The Parliament has in a statement said that "the EU has fallen behind in the global race for tech leadership. "There is a risk that standards will be developed elsewhere, often by non-democratic actors, while MEPs believe the EU needs to act as a global standard-setter in AI", the Parliament said. The MEPs identified policy options that could unlock AI's potential in health, the environment and climate change, to help combat pandemics and global hunger, and enhance people's quality of life through personalised medicine. MEPs say that, combined with the necessary support infrastructure, education and training, AI can increase capital and labour productivity, innovation, sustainable growth and job creation. However, several studies show citizens' hesitation and sometimes fear when facing the potential of artificial intelligence. "The EU should not always regulate AI as a technology and the level of regulatory intervention should be proportionate to the type of risk associated with the ...

I am spending my last weeks on thinking about climate actions, sustainability and economical aspects of these. I listened to a lot of podcasts about these topics, and I have tens of notes from these podcasts. I decided to express my ideas about this topic. But no, it is not my turn to spread the ideas. Only thing I wrote for this article is headline and a couple of keywords.

Proofpoint has announced the launch of its Intelligent Compliance Platform. "We understand today's organisations 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, Compliance at Proofpoint. "The new Intelligent Compliance Platform vastly improves the detection of non-compliant communications and content while quickly pinpointing supervised insider risks." For efficiency, compliance and legal teams can reduce supervised communications content review time by up to 84%.

In this paper we present a general framework for estimating regression models subject to a user-defined level of fairness. We enforce fairness as a model selection step in which we choose the value of a ridge penalty to control the effect of sensitive attributes. We then estimate the parameters of the model conditional on the chosen penalty value. Our proposal is mathematically simple, with a solution that is partly in closed form, and produces estimates of the regression coefficients that are intuitive to interpret as a function of the level of fairness. Furthermore, it is easily extended to generalised linear models, kernelised regression models and other penalties; and it can accommodate multiple definitions of fairness. We compare our approach with the regression model from Komiyama et al. (2018), which implements a provably-optimal linear regression model; and with the fair models from Zafar et al. (2019). We evaluate these approaches empirically on six different data sets, and we find that our proposal provides better goodness of fit and better predictive accuracy for the same level of fairness. In addition, we highlight a source of bias in the original experimental evaluation in Komiyama et al. (2018).

The recent privacy leakage incidences and the more strict policy regulations demand a much higher standard of compliance for companies and mobile apps. However, such obligations also impose significant challenges on app developers for complying with these regulations that contain various perspectives, activities, and roles, especially for small companies and developers who are less experienced in this matter or with limited resources. To address these hurdles, we develop an automatic tool, NL2GDPR, which can generate policies from natural language descriptions from the developer while also ensuring the app's functionalities are compliant with General Data Protection Regulation (GDPR). NL2GDPR is developed by leveraging an information extraction tool, OIA (Open Information Annotation), developed by Sun et al. (2020); Wang et al. (2022b) from Baidu Cognitive Computing Lab. At the core, NL2GDPR is a privacy-centric information extraction model, appended with a GDPR policy finder and a policy generator. We perform a comprehensive study to grasp the challenges in extracting privacy-centric information and generating privacy policies, while exploiting optimizations for this specific task. With NL2GDPR, we can achieve 92.9%, 95.2%, and 98.4% accuracy in correctly identifying GDPR policies related to personal data storage, process, and share types, respectively. To the best of our knowledge, NL2GDPR is the first tool that allows a developer to automatically generate GDPR compliant policies, with only the need of entering the natural language for describing the app features. Note that other non-GDPR-related features might be integrated with the generated features to build a complex app.

Key technologies – including AI, machine learning, and digital video conferencing – are proven to improve the speed, effectiveness, and efficiency of lawyers, allowing them to focus on their core business. While technology will "never be a substitute for the application of judgment and ethics that lawyers deliver to their clients and was never meant to do so, it can free up resources and time for more complex, value-added work," Smith says, noting that those that choose to ignore this fact risk finding themselves at a competitive disadvantage. Backed by a panel of leading experts including Helen Voudouris, Director of Online Product Management, LexisNexis Canada Inc.; Charles E. Gluckstein, Managing Partner, Gluckstein Lawyers; Susan Wortzman, Partner, McCarty Tetrault LLP; Al Hounsell, Senior Innovation Lawyer, Norton Rose Fulbright Canada LLP; and Natalie Munroe, Chief, Osler Works - Transactional & Legal Operations, Osler, Hoskin & Harcourt LLP, the webinar will explore the latest technological trends and identify ways to thrive in an evolving legal industry. The panel is set to tackle topics such as the role of automation and AI in driving productivity, digitizing knowledge management, and ultimately how new technologies are forcing firms to rethink how they operate. "This webinar will be an excellent opportunity for lawyers to get real-world examples from practicing lawyers on how the newest technology was implemented, how processes are managed, and the benefits to the organization," Smith says.

Compounding the danger is the lack of any AI regulation. Instead, unaccountable technology conglomerates, such as Google and Meta, have assumed the roles of judge and jury in all things AI. They are silencing dissenting voices, including their own engineers who warn of the dangers. The world's failure to rein in the demon of AI--or rather, the crude technologies masquerading as such--should serve to be a profound warning. There is an even more powerful emerging technology with the potential to wreak havoc, especially if it is combined with AI: quantum computing.

Can an artificial intelligence (AI) system be a named inventor on a United States patent? No, says a federal appeals court in a decision issued earlier this month. The case, Thaler v. Vidal, arose from two patent applications filed in 2019 by Stephen Thaler, naming an AI system he calls DABUS (for "Device for the Autonomous Bootstrapping of Unified Sentience") as the "inventor." After the U.S. Patent and Trademark Office (PTO) informed Thaler that the applications were incomplete because they did not list a human inventor, he filed a complaint in a federal district court in Virginia. In September 2021, that court ruled against Thaler, citing "the overwhelming evidence that Congress intended to limit the definition of'inventor' to natural persons."

Vivek Wadhwa and Mauritz Kop recently penned an op-ed urging governments around the world to get ahead of the threat posed by the emerging technology known as quantum computing. They even went so far as to title their article "Why Quantum Computing is Even More Dangerous Than Artificial Intelligence." Up front: This one gets a very respectful hard-disagree from me. While I do believe that quantum computing does pose an existential threat to humanity, my reasons differ wildly from those proposed by Wadhwa and Kop. Wadhwa and Kop open their article with a description of AI's failures, potential misuse, and how the media's narrative has exacerbated the danger of AI before it settles on a powerful lead: The world's failure to rein in the demon of AI--or rather, the crude technologies masquerading as such--should serve to be a profound warning.

A new open source AI image generator capable of producing realistic pictures from any text prompt has seen stunningly swift uptake in its first week. Stability AI's Stable Diffusion, high fidelity but capable of being run on off-the-shelf consumer hardware, is now in use by art generator services like Artbreeder, Pixelz.ai and more. But the model's unfiltered nature means not all the use has been completely above board. For the most part, the use cases have been above board. For example, NovelAI has been experimenting with Stable Diffusion to produce art that can accompany the AI-generated stories created by users on its platform.