Social biases and belief-driven behaviors can significantly impact Large Language Models (LLMs) decisions on several tasks. As LLMs are increasingly used in multi-agent systems for societal simulations, their ability to model fundamental group psychological characteristics remains critical yet under-explored. In this study, we present a multi-agent framework that simulates belief congruence, a classical group psychology theory that plays a crucial role in shaping societal interactions and preferences. Our findings reveal that LLMs exhibit amplified belief congruence compared to humans, across diverse contexts. We further investigate the implications of this behavior on two downstream tasks: (1) misinformation dissemination and (2) LLM learning, finding that belief congruence in LLMs increases misinformation dissemination and impedes learning. To mitigate these negative impacts, we propose strategies inspired by: (1) contact hypothesis, (2) accuracy nudges, and (3) global citizenship framework. Our results show that the best strategies reduce misinformation dissemination by up to 37% and enhance learning by 11%. Bridging social psychology and AI, our work provides insights to navigate real-world interactions using LLMs while addressing belief-driven biases.

Besides its common use cases in epidemiology, political, and social sciences, causality turns out to be crucial in evaluating the fairness of automated decisions, both in a legal and everyday sense. We provide arguments and examples, of why causality is particularly important for fairness evaluation. In particular, we point out the social impact of non-causal predictions and the legal anti-discrimination process that relies on causal claims. We conclude with a discussion about the challenges and limitations of applying causality in practical scenarios as well as possible solutions.

When we ask ChatGPT or another bot to draft a memo, email, or presentation, we think these artificial-intelligence assistants are doing our bidding. A growing body of research shows that they also can change our thinking--without our knowing. One of the latest studies in this vein, from researchers spread across the globe, found that when subjects were asked to use an AI to help them write an essay, that AI could nudge them to write an essay either for or against a particular view, depending on the bias of the algorithm. Performing this exercise also measurably influenced the subjects' opinions on the topic, after the exercise.

Between 2019 and 2022, Greek media attention has been attracted by a rather unusually high number of femicide cases which have been trending for several weeks up to months in the public debate and one of the contributing factors is the feedback loop between traditional media and social media. In this paper we are investigating the use of the term "\{gamma}\u{psion}{\nu}{\alpha}{\iota}\k{appa}{\omicron}\k{appa}{\tau}{\omicron}{\nu}{\iota}{\alpha}" (femicide) in Greek speaking twitter. More specifically, we approach the problem from a stance detection perspective, aiming to automatically identify user position with regards to the feministic semantics of the word. We also discuss findings from an identity analysis perspective and intercorrelations with hate speech that have been identified in the collected corpus of tweets.

Tesla is recalling nearly 27,000 cars in the US due to windshield defrosting problems, according to a US safety regulator. The electric vehicle company is recalling 26,681 cars, including some 2021-2022 Model 3, Model S, Model X, and 2020-2022 Model Y vehicles, according to a safety recall report. In an acknowledgement letter from the US National Highway Traffic Safety Administration (NHTSA) dated 8 February, Tesla informed the federal organisation that a software error linked to the vehicle's heat pump was behind the windshield defrosting. "A software error may cause a valve in the heat pump to open unintentionally and trap the refrigerant inside the evaporator, resulting in decreased defrosting performance," the letter said. The defrosting problem may reduce drivers' visibility and potentially increase the risk of a crash, the NHTSA noted.

Researchers have created an ultracompact camera the size of a grain of salt capable of producing pictures on par with lenses hundreds of thousands of times larger than it. Engineers from Princeton University and the University of Washington say that the camera can produce full-colour images that could be used in collaboration with medical robots to diagnose and treat diseases. Traditional cameras use curved glass or plastic to bend light rays, this new camera uses'metasurface' technology which is produced like a computer chip. The metasurface of this particular camera has 1.6 million cylindrical posts – each approximately the size of a virus – to make up a system just half a millimetre wide. Each of those posts has its own unique geometry, working like an optical antenna, and machine-learning algorithms can use the posts' combined interactions with light to create high-quality images.

"I prefer to stay out of politics." Those were Elon Musk's words when the tech exec was forced to respond to a claim by Texas governor Greg Abbott that he supported the state's anti-abortion laws. If so, Mr Musk has a funny way of showing it. Over his decade-plus of public fame as the chief executive of Tesla and SpaceX, the South-African-born tycoon has attacked everyone and everything, from Donald Trump and Bernie Sanders through individual regulatory officials to Covid rules, trade unions, and "pronouns". On Monday, he hammered US president Joe Biden's flagship infrastructure and social spending bills for granting unnecessary subsidies to the electric car industry and increasing the "insane" federal budget deficit.

Artificial intelligence (AI) can obtain unbelievably accurate insights into a neighborhood's inhabitants – from their income and level of education to their ethnic background and political beliefs – just by looking at images from Google Street View. If, for example, you wanted to see whether an area voted Republican or Democrat, the AI algorithm would be able to correctly tell you with over 80 percent accuracy, namely based on the types of vehicles riding on the road. The deep-learning algorithm was developed by a team of computer scientists based at Stanford University. Their study was published in the Proceedings of the National Academy of Sciences. Throughout this process, it used an object recognition algorithm to clock tens of millions of houses, landscape features like shrubberies, and – most importantly – vehicles.

Smart assistants are designed to tackle a whole host of everyday tasks, but some users are unhappy that this seems to include taking a stand on political issues. Amazon's Alexa has come under fire on social media thanks to the AI-powered speaker's thoughts on a number of hot button topics. Some have branded Alexa a'social justice warrior' because of her responses to questions on subjects ranging from feminism to the Black Lives Matter movement. Smart assistants are designed to tackle a whole host of everyday tasks but some users are unhappy that this seems to include taking a stand on political issues. Amazon's Alexa has come under fire thanks to the AI powered speaker's thoughts on a number of hot button topics The response has been particularly vociferous among the alt-right community on social media.