A screen shows a demonstration of SenseTime Group's SenseVideo pedestrian and vehicle recognition system at the company's showroom in Beijing. Facial recognition supporters in the US often argue that the surveillance technology is reserved for the greatest risks -- to help deal with violent crimes, terrorist threats and human trafficking. And while it's still often used for petty crimes like shoplifting, stealing $12 worth of goods or selling $50 worth of drugs, its use in the US still looks tame compared with how widely deployed facial recognition has been in China. A database leak in 2019 gave a glimpse of how pervasive China's surveillance tools are -- with more than 6.8 million records from a single day, taken from cameras positioned around hotels, parks, tourism spots and mosques, logging details on people as young as 9 days old. The Chinese government is accused of using facial recognition to commit atrocities against Uyghur Muslims, relying on the technology to carry out "the largest mass incarceration of a minority population in the world today."

The use of facial recognition technology by South Wales police broke race and sex equalities law and breached privacy rights because the force did not apply proper safeguards, the court of appeal has ruled. The critical judgment came in a case brought by Ed Bridges, a civil liberties campaigner, who was scanned by the police software in Cardiff in 2017 and 2018. He argued that capturing of thousands of faces was indiscriminate. Bridges' case had previously been rejected by the high court, but the court of appeal ruled in his favour on three counts, in a significant test case for how the controversial technology is applied in practice by police. But the appeal court held that Bridges' right to privacy, under article 8 of the European convention on human rights, was breached because there was "too broad a discretion" left to police officers as to who to put on its watchlist of suspects.

Human rights organization Liberty is claiming a win in its native Britain after a court ruled that police trials of facial recognition technology violated privacy laws. The Court of Appeal ruled that the use of automatic facial recognition systems unfairly impacted claimant Ed Bridges' right to a private life. Judges added that there were issues around how people's personal data was being processed, and said that the trials should be halted for now. The court also found that the South Wales Police (SWP) had not done enough to satisfy itself that facial recognition technology was not unbiased. A spokesperson for SWP told the BBC that it would not be appealing the judgment, but Chief Constable Matt Jukes said that the force will find a way to "work with" the judgment.

The use of automatic facial recognition (AFR) technology by South Wales Police is unlawful, the Court of Appeal has ruled. It follows a legal challenge brought by civil rights group Liberty and Ed Bridges, 37, from Cardiff. But the court also found its use was proportionate interference with human rights as the benefits outweighed the impact on Mr Bridges. South Wales Police said it would not be appealing the findings. Mr Bridges had said being identified by AFR caused him distress.

The world must come together to take action on killer robots, according to a new report. There is increasing agreement among various countries that fully autonomous weapons should be banned to avoid the creation of such killer robots, the new report warns. It would be "unacceptable" if weapons systems are able to select and kill targets without human oversight, the researchers warn. The research by Human Rights Watch said 30 countries had now expressed a desire for an international treaty introduced which says human control must be retained over the use of force. The new report, "Stopping Killer Robots: Country Positions on Banning Fully Autonomous Weapons and Retaining Human Control", reviews the policies of 97 countries that have publicly discussed killer robots since 2013.

As part of a perennial project, our team is actively engaged in developing new synthetic assistant (SA) technologies to assist in training combat medics and medical first responders. It is critical that medical first responders are well trained to deal with emergencies more effectively. This would require real-time monitoring and feedback for each trainee. Therefore, we introduced a voice-based SA to augment the training process of medical first responders and enhance their performance in the field. The potential benefits of SAs include a reduction in training costs and enhanced monitoring mechanisms. Despite the increased usage of voice-based personal assistants (PAs) in day-to-day life, the associated effects are commonly neglected for a study of human factors. Therefore, this paper focuses on performance analysis of the developed voice-based SA in emergency care provider training for a selected emergency treatment scenario. The research discussed in this paper follows design science in developing proposed technology; at length, we discussed architecture and development and presented working results of voice-based SA. The empirical testing was conducted on two groups as user studies using statistical analysis tools, one trained with conventional methods and the other with the help of SA. The statistical results demonstrated the amplification in training efficacy and performance of medical responders powered by SA. Furthermore, the paper also discusses the accuracy and time of task execution (t) and concludes with the guidelines for resolving the identified problems.

Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep neural networks in which participants' data remains on their own devices with only model updates being shared with a central server. However, the distributed nature of FL gives rise to new threats caused by potentially malicious participants. In this paper, we study targeted data poisoning attacks against FL systems in which a malicious subset of the participants aim to poison the global model by sending model updates derived from mislabeled data. We first demonstrate that such data poisoning attacks can cause substantial drops in classification accuracy and recall, even with a small percentage of malicious participants. We additionally show that the attacks can be targeted, i.e., they have a large negative impact only on classes that are under attack. We also study attack longevity in early/late round training, the impact of malicious participant availability, and the relationships between the two. Finally, we propose a defense strategy that can help identify malicious participants in FL to circumvent poisoning attacks, and demonstrate its effectiveness.

A newly published study by University of Michigan researchers shows facial recognition technology in schools presents multiple problems and has limited efficacy. Led by Shobita Parthasarathy, director of the university's Science, Technology, and Public Policy (STPP) program, the research say the technology isn't suited to security purposes and can actively promote racial discrimination, normalize surveillance, and erode privacy while institutionalizing inaccuracy and marginalizing non-conforming students. The study follows the New York legislature's passage of a moratorium on the use of facial recognition and other forms of biometric identification in schools until 2022. The bill, which came in response to the launch of facial recognition by the Lockport City School District, was among the first in the nation to explicitly regulate or ban use of the technology in schools. That development came after companies including Amazon, IBM, and Microsoft halted or ended the sale of facial recognition products in response to the first wave of Black Lives Matter protests in the U.S. The Michigan University study -- a part of STPP's Technology Assessment Project -- employs an analogical case comparison method to look at previous uses of security technology like CCTV cameras and metal detectors as well as biometric technologies and anticipate the implications of facial recognition.

One of the most important reasons business, especially consumer facing business, wants to have lots of data is to know as much about the market, us, as possible. Artificial intelligence (AI) has made that focus on customers more and more accurate. While business has been becoming more invasive, governments have begun to look at and pass regulations that begin to provide certain limits. Privacy matters to the electorate, and smart business looks at how to use data to find out information while remaining in compliance with regulatory rules. Almost ten years ago, Target created an algorithm that figured out if people were pregnant based on purchase patterns, and the company then sent coupons to the addresses of those customers.

Former Waymo and Uber self-driving car-whiz kid, Anthony Levandowski was sentenced last week to 18 months in federal prison for stealing trade secrets. Levandowski will also pay a $95,000 fine and $756,499.22 in restitution to Waymo. He co-founded Google's self-driving car program, now Waymo, in 2009 and served as the program's technical lead until January 2016, when he left to co-found self-driving truck start-up Otto. Seven months later Uber acquired Otto for $680M and named Levandowski the head of its self-driving car division. He was on top of the tech world. He appeared in Wired Magazine as the go-to voice in Silicon Valley for self-driving cars and LiDAR technology.