An evolutionary psychologist is challenging the popular understanding of kids and technology. W hen the 82-year-old psychologist Peter Gray describes the way he grew up, he punctuates the anecdotes by saying that modern parents would be arrested for letting a child have such fun. When he was 4 years old, he would walk to a store in Minneapolis to buy cigarettes for his grandmother. When he was 11, he would sometimes stay home from school in Hill City, Minnesota, to operate a newspaper printing press owned by his mother and stepfather. His parents were not arrested, and that's because the childhood they permitted him to have was basically normal at the time, even if his family did have a newspaper printing press in the house. As a boy, Peter was obsessed with fishing and baseball; neighborhood friends taught him how to ride his bike and catch grasshoppers. Although Gray's career as a scientist would begin with laboratory studies of rat hormones, he eventually found his way to writing about his childhood, in a fashion.

Large Language Models (LLMs) have become increasingly incorporated into everyday life for many internet users, taking on significant roles as advice givers in the domains of medicine, personal relationships, and even legal matters. The importance of these roles raise questions about how and what responses LLMs make in difficult political and moral domains, especially questions about possible biases. To quantify the nature of potential biases in LLMs, various works have applied Moral Foundations Theory (MFT), a framework that categorizes human moral reasoning into five dimensions: Harm, Fairness, Ingroup Loyalty, Authority, and Purity. Previous research has used the MFT to measure differences in human participants along political, national, and cultural lines. While there has been some analysis of the responses of LLM with respect to political stance in role-playing scenarios, no work so far has directly assessed the moral leanings in the LLM responses, nor have they connected LLM outputs with robust human data. In this paper we analyze the distinctions between LLM MFT responses and existing human research directly, investigating whether commonly available LLM responses demonstrate ideological leanings: either through their inherent responses, straightforward representations of political ideologies, or when responding from the perspectives of constructed human personas. We assess whether LLMs inherently generate responses that align more closely with one political ideology over another, and additionally examine how accurately LLMs can represent ideological perspectives through both explicit prompting and demographic-based role-playing. By systematically analyzing LLM behavior across these conditions and experiments, our study provides insight into the extent of political and demographic dependency in AI-generated responses.

This study explores computational approaches for measuring moral foundations (MFs) in non-English corpora. Since most resources are developed primarily for English, cross-linguistic applications of moral foundation theory remain limited. Using Chinese as a case study, this paper evaluates the effectiveness of applying English resources to machine translated text, local language lexicons, multilingual language models, and large language models (LLMs) in measuring MFs in non-English texts. The results indicate that machine translation and local lexicon approaches are insufficient for complex moral assessments, frequently resulting in a substantial loss of cultural information. In contrast, multilingual models and LLMs demonstrate reliable cross-language performance with transfer learning, with LLMs excelling in terms of data efficiency. Importantly, this study also underscores the need for human-in-the-loop validation of automated MF assessment, as the most advanced models may overlook cultural nuances in cross-language measurements. The findings highlight the potential of LLMs for cross-language MF measurements and other complex multilingual deductive coding tasks.

Last week, we looked back at how 2024 made Elon Musk the world's most powerful man. Today, we're looking at a few other important themes that will influence the online and offline worlds in 2025. Google: Ruled an illegal monopoly in August, Google could be broken up. The results are anybody's guess, but what seemed impossible for a company worth 2.5tn is at play. The US has asked the judge in the case for a wholesale breakup of the giant, which would force it to divest Chrome, the world's most popular browser and one of Google's core businesses.

This study investigates whether division on political topics is mapped with the distinctive patterns of language use. We collect a total 145,832 Reddit comments on the abortion debate and explore the languages of subreddit communities r/prolife and r/prochoice. With consideration of the Moral Foundations Theory, we examine lexical patterns in three ways. First, we compute proportional frequencies of lexical items from the Moral Foundations Dictionary in order to make inferences about each group's moral considerations when forming arguments for and against abortion. We then create n-gram models to reveal frequent collocations from each stance group and better understand how commonly used words are patterned in their linguistic context and in relation to morality values. Finally, we use Latent Dirichlet Allocation to identify underlying topical structures in the corpus data. Results show that the use of morality words is mapped with the stances on abortion.

A question that preoccupies me as a moral philosopher is to what extent artificial intelligence (AI) is capable of making moral judgments. To address that question, of course, we first need to know how humans arrive at moral judgments. Unfortunately, no consensus on that exists. Moral psychologist Jonathan Haidt argues that our moral reasoning is guided in the first place by our intuition. 'Reason is a slave of the passions,' as philosopher David Hume stated in the 18th century.

A question that preoccupies me as a moral philosopher is to what extent artificial intelligence (AI) is capable of making moral judgments. To address that question, of course, we first need to know how humans arrive at moral judgments. Unfortunately, no consensus on that exists. Moral psychologist Jonathan Haidt argues that our moral reasoning is guided in the first place by our intuition. 'Reason is a slave of the passions,' as philosopher David Hume stated in the 18th century.

In 2011, a friend of mine in college asked me if I'd read The Happiness Hypothesis: Finding Modern Truth in Ancient Wisdom, by Jonathan Haidt. Haidt's aim was to probe and distill--and "savor"--the moral precepts of antiquity in the light of modern science. The 2006 book was an answer to an overabundance of too-little-appreciated advice. "We might have already encountered the Greatest Idea, the insight that would have transformed us had we savored it, taken it to heart, and worked it into our lives," Haidt wrote." My friend was happy to encounter it: Haidt helped him through a difficult breakup. I hadn't heard of the book, but I had heard of its author. A paper of Haidt's, "The Emotional Dog and Its Rational Tail: A Social Intuitionist Approach to Moral Judgment," had been assigned in my moral psychology course, and I was in the middle of writing an essay that argued against its conclusion. Haidt wrote that reason, compared to emotion, typically matters little to what we believe is ...

Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we propose the outlines of an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which both the example of action and its justification influence the moral intuitions of others. The signals an agent provides to others, and the signals received from others, help each agent determine which others are suitable cooperative partners, and which are likely to defect. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.

Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we take steps toward an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which examples of moral judgments and their justifications influence the moral development both of individuals and of the society as a whole. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.