MPs have called for a ban on two Chinese CCTV systems that are used by councils, schools, and police forces across the UK. A group of 67 MPs and Lords including Lib Dem leader Sir Ed Davey and four ex-Conservative ministers is urging the government to ban the sale and use of Hikvision and Dahua cameras. The calls come amid concerns the CCTV cameras can recognise faces, eavesdrop on conversations, and judge people's moods. The Uyghurs, a predominantly Muslim ethnic group, are the majority population in Xinjiang. More than a million Uyghurs and other minorities are estimated to have been detained at camps in Xinjiang, where allegations of torture, forced labour and sexual abuse have emerged.

In 2018, the European Union introduced the General Data Protection Regulation (GDPR) which has clauses that impact AI – notably text indicating a "right to explanation" – an area that affects AI algorithms and has been the subject of much debate since its introduction. Elsewhere, local regulations have been attempted, ranging from bans on the use of certain types of AI (such as facial recognition), to committees to examine the fairness of algorithms used in resource allocation. The exact criterion and specifics of the law are still being debated, with exceptions and loopholes having been identified by a number of institutions. There are many regulations in development, and to make things even more complicated, they differ in their geographical or industry scope and in their targets. Forming a cohesive practice will make it easier to see these regulations as connected entities that are addressed together.

Artificial intelligence has made a big impact on our lives over the past few years. From online shopping bots to voice assistants like Alexa, we're now more connected than ever before thanks to AI technology. But how can we use AI in other areas? In this article, I will look at how artificial intelligence (AI) can revolutionize the world of art and design by creating new works from scratch and improving existing ones. GANs, or generative adversarial networks, are a type of deep learning model that can generate images, text and other types of data.

Scientists are looking for a way to predict crime using, you guessed it, artificial intelligence. There are loads of studies that show using AI to predict crime results in consistently racist outcomes. For instance, one AI crime prediction model that the Chicago Police Department tried out in 2016 tried to get rid of its racist biases but had the opposite effect. It used a model to predict who might be most at risk of being involved in a shooting, but 56% of 20-29 year old Black men in the city appeared on the list. Despite it all, scientists are still trying to use the tool to find out when, and where, crime might occur.

Mediation choreography workshop is a practice and training session to improve bodily confidence, comfort, awareness and dialogue during social interactions focused on the self-presentation development in children and youngsters. The workshop teaches non-verbal mediation and dialogue: Body postures, Expressions and Form aggregations inspired from the museum sculptures. During the workshop, mediation choreography experts help young participants to improvise and feel the quality of our bodily perception, performance and presentation. Disclaimer: The description of Mediation choreography is my understanding as a foreigner in the French city, Strasbourg. I participated in the workshop among native speakers.

Security experts are on the alert for the next evolution of social engineering in business settings: deepfake employment interviews. The latest trend offers a glimpse into the future arsenal of criminals who use convincing, faked personae against business users to steal data and commit fraud. The concern comes following a new advisory this week from the FBI Internet Crime Complaint Center (IC3), which warned of increased activity from fraudsters trying to game the online interview process for remote-work positions. The advisory said that criminals are using a combination of deepfake videos and stolen personal data to misrepresent themselves and gain employment in a range of work-from-home positions that include information technology, computer programming, database maintenance, and software-related job functions. Federal law-enforcement officials said in the advisory that they've received a rash of complaints from businesses.

AI's little guys are getting into the Washington influence game. Tech giants and defense contractors have long dominated AI lobbying, seeking both money and favorable rules. And while the largest companies still dominate the debate, pending legislation in Congress aimed at getting ahead of China on innovation, along with proposed bills on data privacy, have caused a spike in lobbying by smaller AI players. A number of companies focused on robotics, drones and self-driving cars are all setting up their own Washington influence machines, positioning them to shape the future of AI policy to their liking. A lot of it is spurred by one major piece of legislation: The Bipartisan Innovation Act, commonly referred to as USICA -- an acronym for its previous title, and its goal to out-innovate China.

The tech media outlet Toolbox featured the views of 10 experts on "How will AI evolve in the next year?" Edge technology that experts should pay attention to next year was also intensively discussed. The first place was occupied by MIT's Neil Thompson research team featuring an article on the cost of energy to train deep learning systems. As a result of analyzing the improvements of the image classifier, the research team found that "to cut the error rate in half, it can be expected that 500 times more computational resources are required." "The rising cost requires researchers to devise more efficient ways to solve these problems, otherwise we will give up research on these problems, and progress will be difficult," he said.

In 1869, the English judge Baron Bramwell rejected the idea that "because the world gets wiser as it gets older, therefore it was foolish before." Financial regulators should adopt this same reasoning when reviewing financial institutions' efforts to make their lending practices fairer using advanced technology like artificial intelligence and machine learning. If regulators don't, they risk holding back progress by incentivizing financial institutions to stick with the status quo rather than actively look for ways to make lending more inclusive. The simple, but powerful, concept articulated by Bramwell underpins a central public policy pillar: You can't use evidence that someone improved something against them to prove wrongdoing. In law this is called the doctrine of "subsequent remedial measures." It incentivizes people to continually improve products, experiences and outcomes without fear that their efforts will be used against them.

The California Fair Employment and Housing Council (FEHC) recently took a major step towards regulating the use of artificial intelligence (AI) and machine learning (ML) in connection with employment decision-making. On March 15, 2022, the FEHC published Draft Modifications to Employment Regulations Regarding Automated-Decision Systems, which specifically incorporate the use of "automated-decision systems" in existing rules regulating employment and hiring practices in California. The draft regulations seek to make unlawful the use of automated-decision systems that "screen out or tend to screen out" applicants or employees (or classes of applicants or employees) on the basis of a protected characteristic, unless shown to be job-related and consistent with business necessity. The draft regulations also contain significant and burdensome recordkeeping requirements. Before the proposed regulations take effect, they will be subject to a 45-day public comment period (which has not yet commenced) before FEHC can move toward a final rulemaking.