Technological advances in information sharing have raised concerns about data protection. Privacy policies contain privacy-related requirements about how the personal data of individuals will be handled by an organization or a software system (e.g., a web service or an app). In Europe, privacy policies are subject to compliance with the General Data Protection Regulation (GDPR). A prerequisite for GDPR compliance checking is to verify whether the content of a privacy policy is complete according to the provisions of GDPR. Incomplete privacy policies might result in large fines on violating organization as well as incomplete privacy-related software specifications. Manual completeness checking is both time-consuming and error-prone. In this paper, we propose AI-based automation for the completeness checking of privacy policies. Through systematic qualitative methods, we first build two artifacts to characterize the privacy-related provisions of GDPR, namely a conceptual model and a set of completeness criteria. Then, we develop an automated solution on top of these artifacts by leveraging a combination of natural language processing and supervised machine learning. Specifically, we identify the GDPR-relevant information content in privacy policies and subsequently check them against the completeness criteria. To evaluate our approach, we collected 234 real privacy policies from the fund industry. Over a set of 48 unseen privacy policies, our approach detected 300 of the total of 334 violations of some completeness criteria correctly, while producing 23 false positives. The approach thus has a precision of 92.9% and recall of 89.8%. Compared to a baseline that applies keyword search only, our approach results in an improvement of 24.5% in precision and 38% in recall.

Fair representation learning is an attractive approach that promises fairness of downstream predictors by encoding sensitive data. Unfortunately, recent work has shown that strong adversarial predictors can still exhibit unfairness by recovering sensitive attributes from these representations. In this work, we present Fair Normalizing Flows (FNF), a new approach offering more rigorous fairness guarantees for learned representations. Specifically, we consider a practical setting where we can estimate the probability density for sensitive groups. The key idea is to model the encoder as a normalizing flow trained to minimize the statistical distance between the latent representations of different groups. The main advantage of FNF is that its exact likelihood computation allows us to obtain guarantees on the maximum unfairness of any potentially adversarial downstream predictor. We experimentally demonstrate the effectiveness of FNF in enforcing various group fairness notions, as well as other attractive properties such as interpretability and transfer learning, on a variety of challenging real-world datasets.

The EU is facing a backlash over new AI rules that allow for limited use of facial recognition by authorities -- with opponents warning the carveouts could usher in a new age of biometric surveillance. A coalition of digital rights and consumer protection groups across the globe, including Latin America, Africa and Asia are calling for a global ban on biometric recognition technologies that enable mass and discriminatory surveillance by both governments and corporations. In an open letter, 170 signatories in 55 countries argue that the use of technologies like facial recognition in public places goes against human rights and civil liberties. "It shows that organizations, groups, people, activists, technologists around the world who are concerned with human rights, agree to this call," said Daniel Leufer of U.S. digital rights group Access Now, which co-authored the letter. The use of facial recognition technology is becoming widespread.

Keith E. Sonderling is a commissioner for the U.S. Equal Employment Opportunity Commission. Views are the author's own. It's no secret that online advertising is big business. In 2019, digital ad spending in the United States surpassed traditional ad spending for the first time, and by 2023, digital ad spending will all but eclipse it. It's easy to understand why. Digital marketing is now the most effective way for advertisers to reach an enormous segment of the population -- and social media platforms have capitalized on this to the tune of billions of dollars.

The European Union has been digging into the competition implications of AI-powered voice assistants and other Internet of Things (IoT) connected technologies for almost a year. Today it's put out a first report discussing potential concerns that EU lawmakers say will help inform their wider digital policymaking in the coming years. A major piece of EU legislation introduced at the back of last year is already set to apply ex ante regulations to so-called'gatekeeper' platforms operating in the region, with a list of business practice'dos and don'ts' for powerful, intermediating platforms being baked into the forthcoming pan-EU Digital Services Act. The bloc's competition chief, Margrethe Vestager, has also had her eye on voice assistant AI technologies for a while -- raising concerns about the challenges being posed for user choice as far back as 2019, when she said her department was "trying to figure out how access to data will change the marketplace". The Commission took a concrete step last July when it announced a sectoral inquiry to examine IoT competition concerns in detail.

Law360 (June 7, 2021, 7:27 PM EDT) -- Artificial intelligence-focused startup WalkMe Ltd. launched plans on Monday for an initial public offering estimated to raise $282 million, one of four companies to bolster June's growing IPO pipeline with offerings that could raise $659 million combined, guided by nine law firms total. WalkMe, which also has offices in San Francisco, is advised by Latham & Watkins LLP on U.S. legal matters and by Meitar on Israeli legal matters. Stay ahead of the curve You have to know what's happening with clients, competitors, practice areas, and industries. Law360 (June 7, 2021, 7:27 PM EDT) -- Artificial intelligence-focused startup WalkMe Ltd. launched plans on Monday for an initial public offering estimated to raise $282 million, one of four companies to bolster June's growing IPO pipeline with offerings that could raise $659 million combined, guided by nine law firms total. WalkMe, which also has offices in San Francisco, is advised by Latham & Watkins LLP on U.S. legal matters and by Meitar on Israeli legal matters.

Among different ML models, Deep Neural Networks (DNNs) [130] are well-known and widely used for their powerful representation learning from high-dimensional data such as images, texts, and speech. However, as ML algorithms enter sensitive real-world domains with trustworthiness, safety, and fairness prerequisites, the need for corresponding techniques and metrics for high-stake domains is more noticeable than before. Hence, researchers in different fields propose guidelines for Trustworthy AI [208], Safe AI [5], and Explainable AI [155] as stepping stones for next generation Responsible AI [6, 247]. Furthermore, government reports and regulations on AI accountability [75], trustworthiness [216], and safety [31] are gradually creating mandating laws to protect citizens' data privacy, fair data processing, and upholding safety for AI-based products. The development and deployment of ML algorithms for open-world tasks come with reliability and dependability limitations rooting from model performance, robustness, and uncertainty limitations [156]. Unlike traditional code-based software, ML models have fundamental safety drawbacks, including performance limitations on their training set and run-time robustness in their operational domain.

Now is the time for industry to step forward and work with policymakers to pass legislation to address risks of AI bias, and BSA will help lead this effort. Companies and governments alike should use BSA's AI Risk Management Framework as a playbook for building trust and transparency at every point in the AI lifecycle, from design to deployment,

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Senate is likely to pass a sprawling bill aimed at helping the United States compete against China on Tuesday despite criticism from many Republicans that the bill either doesn't do enough, costs too much, or both. The bill, which started as the Endless Frontier Act before being changed to the U.S. Competition and Innovation Act, will invest in domestic chip production and R&D programs, create a new technology directorate at the National Science Foundation, seek to reassure American supply chains, invest in artificial intelligence, semiconductors, biotechnology; and more. It comes amid growing tensions and competition between the United States and China.

We live in an age in which intersecting crises are being lifted to a global scale, with unseen levels of inequality, environmental degradation, and climate destabilization, as well as new surges in populism, conflict, economic uncertainty, and mounting public health threats. All are crises that are slowly tipping the balance, questioning our business-as-usual economic model of the past decades, and requiring us to rethink our next steps. In the next few months, we will once again witness a gathering of verbal diarrhea in Scotland all promising to go green. There is no doubting in the last few decades that we humans have achieved advances away beyond what our ancestors would have believed possible. The irony is that to survive we have to become something very different from what we are.