A world-first report from Dr Allan McCay in the Law School scrutinises advances in neurotechnology and what they might mean for the law and the legal profession. Dr Allan McCay, a criminal law scholar at the University of Sydney Law School, has published the first substantial overview of neurotechnology and its implications for the law and the legal profession. Neurotechnologies are technologies that interact directly with the brain, or more broadly the nervous system, by monitoring and recording neural activity, and/or acting to influence it. Sometimes neurotechnology is implanted in the brain but it may also be in the form of a headset, wristband or helmet. The technology is already being used in health settings for the treatment of patients with Parkinson's and epilepsy and could be used in the future to monitor and treat schizophrenia, depression and anxiety.

Proofpoint, the Sunnyvale, Calif., cybersecurity and compliance company launched an intelligent compliance platform, offering enterprises regulatory compliance safeguards while simplifying corporate legal protection practices. The platform leverages Proofpoint's proprietary machine learning engine to provide business leaders with AI powered collection, clarify classification, detection, prevention, search, e-discovery, supervision and next gen predictive analytics while meeting complex compliance and informational governance obligations. The platform enables intuitive compliance, insider risk, and data management controls to classify or predict risks across a wide array of digital communications channels, files, e-mail and endpoint activities. Compliance, IT, information management and legal teams will gain greater visibility and access to information to the growing volumes of enterprise data, detecting and preventing corporate and regulatory risks in real-time. "We understand today's organizations are overwhelmed with growing volumes of data that are incredibly difficult to manage. For Compliance and Legal staff, that means having to manually search and review petabytes of messages or files from regulatory compliance, supervisory, or investigation review queues," said Kevin Leusing, senior vice president and general manager of compliance at Proofpoint, in a statement.

With the increasing digitisation of society and human communication, text data are becoming more important for research in the social and behavioural sciences (Gentzkow, Kelly, and Taddy 2019; Salganik 2019). Advances made in natural language processing (NLP) in particular have led to exciting insights derived from text data (e.g., on emotional responses to the pandemic (Kleinberg, Vegt, and Mozes 2020) or on the rhetoric around immigration in political speeches (Card et al. 2022); for an overview, see (Boyd and Schwartz 2021)). Importantly, the use of computational techniques to quantify and analyse text data has triggered a demand, especially for large datasets (often of several tens of thousands of documents) that can be harnessed for machine learning approaches (e.g., (Socher et al. 2013; Lewis et al. 2020)). That status quo of a need for larger datasets and an appetite to use text data for the study of social science phenomena has resulted in a dilemma: many of the important questions require targeted, primary data collection or access to potentially sensitive data. However, such data are hard to obtain, not because they do not exist but because sharing them is constrained by data protection regulations and ethical concerns. One potential consequence is that research activity may be biased toward topics for which suitable data is more readily available rather than those most important. One of the few viable solutions to this dilemma is automated text anonymisation; that is, the large-scale processing of text data so that individuals cannot be identified from the resulting output. Such a method would allow for the flow of sensitive data so that the staggering potential of text data can be exploited for scientific progress. With this paper and the tool it introduces, we seek to enable researchers to work with such sensitive data in a way that protects the privacy of individuals whilst retaining the usefulness of anonymised data for computational text analysis.

This report documents the history of research on AI rights and other moral consideration of artificial entities. It highlights key intellectual influences on this literature as well as research and academic discussion addressing the topic more directly. We find that researchers addressing AI rights have often seemed to be unaware of the work of colleagues whose interests overlap with their own. Academic interest in this topic has grown substantially in recent years; this reflects wider trends in academic research, but it seems that certain influential publications, the gradual, accumulating ubiquity of AI and robotic technology, and relevant news events may all have encouraged increased academic interest in this specific topic. We suggest four levers that, if pulled on in the future, might increase interest further: the adoption of publication strategies similar to those of the most successful previous contributors; increased engagement with adjacent academic fields and debates; the creation of specialized journals, conferences, and research institutions; and more exploration of legal rights for artificial entities.

Mobile photo editing app creator Lightricks is hopping on the AI-generated art bandwagon with its new "Text to Image" generator within its apps including Photoleap, which is known for its photo editing capabilities, and Motionleap, an app that can animate a still photo to make it look like it's in motion. As of today, users can now create AI-generated images via text prompts to share with their friends and social media followers. The new feature is the latest AI development for Lightricks, whose flagship Facetune app is used by many Instagram models to retouch their selfies and alter waistlines. The Israel-based startup is known for leveraging artificial intelligence technology to power a range of creative expression tools. With the new "Text to Image" feature, Lightricks is adopting technology that will help users not just edit photos or videos, but create art by leveraging to AI capabilities.

In the wake of the Black Lives Matter protests; companies that had previously defended their facial recognition products began to reconsider their approach. IBM announced that it would no longer offer facial recognition, Amazon said it would pause selling its facial recognition to police for a year, and Microsoft announced Thursday that it would not sell its facial recognition technology to police "until there is a federal law regulating the technology," the Washington Post reported. The message is now clear: Even tech giants like Amazon, IBM, and Google, which had earlier decided not to offer a facial recognition API, do not think this technology is ready for use by police. It's important to remember, though, that companies like IBM and Amazon are the tip of the facial recognition iceberg. Many U.S. police departments with facial recognition tools didn't buy them from big tech companies but rather from smaller contractors that claim to use "forensic" facial recognition algorithms developed by companies like NEC, Rank One, and Cognitec.

In 2020, a Canadian university employee named Ian Linkletter became increasingly alarmed by a new kind of technology that was exploding in use with the pandemic. It was meant to detect cheating by college and high-school students taking tests at home, and claimed to work by watching students' movements and analyzing sounds around them through their webcams and microphones to automatically flag suspicious behavior. So Linkletter accessed a section of the website of one of the anti-cheating companies, named Proctorio, intended only for instructors and administrators. He shared what he found on social media. Now Linkletter, who became a prominent critic of the technology, has been sued by the company. But he is not the only one.

It's August 2022, and by now you've no doubt read (or more likely seen) something about AI art by now. Whether it's random jokes made for Twitter or paintings that look like they were made by actual human beings, artificial intelligence's ability to create art has exploded onto the scene over the last few months, and while this has been great news for shitposts and fans of tech, it has also raised a number of important questions and concerns. If you haven't read or seen anything about the subject, AI art--or at least as it exists in the state we know it today--is, as Ahmed Elgammal writing in American Scientist so neatly puts it, made when "artists write algorithms not to follow a set of rules, but to'learn' a specific aesthetic by analyzing thousands of images. The algorithm then tries to generate new images in adherence to the aesthetics it has learned." Currently there are a handful of prominent platforms that people are using, with three of the most popular being Midjourney, Dall-E and Stable Diffusion.

New South Wales digital minister Victor Dominello has asked the state's information and privacy watchdogs to undertake a "scan of the AI and privacy landscape" in the wake of the robodebt royal commission launch. The move is a possible precursor to Australia's first Artificial Intelligence commissioner, a role the outgoing minister backed earlier this week. Mr Dominello told reporters on Monday a dedicated AI watchdog would be "good to see" as the next layer of independent oversight of government technologies and digital services. He said it likely won't arrive before he retires from politics early next year, but an AI commissioner is being explored and will be necessary to maintain public trust in government technology. "I want to make sure that there is oversight [and] there's checks and balances all the way because there will be failures," Mr Dominello said.

We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field, concerns about AGI, and ethics. Our results put concrete numbers to several controversies: For example, respondents are split almost exactly in half on questions about the importance of artificial general intelligence, whether language models understand language, and the necessity of linguistic structure and inductive bias for solving NLP problems. In addition, the survey posed meta-questions, asking respondents to predict the distribution of survey responses. This allows us not only to gain insight on the spectrum of beliefs held by NLP researchers, but also to uncover false sociological beliefs where the community's predictions don't match reality. We find such mismatches on a wide range of issues. Among other results, the community greatly overestimates its own belief in the usefulness of benchmarks and the potential for scaling to solve real-world problems, while underestimating its own belief in the importance of linguistic structure, inductive bias, and interdisciplinary science.