The U.S. Bureau of Labor Statistics reported that 4 million Americans quit their jobs in July 2021. Termed "The Great Resignation," the mass exodus of employees from the workplace is perhaps the reflection of deep dissatisfaction in the past work culture. You may have heard of a concept called time poverty – when you have too much to do and too little time to do it. In 2020, about 80 percent of American workers persistently felt time-poor, and the concept wasn't just in their heads. It is a well-known fact that most employees have been overburdened over the years.

Health insurance is a source of confusion, frustration and stress for many Americans. While the federal and state governments have taken measures to improve the health insurance system, many Americans still groan at the complexities and shortcomings that leave some 15% of adults ages 19-34 uninsured, and both uninsured and insured people say insurance is too expensive. Reforms to the nation's healthcare system are also insufficient for many. About 11% of uninsured people had income below the poverty level but were ineligible for Medicaid because their state did not expand the program. Even reforms to the health insurance system are not reaching most of those who still lack insurance.

Structured capability access ("SCA") is an emerging paradigm for the safe deployment of artificial intelligence (AI). Instead of openly disseminating AI systems, developers facilitate controlled, arm's length interactions with their AI systems. The aim is to prevent dangerous AI capabilities from being widely accessible, whilst preserving access to AI capabilities that can be used safely. The developer must both restrict how the AI system can be used, and prevent the user from circumventing these restrictions through modification or reverse engineering of the AI system. SCA is most effective when implemented through cloud-based AI services, rather than disseminating AI software that runs locally on users' hardware. Cloud-based interfaces provide the AI developer greater scope for controlling how the AI system is used, and for protecting against unauthorized modifications to the system's design. This chapter expands the discussion of "publication norms" in the AI community, which to date has focused on the question of how the informational content of AI research projects should be disseminated (e.g., code and models). Although this is an important question, there are limits to what can be achieved through the control of information flows. SCA views AI software not only as information that can be shared but also as a tool with which users can have arm's length interactions. There are early examples of SCA being practiced by AI developers, but there is much room for further development, both in the functionality of cloud-based interfaces and in the wider institutional framework.

In this paper, we introduce an important yet relatively unexplored NLP task called Multi-Narrative Semantic Overlap (MNSO), which entails generating a Semantic Overlap of multiple alternate narratives. As no benchmark dataset is readily available for this task, we created one by crawling 2,925 narrative pairs from the web and then, went through the tedious process of manually creating 411 different ground-truth semantic overlaps by engaging human annotators. As a way to evaluate this novel task, we first conducted a systematic study by borrowing the popular ROUGE metric from text-summarization literature and discovered that ROUGE is not suitable for our task. Subsequently, we conducted further human annotations/validations to create 200 document-level and 1,518 sentence-level ground-truth labels which helped us formulate a new precision-recall style evaluation metric, called SEM-F1 (semantic F1). Experimental results show that the proposed SEM-F1 metric yields higher correlation with human judgement as well as higher inter-rater-agreement compared to ROUGE metric.

Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of fair machine learning with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of fair machine learning produced by both social and formal (specifically machine learning and statistics) sciences in this field guide. Specifically, in addition to giving the mathematical and algorithmic backgrounds of several popular statistical and causal-based fair machine learning methods, we explain the underlying philosophical and legal thoughts that support them. Further, we explore several criticisms of the current approaches to fair machine learning from sociological and philosophical viewpoints. It is our hope that this field guide will help fair machine learning practitioners better understand how their algorithms align with important humanistic values (such as fairness) and how we can, as a field, design methods and metrics to better serve oppressed and marginalized populaces.

The new AI Standard Hub will create practical tools for businesses, bring the UK's AI community together through a new online platform, and develop educational materials to help organisations develop and benefit from global standards. This will help put the UK at the forefront of this rapidly developing area. The Hub will work to improve the governance of AI, complement pro-innovation regulation and unlock the huge economic potential of these technologies to boost investment and employment now the UK has left the European Union. BSI, the UK National Standards Body, and NPL, the country's national metrology institute, will share their world-class expertise in developing standards and research to deliver the pilot with The Alan Turing Institute, the national institute for data science and AI. The hub is backed by the Department for Digital, Culture, Media and Sport (DCMS) and the Office for AI (OAI).

The problem: Science reports that from a sample of 400 papers at top AI conferences in recent years, only 6 percent of presenters shared code. Just a third shared data, and a little over half shared summaries of their algorithms known as pseudocode. Why it matters: Without access to that information, it's hard to reproduce a study's findings. That makes it all but impossible to benchmark newly developed tools against existing ones, causing difficulties in knowing which direction in which to push future research. How to solve it: Sometimes a lack of sharing may be understandable--say, if intellectual property is owned by a private firm.

Albert Pomales is Co-Founder and CEO of KindHealth, bringing complex insurance solutions to the consumer. Health insurance is a source of confusion, frustration and stress for many Americans. While the federal and state governments have taken measures to improve the health insurance system, many Americans still groan at the complexities and shortcomings that leave some 15% of adults ages 19-34 uninsured, and both uninsured and insured people say insurance is too expensive. Reforms to the nation's healthcare system are also insufficient for many. About 11% of uninsured people had income below the poverty level but were ineligible for Medicaid because their state did not expand the program.

A joint academic research project from the United States has developed a method to foil CAPTCHA* tests, reportedly outperforming similar state-of-the-art machine learning solutions by using Generative Adversarial Networks (GANs) to decode the visually complex challenges. Testing the new system against the best current frameworks, the researchers found that their method achieves more than 94.4% success on a carefully curated real-world benchmark dataset, and has proved capable of'eliminating human involvement' when navigating a highly CAPTCHA-protected emerging Dark Net Marketplace, automatically resolving CAPTCHA challenges in a maximum of three attempts. The authors contend that their approach represents a breakthrough for cybersecurity researchers, who traditionally have had to bear the costs of supplying humans-in-the-loop to manually solve CAPTCHAs, usually via crowdsourcing platforms such as Amazon Mechanical Turk (AMT). If the system can prove adaptable and resilient, it may further pave the way for more automated oversight systems, and for the indexing and web-scraping of TOR networks. This could enable scalable and high-volume analyses, as well as the development of new cybersecurity approaches and techniques, which have been hamstrung, to date, by CAPTCHA firewalls.

The Pentagon did not adequately document work on its flagship artificial intelligence effort according to a government watchdog report, increasing the risks of lapses in the future. The Department of Defense's inspector general evaluated whether the government monitored contacts in accordance with federal laws and policy for Project Maven, which aimed to accelerate the integration of big data and machine learning. It is frequently held up as the poster child for how DoD is using AI. Army Contracting Command and the Army Research Laboratory partnered with the Pentagon's Algorithmic Warfare Cross-Functional Team to support AI development and award four contracts and a cooperative agreement for Project Maven. ECS Federal scored three of the contracts, with Morse Corporation securing the fourth and Carnegie Mellon University receiving a cooperative agreement.