A group of 226 engineers and other Google workers have formed a union, according to an article and opinion piece in the New York Times. Called the Alphabet Workers Union, it is affiliated with the Communications Workers of America and was organized in secret over the last year or so. "We are joining together -- temps, vendors, contractors, and full-time employees -- to create a unified worker voice," wrote the Parul Koul and Chewy Shaw, the executive chair and vice chair of the Alphabet Workers Union. "We want Alphabet to be a company where workers have a meaningful say in decisions that affect us and the societies we live in." The union represents a small minority of the company's 260,000 strong employee and contractor workforce.
But there is no doubt that the pandemic has hastened the adoption of emerging digital technologies, ushered in a new era of remote and flexible working arrangements, increased organisations' reliance on digital infrastructure and exposed our tech-related strengths and weaknesses alike. Leaving 2020 in the rear-view mirror, we count down our top 10 predictions for 2021 and beyond in the domain of Digital Law in Australia. Despite an existing principles-based framework for the protection of privacy under the Privacy Act, in recent years the Federal Government has preferred to introduce parallel privacy requirements, such as the 13 Privacy Safeguards under the Consumer Data Right legislation and the privacy aspects of the upcoming Data Availability and Transparency Act for Government agencies. These nascent regimes are similar enough to the existing privacy regime to encourage complacency and different enough to give any compliance function a headache. Overlapping and often sectorial regulation adds to the increasing complexity of privacy law in Australia.
Successive lockdowns imposed across the globe and travel restrictions accelerated digital transformation at workplaces and for essential services. Unable to step out, people turned to online portals and apps for most tasks including shopping, learning and banking. Healthcare gained importance, driving more people to get insurance and firms also embraced digitisation to serve consumers, globally and in the Middle East. But interacting with consumers and verifying claims online, in order to ensure contactless service, has its challenges when cybercrime is surging as quickly as the tech-savvy economy. Saudi Arabia's cooperative insurer Tawuniya also found itself vulnerable, at a time when 95% firms in the kingdom were reportedly targeted by cybercrooks.
Deepfake technology (DT) has taken a new level of sophistication. Cybercriminals now can manipulate sounds, images, and videos to defraud and misinform individuals and businesses. This represents a growing threat to international institutions and individuals which needs to be addressed. This paper provides an overview of deepfakes, their benefits to society, and how DT works. Highlights the threats that are presented by deepfakes to businesses, politics, and judicial systems worldwide. Additionally, the paper will explore potential solutions to deepfakes and conclude with future research direction.
Adversarial attacks for machine learning models have become a highly studied topic both in academia and industry. These attacks, along with traditional security threats, can compromise confidentiality, integrity, and availability of organization's assets that are dependent on the usage of machine learning models. While it is not easy to predict the types of new attacks that might be developed over time, it is possible to evaluate the risks connected to using machine learning models and design measures that help in minimizing these risks. In this paper, we outline a novel framework to guide the risk management process for organizations reliant on machine learning models. First, we define sets of evaluation factors (EFs) in the data domain, model domain, and security controls domain. We develop a method that takes the asset and task importance, sets the weights of EFs' contribution to confidentiality, integrity, and availability, and based on implementation scores of EFs, it determines the overall security state in the organization. Based on this information, it is possible to identify weak links in the implemented security measures and find out which measures might be missing completely. We believe our framework can help in addressing the security issues related to usage of machine learning models in organizations and guide them in focusing on the adequate security measures to protect their assets.
Let us consider a scenario: one night, an executive responsible for operations for a remote downstream oil and gas refinery gets a call from one of their subordinates saying things started acting up ever since they plugged in a USB they brought from home. Multiple processes have become unstable and commands sent to equipment are not executed as requested. Panicking, they say there has been a cyber attack on the supervisory control and data acquisition (SCADA) system. Valves, pumps, and compressors connected to the system are going haywire, and the organisation's legacy systems were not equipped to prevent whatever new malware snuck into the system. Production comes to a halt for two days.
The advent of technology has brought convenience to life. Believe it or not, survival without technology is one of the darkest thoughts that can cross your mind in the digital era. The world has become a global village thanks to rapid digitization, but it has also opened doors for many fraudsters to step in and terrify people. Organizations in every sector are unsafe due to increasing ransomware and data breaches. Considering the increasing number of frauds, companies opt for robust verification systems with OCR technology to only onboard legitimate customers.
How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.
Deepfake audio technology is becoming incredibly convincing, so much so that Jay-Z apparently took legal action against an AI-powered impersonation of him this year. Eminem is the latest rapper to receive the deepfake treatment, and in a new digitally fabricated song, he goes after Facebook founder Mark Zuckerberg. The video was created by YouTube channel Calamity AI in partnership with another YouTuber, 30HZ. Calamity AI explains the song, "An Eminem diss-track written by Artificial Intelligence. We inputted the title'Mark Zuckerberg Diss in the Style of Eminem' and let the A.I. write the rest. From there, we sent the lyrics to 30HZ, who synthesized and created the vocals. The audio was not record by Eminem."
Artificial intelligence (AI) is swiftly fueling the development of a more dynamic world. AI, a subfield of computer science that is interconnected with other disciplines, promises greater efficiency and higher levels of automation and autonomy. Simply put, it is a dual-use technology at the heart of the fourth industrial revolution. Together with machine learning (ML) -- a subfield of AI that analyzes large volumes of data to find patterns via algorithms -- enterprises, organizations, and governments are able to perform impressive feats that ultimately drive innovation and better business. The use of both AI and ML in business is rampant.