In May 2021, Twitter, a platform notorious for abuse and hot-headedness, rolled out a "prompts" feature that suggests users think twice before sending a tweet. The following month, Facebook announced AI "conflict alerts" for groups, so that admins can take action where there may be "contentious or unhealthy conversations taking place." Amazon's Halo, launched in 2020, is a fitness band that monitors the tone of your voice. Wellness is no longer just the tracking of a heartbeat or the counting of steps, but the way we come across to those around us. Algorithmic therapeutic tools are being developed to predict and prevent negative behavior.

The Biden administration established several artificial intelligence bodies in 2021 likely to impact how agencies use the technology moving forward, but oversight mechanisms are lacking, experts say. Bills mandating greater accountability around AI haven't gained traction because the U.S. lacks comprehensive privacy legislation, like the European Union's General Data Protection Regulation, which would serve as a foundation for regulating algorithmic systems, according to an Open Technology Institute brief published in November. Still the White House launched the National AI Research Resource (NAIRR) Task Force and the National AI Advisory Committee, both authorized by the National AI Initiative Act of 2020, hoping to strengthen the U.S.'s competitive position globally, which may prove a losing battle absent oversight. "Right now most advocates and experts in the space are really looking to the EU as the place that's laying the groundwork for these kinds of issues," Spandana Singh, policy analyst at OTI, told FedScoop. "And the U.S. is kind of lagging behind because it hasn't been able to identify a more consolidated approach."

Existing studies on prejudice, which is important in multi-group dynamics in societies, focus on the social-psychological knowledge behind the processes involving prejudice and its propagation. We instead create a multi-agent framework that simulates the propagation of prejudice and measures its tangible impact on the prosperity of individuals as well as of larger social structures, including groups and factions within. Groups in society help us define prejudice, and factions represent smaller tight-knit circles of individuals with similar opinions. We model social interactions using the Continuous Prisoner's Dilemma (CPD) and a type of agent called a prejudiced agent, whose cooperation is affected by a prejudice attribute, updated over time based both on the agent's own experiences and those of others in its faction. Our simulations show that modeling prejudice as an exclusively out-group phenomenon generates implicit in-group promotion, which eventually leads to higher relative prosperity of the prejudiced population. This skew in prosperity is shown to be correlated to factors such as size difference between groups and the number of prejudiced agents in a group. Although prejudiced agents achieve higher prosperity within prejudiced societies, their presence degrades the overall prosperity levels of their societies. Our proposed system model can serve as a basis for promoting a deeper understanding of origins, propagation, and ramifications of prejudice through rigorous simulative studies grounded in apt theoretical backgrounds. This can help conduct impactful research on prominent social issues such as racism, religious discrimination, and unfair immigrant treatment. This model can also serve as a foundation to study other socio-psychological phenomena in tandem with prejudice such as the distribution of wealth, social status, and ethnocentrism in a society.

The burgeoning of AI has prompted recommendations that AI techniques should be "human-centered". However, there is no clear definition of what is meant by Human Centered Artificial Intelligence, or for short, HCAI. This paper aims to improve this situation by addressing some foundational aspects of HCAI. To do so, we introduce the term HCAI agent to refer to any physical or software computational agent equipped with AI components and that interacts and/or collaborates with humans. This article identifies five main conceptual components that participate in an HCAI agent: Observations, Requirements, Actions, Explanations and Models. We see the notion of HCAI agent, together with its components and functions, as a way to bridge the technical and non-technical discussions on human-centered AI. In this paper, we focus our analysis on scenarios consisting of a single agent operating in dynamic environments in presence of humans.

As if Alexa's general omnipresence weren't concerning enough, the AI assistant is now apparently telling children to stick coins in open electrical sockets. When asked for a new challenge to partake in, a 10-year-old girl was reportedly told by Alexa that she should "plug a phone charger about halfway into a wall outlet, then touch a penny to the exposed prongs." Amazon has already responded to the incident with a relatively abstract placation. "Customer trust is at the center of everything we do and Alexa is designed to provide accurate, relevant, and helpful information to customers," the company told BBC. "As soon as became aware of this error, we took swift action to fix it. It's unclear what, exactly, Amazon did to "fix" the issue.

The Shanghai People's Court in the PRC said it had developed an artificially intelligent prosecutor who accuses a person with 97 percent accuracy based on an oral description of a court case. The digital prosecutor is supposed to help reduce the burden on living prosecutors, and in some cases even replace charges at trial. Among the cases that can be considered by the "digital prosecutor": At the same time, for the use of digital the prosecutor does not require separate capacities, even an ordinary desktop computer is enough. Skeptics believe that the artificial intelligence of the prosecutor can be rare cases more harmful than useful, since he can be wrong in difficult cases. In addition, given the rapidly changing social environment, the information embedded in it is likely to quickly become outdated.

By 2030, China plans to become the world's leading country in artificial intelligence (AI). Beijing's AI development and implementation approach are fast-paced and pragmatic, with a focus on finding applications to help solve real-world problems. Several advances are being made in healthcare, such as "AI doctor" chatbots, machine learning for pharmaceutical research, and deep learning for medical image processing. Aside from this rapid development in artificial intelligence, China's AI policies are deeply troubling and deserve condemnation. Nevertheless, portraying China as a "villain" in this way may be overly simplistic and potentially costly.

A new report from Drone Industry Insights says the commercial industry can expect progress globally. DRONEII Editor Ed Alvarado writes that around the world, drone regulations – and the regulatory framework – are evolving rapidly. "This is a very welcome development given that the drone industry sees this as the most important driving factor. The movement on drone regulation in 2022 is global. In Korea, significant movement towards urban air mobility is underway: continuing the progress made this year with trial flights and the government committment to an early implementation of passenger VTOL aircraft.

A high level of AI innovation is making Switzerland a global leader in tech, talent, and funding. The alpine country of Switzerland -- famous for its luxury watches, delicious chocolate, and fondue -- is also home to a closely knit network of leading Artificial Intelligence (AI) research institutes. Their pragmatic collaboration with industrial players results in the highly efficient transfer of technology to bring innovative products to market quickly. The Digital Switzerland Strategy provides guidelines for government action and indicates where and how authorities, academia, the private sector, civil society, and politics must work together to shape the transformation process for the benefit of everyone. Moreover, the Swiss Federal Institutes of Technology in Zurich and Lausanne (ETH Zurich and EPFL) rank among the world's best technological universities, while the Lugano IDSIA Lab attracts some of the brightest minds in AI development.

Artificial intelligence (AI) has had a profound impact on our society in recent years, but it's been around longer than you may realize. Many people attribute the beginning of AI to a paper written in 1950 by Alan Turing titled "Computer Machinery and Intelligence." The term artificial intelligence, however, was first coined in 1956 at a conference that took place at Dartmouth College in Hanover, New Hampshire. Since then, interest in AI has wavered. Its most recent resurgence can be attributed to IBM's Deep Blue chess-playing supercomputer and its question-answering machine Watson. Today, AI is part of our everyday lives – from facial recognition technology and ride-share apps to smart assistants.