Meta is shutting down Facebook's controversial face recognition feature and deleting the face data collected from users through the social media network, citing "growing societal concerns". But privacy campaigners are concerned that the company hasn't been clear on whether the algorithms trained on that data will be deleted. Images uploaded to Facebook have been scanned by artificial intelligence (AI) tools since 2010, giving the uploader the option of "tagging" people in the image. Meta, then known as Facebook itself, attracted criticism when the feature first launched for failing to ask permission from users, and has since struggled to align it with local privacy laws. In 2012, the company switched off face recognition for people in the EU after a German data protection commissioner said that it violated European Union law – it returned in 2018 with an explicit opt-in requirement.

International Law and Health Related International Standard Setting Instruments play an important role in evolution and development of International Health Law. Conventional International Law is the primary International Legal Instrument through which International Organisations can extend International Cooperation for improving the Global Health Status as also reducing the Global Burden of Diseases. In the recent times, there has been an increase in the Inter-Governmental Organisations in the domain of Health Care. Let us take the instance of the growing diversity of International Law relating to Public Health wherein a broad array of Inter-Governmental Organisations including United Nations and its agencies and other related bodies are contributing to the development of International Health Law. The International Health Law is therefore emerging in a fragmented and amorphous manner.

The man responsible for ensuring the EU's institutions stick to its data protection laws believes Europe isn't ready for facial recognition tech that watches people in public. European "society is not ready," European Data Protection Supervisor (EDPS) Wojciech Wiewiórowski told POLITICO in an interview. The tech and its applications have divided Europe. The EU's proposed AI legislation bans most applications of remote biometric identification, such as facial recognition, in public places by law enforcement, but makes exceptions for fighting "serious" crime, which could include terrorism. Proponents of the technology, which include law enforcement and some security-minded governments, argue that the police need the technology to catch criminals.

Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal approach to settings where identification of causal effects hinges upon a set of mediators which unfortunately are not directly observed, however proxies of the hidden mediators are measured. Specifically, we establish (i) a new hidden front-door criterion which extends the classical front-door result to allow for hidden mediators for which proxies are available; (ii) We extend causal mediation analysis to identify direct and indirect causal effects under unconfoundedness conditions in a setting where the mediator in view is hidden, but error prone proxies of the latter are available. We view (i) and (ii) as important steps towards the practical application of front-door criteria and mediation analysis as mediators are almost always error prone and thus, the most one can hope for in practice is that our measurements are at best proxies of mediating mechanisms. Finally, we show that identification of certain causal effects remains possible even in settings where challenges in (i) and (ii) might co-exist.

Community detection refers to the problem of clustering the nodes of a network (either graph or hypergrah) into groups. Various algorithms are available for community detection and all these methods apply to uncensored networks. In practice, a network may has censored (or missing) values and it is shown that censored values have non-negligible effect on the structural properties of a network. In this paper, we study community detection in censored $m$-uniform hypergraph from information-theoretic point of view. We derive the information-theoretic threshold for exact recovery of the community structure. Besides, we propose a polynomial-time algorithm to exactly recover the community structure up to the threshold. The proposed algorithm consists of a spectral algorithm plus a refinement step. It is also interesting to study whether a single spectral algorithm without refinement achieves the threshold. To this end, we also explore the semi-definite relaxation algorithm and analyze its performance.

The technological advancement of Artificial Intelligence is impacting many areas of our lives like health care, manufacturing, entertainment, and farming. The development of AI, however, also comes with new problems. Bias, accuracy, privacy, and security issues in recent years made the general public worry about the ethical and legal consequences of AI. To address these concerns, many government bodies began to draft a legal framework around trustworthy AI so as to make it possible to regulate AI without hindering its development. Just as they did for data protection with GDPR (General Data Protection Regulations) in 2016, the EU led the world on Trustworthy AI by publishing Ethics Guidelines for Trustworthy AI (2019), White Paper on Artificial Intelligence -- A European approach to excellence and trust (2020), and A European Strategy for Data (2020).

Last November, the Chinese government ordered Ant Group, a business spun out of Alibaba that operates the ubiquitous Alipay mobile payments platform and other financial services, to cancel its hotly anticipated IPO at the last moment. Shortly after ride-hailing giant DiDi went ahead with an IPO in New York this summer despite government concerns, officials removed the company's app from Chinese app stores and ordered it to comply with an extensive cybersecurity review. Soon after, ByteDance, operator of wildly popular news and entertainment apps, as well as the short-video sensation TikTok outside of China, shelved its own plans for an IPO to comply with tighter government rules around data protection and security. So it's a little odd that two titans of China's artificial intelligence industry, SenseTime and Megvii, are proceeding with plans for IPOs seemingly unbothered, with listings on the Hong Kong and Shanghai stock exchanges, respectively. After a decade of unchecked growth, many Chinese tech firms now face a stark new reality, with canceled IPOs, stricter regulations, and hefty fines.

The decision comes as part of "a company-wide move to limit the use of facial recognition in our products," Jerome Pesenti, Facebook's VP of Artificial Intelligence, said on Tuesday. When the feature is discontinued, sometime "in the coming weeks," more than a billion Facebook users who have opted in will have their facial recognition templates deleted and they will not be automatically recognized, Pesenti explained. "Looking ahead, we still see facial recognition technology as a powerful tool, for example, for people needing to verify their identity, or to prevent fraud and impersonation," Pesenti wrote, but noted also that "the many specific instances where facial recognition can be helpful need to be weighed against growing concerns about the use of this technology as a whole." Authorities around the world are still working to provide clear rules on the use of the technology, and Facebook is committed to "working with the civil society groups and regulators who are leading this discussion," the executive added. Back in August, South Korean regulators fined Facebook for presuming consent to the feature for 200,000 users and not letting them opt out of face recognition.

Atlas and Spot won't have blank spaces on their resumes. The Boston Dynamics robots, famous for their YouTube parkour and dancing exploits, could land a position in a heartbeat, as can many non-human job-seekers, part of the wave of robot hires amid a human worker shortage. As the Associated Press reported, the pandemic ushered in these workplace changes. Companies are starting to automate service sector jobs, thanks to higher labor costs and the aforementioned worker shortages. Machines can do many tasks such as toss pizza dough, transport hospital linens, inspect gauges and sort goods.

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