exceeded the amount of money spent in brick-and-mortar stores. The Crowe UK and Centre for Counter Fraud Studies (CCFS) created Europe's most complete database of information on fraud, with data from more than 1,300 enterprises from almost every economic field.


Fraud Detection with Machine Learning Versus the Most Common Threats

#artificialintelligence

Machine Learning and Artificial Intelligence are offering an entirely new level of possibilities to businesses worldwide, one of those possibilities is Fraud Detection. Financial institutions and banks will never be the same with the opportunities technology offers to deal with criminal activities and fight internet fraud. Learn how it works in this post! The things people used to buy at shops years ago are now purchased online, no matter what they are: furniture, food, or clothes. As a result, the global E-Commerce market is rapidly rising and estimated to reach $4.9 trillion by 2021. This undoubtedly triggers members of the criminal world to find paths to victims' wallets through the Web. Federal, local, and state law enforcement agencies along with private organizations reported 3 million cases of identity theft in 2019. Money was lost in about 25% of these cases.


AI can predict which criminals may break laws again better than humans

#artificialintelligence

Computer algorithms can outperform people at predicting which criminals will get arrested again, a new study finds. Risk-assessment algorithms that forecast future crimes often help judges and parole boards decide who stays behind bars (SN: 9/6/17). But these systems have come under fire for exhibiting racial biases (SN: 3/8/17), and some research has given reason to doubt that algorithms are any better at predicting arrests than humans are. One 2018 study that pitted human volunteers against the risk-assessment tool COMPAS found that people predicted criminal reoffence about as well as the software (SN: 2/20/18). The new set of experiments confirms that humans predict repeat offenders about as well as algorithms when the people are given immediate feedback on the accuracy of their predications and when they are shown limited information about each criminal.