As more and more workforces adopt hybrid and work-from-anywhere models, using the right combination of technologies to create effective meetings can not only enhance productivity, but also promote well-being. Using a variety of tech solutions that improve scheduling and time management, communication, and collaboration is key to achieving positive work-life balance. In a world of constant technological innovation, it can be difficult to determine what's most helpful to supporting a workforce's performance. As we navigate new work models such as hybrid and work-from-anywhere, this technology must also factor in the importance of protecting employee personal time. Meetings can have a major impact on the length of a workday, which affects work-life balance.

Whenever a professional sector faces new technology, questions arise regarding how that technology will disrupt daily operations and the careers of those who choose that profession. And lawyers and the legal profession are no exception. Today, artificial intelligence (AI) is beginning to transform the legal profession in many ways, but in most cases it augments what humans do and frees them up to take on higher-level tasks such as advising to clients, negotiating deals and appearing in court. Artificial intelligence mimics certain operations of the human mind and is the term used when machines are able to complete tasks that typically require human intelligence. The term machine learning is when computers use rules (algorithms) to analyze data and learn patterns and glean insights from the data.

In order to settle a lawsuit brought by the US Equal Employment Opportunity Commission, Activision Blizzard has agreed to establish an $18 million fund for eligible claimants -- meaning, employees who were harmed by the company's discriminatory hiring and management practices. The EEOC lawsuit was filed Monday, and that same afternoon, Activision Blizzard announced the $18 million conclusion. Activision Blizzard is the company behind blockbuster video game franchises including Call of Duty, World of Warcraft, Diablo and Overwatch. Activision Blizzard's revenue for the year 2020 was $8.1 billion, with a profit of more than $2 billion. Today's $18 million agreement follows a three-year investigation into Activision Blizzard by the EEOC.

Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? Existing benchmarks are not designed to measure progress in applied settings, and so don't directly answer this question. The RAFT benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment. Baseline evaluations on RAFT reveal areas current techniques struggle with: reasoning over long texts and tasks with many classes. Human baselines show that some classification tasks are difficult for non-expert humans, reflecting that real-world value sometimes depends on domain expertise. Yet even non-expert human baseline F1 scores exceed GPT-3 by an average of 0.11. The RAFT datasets and leaderboard will track which model improvements translate into real-world benefits at https://raft.elicit.org .

Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, safety for ML should be a leading research priority. In response to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address. We present four problems ready for research, namely withstanding hazards ("Robustness"), identifying hazards ("Monitoring"), steering ML systems ("Alignment"), and reducing risks to how ML systems are handled ("External Safety"). Throughout, we clarify each problem's motivation and provide concrete research directions.

Some of the world's biggest organizations, from the United Nations to Google to the U.S. Defense Department, proudly proclaim their bona fides when it comes to their ethical use of artificial intelligence. But for many other organizations, talking the talk is the easy part. A new report by a pair of Northeastern researchers discusses how articulating values, ethical concepts, and principles is just the first step in addressing AI and data ethics challenges. The harder work is moving from vague, abstract promises to substantive commitments that are action-guiding and measurable. "You see case after case where a company has these mission statements that they fail to live up to," says John Basl, an associate professor of philosophy and a co-author of the report.

Dr Thaler's international crusade for recognition of machine inventorship (which I reported on last year) is nearing the end of the line in the UK. Last week, in Thaler v Comptroller General of Patents Trade Marks And Designs [2021] EWCA Civ 1374, the Court of Appeal upheld the rejection of his DABUS patent applications. In 2018, Dr Thaler, the owner of DABUS (an artificial intelligence ("AI") creativity machine) submitted two patent applications to the UKIPO naming himself as the owner and DABUS as the inventor. The UKIPO rejected his applications on the basis that, for the purposes of the Patents Act 1977 ("PA 1997"), the inventor must be a "person" (with legal personality, such as a human or a corporate entity), and considering how ownership is derived from inventorship, Dr Thaler could not be the owner in the absence of a valid inventor. In 2020, in the Court of First Instance, Marcus Smith J upheld the UKIPO's decision, concluding that section 7 PA 1997, which sets out the classes of persons to whom patents can be granted, could not be interpreted to cover non-legal persons such as machines. On that basis, he found that the UKIPO was entitled to withdraw Dr Thaler's application under section 13 PA 1997.

Events over the past few years have revealed several human rights violations associated with increasing advances in artificial intelligence (AI). Algorithms created to regulate speech online have censored speech ranging from religious content to sexual diversity. AI systems created to monitor illegal activities have been used to track and target human rights defenders. And algorithms have discriminated against Black people when they have been used to detect cancers or assess the flight risk of people accused of crimes. As researchers studying the intersection between AI and social justice, we've been examining solutions developed to tackle AI's inequities.

Federated learning models must be protected against plagiarism since these models are built upon valuable training data owned by multiple institutions or people.This paper illustrates a novel federated deep neural network (FedDNN) ownership verification scheme that allows ownership signatures to be embedded and verified to claim legitimate intellectual property rights (IPR) of FedDNN models, in case that models are illegally copied, re-distributed or misused. The effectiveness of embedded ownership signatures is theoretically justified by proved condition sunder which signatures can be embedded and detected by multiple clients with-out disclosing private signatures. Extensive experimental results on CIFAR10,CIFAR100 image datasets demonstrate that varying bit-lengths signatures can be embedded and reliably detected without affecting models classification performances. Signatures are also robust against removal attacks including fine-tuning and pruning.

Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for algorithmic fairness should therefore be approached through a sociotechnical lens. We advance the discourse on algorithmic fairness as a sociotechnical phenomenon. Our research objective is to embed AF in the sociotechnical view of IS. Specifically, we elaborate on why outcomes of a system that uses algorithmic means to assure fairness depends on mutual influences between technical and social structures. This perspective can generate new insights that integrate knowledge from both technical fields and social studies. Further, it spurs new directions for IS debates. We contribute as follows: First, we problematize fundamental assumptions in the current discourse on algorithmic fairness based on a systematic analysis of 310 articles. Second, we respond to these assumptions by theorizing algorithmic fairness as a sociotechnical construct. Third, we propose directions for IS researchers to enhance their impacts by pursuing a unique understanding of sociotechnical algorithmic fairness. We call for and undertake a holistic approach to AF. A sociotechnical perspective on algorithmic fairness can yield holistic solutions to systemic biases and discrimination.