In November, voters in Bellingham, Washington, passed a ballot measure banning government use of face recognition technology. It added to a streak of such laws that started with San Francisco in 2019 and now number around two dozen. The spread of such bans has inspired hope from campaigners and policy experts of a turn against an artificial intelligence technology that can lead to invasions of privacy or even wrongful arrest. Such feelings got a boost when Facebook unexpectedly announced on the day of the Bellingham vote that it would shutter its own face recognition system for identifying people in photos and videos, due to "growing societal concerns." Yet a few months earlier and about 100 miles from Bellingham, the commission that runs Seattle-Tacoma International Airport passed its own face recognition restrictions that leave airlines free to use the technology for functions like bag drop and check in, although it promised to provide some oversight and barred the technology's use by port police.

Federated learning, also known as collaborative learning, allows training models at scale on data that remains distributed on the devices where they are generated. Sensitive data remains with the owners of said data, where training is conducted, and a centralized training orchestrator of training only sees the contribution of each client through model updates. Federated learning doesn't guarantee privacy on its own (we'll touch on breaking and repairing privacy in federated learning systems later on), but it does make privacy possible. With the public and policy-makers becoming more aware of the data economy, demand for privacy-preserving machine learning is on the rise. As a result, data practices have been garnering increased scrutiny, and research on privacy-respecting tools like federated learning is increasingly active.

France's foremost privacy regulator has ordered Clearview AI to delete all its data relating to French citizens, as first reported by TechCrunch. In its announcement, the French agency CNIL argued that Clearview had violated the GDPR in collecting the data and violated various other data access rights in its processing and storage. As a result, CNIL is calling on Clearview to purge the data from its systems or face escalating fines as laid out by European privacy law. Clearview rose to prominence in 2020 after a New York Times investigation highlighted the company's massive data collection efforts. In particular, the company offered the unique ability to identify subjects by name, drawing on data scraped from public-facing social networks.

The rapid advancement of Artificial Intelligence (AI) technologies has already transformed business operations across the globe. From customer service chat-bots to adaptive cybersecurity, the applications of AI are nearly limitless. When properly designed, AI can help minimize paperwork, reduce costs, and drive better business decisions by increasing the predictive accuracy of future outcomes and mitigating the cognitive biases inherent in human decision making. In the financial services industry, AI has the potential to expand access to affordable credit for consumers and small businesses and combat fraud and financial crimes, but many financial institutions remain reluctant to deploy AI to its maximum potential without clear guidance from US regulatory agencies. Like many new technologies, the current AI landscape lacks a depth of established legal and regulatory precedent to rely on.

The maturity of artificial intelligence (AI) was evident this year, as the conversations in the industry shifted focus from deployment and innovation to ethics and legislation of algorithms. Building better data foundations to make the most of AI The UK government's national artificial intelligence strategy relies on businesses putting in place the foundations for better use of data – EY research highlights the challenges ahead. Making machine learning operational As artificial intelligence matures, IT departments will need to take control of change management and governance of data models. Self-regulation of AI is not an option The House of Lords Communications and Digital Committee recently took evidence from two experts, who were asked to share their thoughts on regulating artificial intelligence. Europe's proposed AI regulation falls short on protecting rights The European Commission's proposal for artificial intelligence regulation focuses on creating a risk-based, market-led approach replete with self-assessments, transparency procedures and technical standards, but critics warn it falls short.

As we approach the end of 2021, we wanted to share 13 of the most important AI papers of the year, as selected by the experts in the RE•WORK community who will be speaking at the Deep Learning Hybrid Summit in San Francisco in February 2022. These papers are free to access and cover a range of topics from computer vision to the way deep learning is helping to uncover the mysteries of space. You can join us and connect with our experts discussing trends and industry updates in the Deep Learning Hybrid Summit. Get your ticket here to join us in-person or virtually. Before joining Salesforce as Senior AI Product Manager, Vera Serdiukova built edge computing machine learning capabilities as a part of LG's Silicon Valley Lab Advanced AI Team.

In November 2021 the All-Party Parliamentary Group ("APPG") on the Future of Work ("Future of Work") published its report titled "The New Frontier: Artificial Intelligence at Work" (the "Report"). The Report follows the National AI Strategy (the "Strategy") released by the government in September and sets out to identify and resolve challenges posed by artificial intelligence ("AI") in the workplace through the development of a new regulatory framework. Whilst the proposed framework addresses AI in the workforce, we consider some of the principles could be applied across all sectors. The recommendations made by the Future of Work inform the wider debate about AI governance and regulation as part of the Strategy. APPGs are informal cross-party groups that have no official status in Parliament but are run by and for Members of the Commons and Lords, bringing together parliamentarians, industry and civil society. There is an Artificial Intelligence APPG, but the author of the Report is the Future of Work, an APPG which aims to "foster understanding of the challenges and opportunities of technology and the future of work".

The New York City Council voted 38-4 on November 10, 2021 to pass a bill that would require hiring vendors to conduct annual bias audits of artificial intelligence (AI) use in the city's processes and tools. Companies using AI-generated resources will be responsible for disclosing to job applicants how the technology was used in the hiring process, and must allow candidates options for alternative approaches such as having a person process their application instead. For the first time, a city the size of New York will impose fines for undisclosed or biased AI use, charging up to $1,500 per violation on employers and vendors. Lapsing into law without outgoing Mayor DeBlasio's signature, the legislation is now set to take effect in 2023. It is a telling move in how government has started to crack down on AI use in hiring processes and foreshadows what other cities may do to combat AI-generated bias and discrimination.

Individual human rights, privacy and the free press are under siege in an increasing number of countries around the world. Liberty itself is being challenged by authoritarian governments whose power is enhanced by the unethical use of social media, facial recognition technology and the ability to intercept private communications. Even in democracies, disinformation and doctored videos are often used on social media to undermine confidence in political leaders. Conspiracy theories abound, amplified by unregulated technology. As we have seen in the United States, democracy is threatened when a high percentage of citizens lose confidence in governance and the electoral system.

Human-performed annotation of sentences in legal documents is an important prerequisite to many machine learning based systems supporting legal tasks. Typically, the annotation is done sequentially, sentence by sentence, which is often time consuming and, hence, expensive. In this paper, we introduce a proof-of-concept system for annotating sentences "laterally." The approach is based on the observation that sentences that are similar in meaning often have the same label in terms of a particular type system. We use this observation in allowing annotators to quickly view and annotate sentences that are semantically similar to a given sentence, across an entire corpus of documents. Here, we present the interface of the system and empirically evaluate the approach. The experiments show that lateral annotation has the potential to make the annotation process quicker and more consistent.