Trump on the brink of'major war' with Iran as Ayatollah defies his nuclear red line It looks like paradise... but the Costa Rica resort where a surfing legend was murdered while living with girlfriend less than half his age is hiding a seedy underbelly Courtney Love's agony over Kurt Cobain'homicide' investigation: Insiders break silence about new probe My wife showed me her extreme kink on Pornhub... then she begged me to do the unthinkable: DEAR JANE Lindsey Vonn's Winter Olympics ski crash injury is'a lot more severe than a broken leg' with her'leg in pieces' after specialists suggested she may need amputation Nancy Guthrie sheriff insists her case is'far from cold' despite no leads, arrests, or DNA matches 18 days after disappearance Unseen trove of Alexander brothers photos revealed... as horrifying sex crimes trial is rocked by jury scandal Ukraine peace talks collapse in less than two hours as Zelensky says it is'not fair' Trump wants him to compromise and not Putin How I lost eight stone by filling up on THESE two foods - and not a fat jab in sight: I will forever be haunted by my wedding and honeymoon pictures, but now I'm nine-and-a-half stone and eating more than ever Police arrest boyfriend of girl who vanished without a trace as they believe he'heinously murdered her' JFK Jr's hunky love rival kept Carolyn Bessette coming back for more... now we've found silver-haired Baywatch star on a bus bench Secret'immovable' UFO is hiding in plain sight in purpose-built structure claims US congressman The sex complaints women are too afraid to tell their husbands: The position we dread, the mistake most men make... and our favorite sneaky trick Your choice of a cheap Zinfandel Rosé over an expensive Argentinian Malbec might reveal more about your personality than your palate, according to a new study. Researchers have found that traits such as extraversion, openness and neuroticism can indicate what type of plonk you prefer. They used AI to determine personality traits based on the reviews, and compared it to the strength of wine people were buying. Analysis revealed that people who score high in agreeableness and openness tend to go for wines with a higher alcohol content. These are usually perceived as being of higher quality and have a richer body and taste - for example a Cabernet Sauvignon, Malbec, Port or Sherry.

As large language models (LLMs) are increasingly used in morally sensitive domains, it is crucial to understand how persona traits affect their moral reasoning and persuasive behavior. We present the first large-scale study of multi-dimensional persona effects in AI-AI debates over real-world moral dilemmas. Using a 6-dimensional persona space (age, gender, country, class, ideology, and personality), we simulate structured debates between AI agents over 131 relationship-based cases. Our results show that personas affect initial moral stances and debate outcomes, with political ideology and personality traits exerting the strongest influence. Persuasive success varies across traits, with liberal and open personalities reaching higher consensus and win rates. While logit-based confidence grows during debates, emotional and credibility-based appeals diminish, indicating more tempered argumentation over time. These trends mirror findings from psychology and cultural studies, reinforcing the need for persona-aware evaluation frameworks for AI moral reasoning.

Socioeconomic status (SES) fundamentally influences how people interact with each other and more recently, with digital technologies like Large Language Models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from diverse socioeconomic backgrounds about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entails a higher level of abstraction, convey requests more concisely, and topics like 'inclusivity' and 'travel'. Lower SES correlates with higher anthropomorphization of LLMs (using ''hello'' and ''thank you'') and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups.

Since the foundational work of William Labov on the social stratification of language (Labov, 1964), linguistics has made concentrated efforts to explore the links between sociodemographic characteristics and language production and perception. But while there is strong evidence for socio-demographic characteristics in language, they are infrequently used in Natural Language Processing (NLP). Age and gender are somewhat well represented, but Labov's original target, socioeconomic status, is noticeably absent. And yet it matters. We show empirically that NLP disadvantages less-privileged socioeconomic groups. We annotate a corpus of 95K utterances from movies with social class, ethnicity and geographical language variety and measure the performance of NLP systems on three tasks: language modelling, automatic speech recognition, and grammar error correction. We find significant performance disparities that can be attributed to socioeconomic status as well as ethnicity and geographical differences. With NLP technologies becoming ever more ubiquitous and quotidian, they must accommodate all language varieties to avoid disadvantaging already marginalised groups. We argue for the inclusion of socioeconomic class in future language technologies.

Since Labov's (1964) foundational work on the social stratification of language, linguistics has dedicated concerted efforts towards understanding the relationships between socio-demographic factors and language production and perception. Despite the large body of evidence identifying significant relationships between socio-demographic factors and language production, relatively few of these factors have been investigated in the context of NLP technology. While age and gender are well covered, Labov's initial target, socio-economic class, is largely absent. We survey the existing Natural Language Processing (NLP) literature and find that only 20 papers even mention socio-economic status. However, the majority of those papers do not engage with class beyond collecting information of annotator-demographics. Given this research lacuna, we provide a definition of class that can be operationalised by NLP researchers, and argue for including socio-economic class in future language technologies.

There are several theories in economics regarding the roots or causes of prosperity in a society. One of these theories or hypotheses -- named geography hypothesis -- mentions that the reason why some countries are prosperous and some others are poor is the geographical location of the countries in the world as makes their climate and environment favorable or unfavorable regarding natural resources. Another competing hypothesis states that man-made institutions particularly inclusive political institutions are the reasons why some countries are prosperous and some others are poor. On the other hand, there is a specific political theory developed for the long-term social development in Iran -- named Arbitrary Rule and Aridisolatic Society which particularly emphasizes on the role of aridity to shape arbitrary political and economical institutions in Iran, without any functional social classes in the society. In this paper, by extending the AI-Economist -- a recently developed two-level multi-agent reinforcement learning environment -- I show that when the central planner is ruling the environment by arbitrary rules, the society evolves through different paths in different environments. In the environment having band-like vertical isolated patches of natural resources, all mobile agents are equally exploited by the central planner and the central planner is also not gaining any income, while in the society having more uniformly distributed natural resources, the productivity and Maximin are higher and the society generates a heterogeneous stratified social structure. All these findings provide a partial answer to the above debate and reconcile the role of geography and political institutions on the long-term development in a region.

Studies have shown that large pretrained language models exhibit biases against social groups based on race, gender etc, which they inherit from the datasets they are trained on. Various researchers have proposed mathematical tools for quantifying and identifying these biases. There have been methods proposed to mitigate such biases. In this paper, we present a comprehensive quantitative evaluation of different kinds of biases such as race, gender, ethnicity, age etc. exhibited by popular pretrained language models such as BERT, GPT-2 etc. and also present a toolkit that provides plug-and-play interfaces to connect mathematical tools to identify biases with large pretrained language models such as BERT, GPT-2 etc. and also present users with the opportunity to test custom models against these metrics. The toolkit also allows users to debias existing and custom models using the debiasing techniques proposed so far. The toolkit is available at https://github.com/HrishikeshVish/Fairpy.

Those deemed in the higher class may be envied for their luxurious cars, large homes and stylish clothes, but there is one thing they do not have – the ability to read people's emotions. A study used a cognitive empathy test called'the Reading the mind in the eyes,' which participants from higher and lower social classes were asked to determine emotional states from images of eyes. The results showed those in the lower class were better at understanding other people's minds compared to their counterparts. Experts suggest the reason is because lower social classes tend to prioritize the needs and preferences of others, and are ultimately more empathetic. A study used a cognitive empathy test called'the Reading the mind in the eyes,' which participants from higher and lower social classes were asked to determine emotional states from images of eyes - and the team calculated the scores The study was conducted by a team at the University of California, Irvine who questioned – 'How does access to resources (e.g., money, education) influence the way we process information about other human beings,' PsyPost reported.

One reason for the widespread interest in AI is that it has the potential to reduce the degree of bias underpinning human decisions. For example, meta-analytic studies have long highlighted the pervasive nature of bias in hiring and recruitment. Even in the rich and liberal world, there are many biases at play in the workplace, which account for the unmeritocratic or unfair advantage that some groups have over others, irrespective of their actual talent or potential: sexism, racism, and ageism, to name just a few. But one of the most prominent biases is hardly ever discussed or acknowledged, namely the beauty bias -- also known as "lookism." Indeed, the existence of a beauty premium in the labor market is well-documented.

In a matter of seconds, hiring managers determine competence of candidates based on their speech ... [ ] patterns. Undeniably, the first few minutes of an interview are critical when it comes to making a positive first impression. A study by the Journal of Occupational and Organizational Psychology found 60% of interviewers know within the first 15 minutes if the candidate they're interviewing is suitable for the role. If that's not alarming, recent research says nearly 30% of interviewers have made up their mind within the first five minutes of meeting a candidate. These snap decisions are heavily influenced by unconscious bias resulting in unequal treatment for job applicants.