The news: US Democratic lawmakers have introduced a bill that would ban the use of facial recognition technology by federal law enforcement agencies. Specifically, it would make it illegal for any federal agency or official to "acquire, possess, access, or use" biometric surveillance technology in the US. It would also require state and local law enforcement to bring in similar bans in order to receive federal funding. The Facial Recognition and Biometric Technology Moratorium Act was introduced by Senators Ed Markey of Massachusetts and Jeff Merkley of Oregon and Representatives Pramila Jayapal of Washington and Ayanna Pressley of Massachusetts. Seize the moment: The proposed law has arrived at a point when the police use of facial recognition technology is coming under increased scrutiny amid protests after the killing of George Floyd in late May.

In the last few months, millions of dollars have been stolen from unemployment systems during this time of immense pressure due to coronavirus-related claims. A skilled ring of international fraudsters has been submitting false unemployment claims for individuals that still have steady work. The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims. Payouts to these employed people are then redirected to money laundering accomplices who pass the money around to veil the illicit nature of the cash before depositing it into their own accounts. The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.

Lawmakers have proposed legislation that would indefinitely ban the use of facial recognition technology by law enforcement nationwide. The new bill comes after months of public concerns surrounding facial recognition's implications for data privacy, government surveillance and racial bias. The Facial Recognition and Biometric Technology Moratorium Act was proposed Thursday by Sens. Ed Markey (D-MA) and Jeff Merkley (D-OR), and Reps. While various cities have banned government use of the technology (with Boston this week becoming the tenth U.S. city to do so), the bill would be the first temporary ban on facial recognition technology ever enacted nationwide. The newly proposed bill would "prohibit biometric surveillance by the Federal Government without explicit statutory authorization and to withhold certain Federal public safety grants from State and local governments that engage in biometric surveillance."

Geneva – More than 50 countries, including Japan, South Korea and the European Union member states, have agreed common regulations for vehicles that can take over some driving functions, including having a mandatory black box, the U.N. announced Thursday. The binding rules on Automated Lane Keeping Systems (ALKS) will come into force in January 2021. The measures were adopted by the United Nations Economic Commission for Europe (UNECE) World Forum for Harmonization of Vehicle Regulations, which brings together 53 countries, not just in Europe but also in Africa and Asia. "This is the first binding international regulation on so-called'Level 3' vehicle automation," UNECE said in a statement. "The new regulation therefore marks an important step towards the wider deployment of automated vehicles to help realize a vision of safer, more sustainable mobility for all."

John Salmon: 'Machine learning provides high levels of accuracy' Hogan Lovells has launched an AI-based tool in collaboration with FTI Consulting to help businesses comply with new European Banking Authority (EBA) outsourcing requirements. The EBA Outsourcing Solution uses machine learning software to process outsourcing agreements and identify any terms that need updating in order to secure compliance with the new regulations. Banks, larger investment firms, payment and electronic money institutions all fall under the EBA's remit and are required to update existing outsourcing agreements by the end of 2021. The tool, which is powered by Kira's contracts review software, was developed by a Hogan Lovells team led by technology partner John Salmon and FTI Consulting managing director Ryan Drimalla. Salmon said: "By supplementing our machine learning software with human review quality control, we maintain high levels of accuracy to ensure only relevant legal issues are sent to our teams for varying or redrafting." It is the latest initiative to emerge from the firm's Legal Delivery Centre which offers FTI Consulting's'legal subject matter experts, project managers, contract analysis professionals, platform and data technologists and collection specialists' working alongside Hogan Lovells' lawyers with'alternative resourcing' provided by Elevate and Cognia Law.

Research in adversarial machine learning has shown how the performance of machine learning models can be seriously compromised by injecting even a small fraction of poisoning points into the training data. While the effects on model accuracy of such poisoning attacks have been widely studied, their potential effects on other model performance metrics remain to be evaluated. In this work, we introduce an optimization framework for poisoning attacks against algorithmic fairness, and develop a gradient-based poisoning attack aimed at introducing classification disparities among different groups in the data. We empirically show that our attack is effective not only in the white-box setting, in which the attacker has full access to the target model, but also in a more challenging black-box scenario in which the attacks are optimized against a substitute model and then transferred to the target model. We believe that our findings pave the way towards the definition of an entirely novel set of adversarial attacks targeting algorithmic fairness in different scenarios, and that investigating such vulnerabilities will help design more robust algorithms and countermeasures in the future.

Without an enterprise AI approach, prosecutors see fraud in the federal government's Paycheck Protection Program, they admit there are too many scams to count, let alone stop. Organized crime is scheming to take a growing cut of the emergency spending in the CARES Act. The rules of stimulus programs are constantly changing, making it hard to know who should and shouldn't obtain that financing or how they should spend it. This sounds like a job for enterprise artificial intelligence, and banks are indeed turning to it for help. But what qualifies as AI in quelling stimulus fraud, and how exactly would it work?

If you marched in recent Black Lives Matter protests in Atlanta, Los Angeles, Minneapolis or New York, there's a chance the mobile analytics company Mobilewalla gleaned demographic data from your cellphone use. Last week, Mobilewalla released a report detailing the race, age and gender breakdowns of individuals who participated in protests in those cities during the weekend of May 29th. What is especially disturbing is that protestors likely had no idea that the tech company was using location data harvested from their devices. As BuzzFeed News explains, Mobilewalla buys data from sources like advertisers, data brokers and ISPs. It uses AI to predict a person's demographics (race, age, gender, zip code, etc.) based on location data, device IDs and browser histories.

A coalition of AI researchers, data scientists, and sociologists has called on the academic world to stop publishing studies that claim to predict an individual's criminality using algorithms trained on data like facial scans and criminal statistics. Such work is not only scientifically illiterate, says the Coalition for Critical Technology, but perpetuates a cycle of prejudice against Black people and people of color. Numerous studies show the justice system treats these groups more harshly than white people, so any software trained on this data simply amplifies and entrenches societal bias and racism. "Let's be clear: there is no way to develop a system that can predict or identify'criminality' that is not racially biased -- because the category of'criminality' itself is racially biased," write the group. "Research of this nature -- and its accompanying claims to accuracy -- rest on the assumption that data regarding criminal arrest and conviction can serve as reliable, neutral indicators of underlying criminal activity. Yet these records are far from neutral."

A group of Democratic Senators and House representatives have introduced a bill that seeks to ban federal use of facial recognition technology. It follows an incident in which Detroit police wrongfully arrested a man after a facial recognition system incorrectly flagged him as a suspect. That's believed to be the first wrongful arrest of its kind in the US. Senators Ed Markey (Massachusetts) and Jeff Merkley (Oregon) authored the Facial Recognition and Biometric Technology Moratorium Act, which Reps. The aim of the bill is to "prohibit biometric surveillance by the federal government without explicit statutory authorization."