Cultural evaluation of large language models has become increasingly important, yet current benchmarks often reduce culture to static facts or homogeneous values. This view conflicts with anthropological accounts that emphasize culture as dynamic, historically situated, and enacted in practice. To analyze this gap, we introduce a four-part framework that categorizes how benchmarks frame culture, such as knowledge, preference, performance, or bias. Using this lens, we qualitatively examine 20 cultural benchmarks and identify six recurring methodological issues, including treating countries as cultures, overlooking within-culture diversity, and relying on oversimplified survey formats. Drawing on established anthropological methods, we propose concrete improvements: incorporating real-world narratives and scenarios, involving cultural communities in design and validation, and evaluating models in context rather than isolation. Our aim is to guide the development of cultural benchmarks that go beyond static recall tasks and more accurately capture the responses of the models to complex cultural situations.

GPT can predict users' future decisions by analyzing narrative tables, with accuracy further improved when guided by an anthropological framework. Moreover, by integrating contextual knowledge and an interpretative lens into LLMs, this approach enhances AI explainability while ensuring a human-centric perspective in requirement elicitation. By asking GPT to generate a user profile, it becomes possible to directly assess what the model has understood about the user and how it represents them. Furthermore, since the model is not only tasked with predicting users' responses in new scenarios but also with justifying its choices, it is possible, on one hand, to understand the rationale behind the model's output and, on the other, to identify potential misalignments between the model's prediction and the user's actual values and preferences. This enables targeted interventions to improve alignment between the LLM and the user profile, creating a continuous feedback loop that involves both the user and the LLM trained to interpret data through an anthropological lens. The process strengthens the model's interpretability, ethical alignment, and predictive adaptability, thereby making AI systems more transparent and attuned to real-world human values. Ultimately, the approach lays the groundwork for AI assistants capable of recognizing and adapting to individuals' soft ethics and ethical decision-making process. B. Threat to V alidity We discuss threats to validity following the qualitative research framework proposed in [72]--namely, credibility, transferability, dependability, and confirmability.

While screenwriters strike, partly over the threat from artificial intelligence, playwrights are busy writing about AI. Lauren Gunderson's Anthropology is the second world premiere in a week featuring pseudo-humanity – after Alan Ayckbourn's Constant Companions – and the third such London play in six months, following Jordan Harrison's Marjorie Prime and Andrew Stein's Disruption. Gunderson, an American whose I and You was a 2018 Hampstead success, creates Merril, a software engineer, whose sister Angie has been missing for a year after failing to reach home one night. From the phone, laptop and online footprint the young woman left behind, Merril sculpts a virtual Angie. The early scenes are a Merril duologue with a disembodied voice, like a digital Krapp's Last Tape, but Gunderson and director Anna Ledwich sensibly open up this closed circuit so that we see three, or by some counts four, others.

Language contact is a pervasive phenomenon reflected in the borrowing of words from donor to recipient languages. Most computational approaches to borrowing detection treat all languages under study as equally important, even though dominant languages have a stronger impact on heritage languages than vice versa. We test new methods for lexical borrowing detection in contact situations where dominant languages play an important role, applying two classical sequence comparison methods and one machine learning method to a sample of seven Latin American languages which have all borrowed extensively from Spanish. All methods perform well, with the supervised machine learning system outperforming the classical systems. A review of detection errors shows that borrowing detection could be substantially improved by taking into account donor words with divergent meanings from recipient words.

Education is a right of all, however, every individual is different than others. Teachers in post-communism era discover inherent individualism to equally train all towards job market of fourth industrial revolution. We can consider scenario of ethnic minority education in academic practices. Ethnic minority group has grown in their own culture and would prefer to be taught in their native way. We have formulated such linguistic anthropology(how people learn)based engagement as semi-supervised problem. Then, we have developed an conditional deep generative adversarial network algorithm namely LA-GAN to classify linguistic ethnographic features in student engagement. Theoretical justification proves the objective, regularization and loss function of our semi-supervised adversarial model. Survey questions are prepared to reach some form of assumptions about z-generation and ethnic minority group, whose learning style, learning approach and preference are our main area of interest.

Carmine Recchiuto, Antonio Sgorbissa Introduction Mayra is a 34-year-old woman from Sri Lanka who arrived in Genoa in 2020, just before the COVID-19 pandemic. Mayra spends her days taking care of her three children and doing housework. Due to the lockdown measures, she had few opportunities to develop relationships with Italian people, so her Italian has remained very basic. The situation did not improve until December 2021 because finding a job was challenging due to the remaining COVID restriction. In January 2022, her husband bought a small robot, and Mayra called it "Dhvija."

Machine learning (ML), being now widely accessible to the research community at large, has fostered a proliferation of new and striking applications of these emergent mathematical techniques across a wide range of disciplines. In this paper, we will focus on a particular case study: the field of paleoanthropology, which seeks to understand the evolution of the human species based on biological and cultural evidence. As we will show, the easy availability of ML algorithms and lack of expertise on their proper use among the anthropological research community has led to foundational misapplications that have appeared throughout the literature. The resulting unreliable results not only undermine efforts to legitimately incorporate ML into anthropological research, but produce potentially faulty understandings about our human evolutionary and behavioral past. The aim of this paper is to provide a brief introduction to some of the ways in which ML has been applied within paleoanthropology; we also include a survey of some basic ML algorithms for those who are not fully conversant with the field, which remains under active development. We discuss a series of missteps, errors, and violations of correct protocols of ML methods that appear disconcertingly often within the accumulating body of anthropological literature. These mistakes include use of outdated algorithms and practices; inappropriate train/test splits, sample composition, and textual explanations; as well as an absence of transparency due to the lack of data/code sharing, and the subsequent limitations imposed on independent replication. We assert that expanding samples, sharing data and code, re-evaluating approaches to peer review, and, most importantly, developing interdisciplinary teams that include experts in ML are all necessary for progress in future research incorporating ML within anthropology.

The problem with tech, many declare, is its quantitative inclination, its "hard" math deployed in the softer human world. Tech is Mark Zuckerberg: all turning pretty girls into numbers and raving about the social wonders of the metaverse while so awkward in every human interaction that he is instantly memed. The human world contains Zuck, but it is also everything he fails at so spectacularly. That failure, the lack of social and ethical chops, is one many believe he shares with the industry with which he is so associated. And so, because Big Tech is failing at understanding humans, we often hear that its workforce simply needs to employ more people who do understand.

Leaders are increasingly expected to take reactive, business-critical decisions without a clear lens into their organisations, leaving them without the time or insight for true strategic thinking. Poor decision making in the board room impacts the bottom line; research from McKinsey has demonstrated that ineffective decision making could cost the average Fortune 500 company 530,000 days of lost working time and $250 million of wasted labour costs. In recent years, one solution has been louder than most: big data. By collecting vast amounts of intricate details across all aspects of their business, leaders can get a clearer picture of what's going on within their organisation. But there's one major hurdle that data cannot overcome on its own.

Over the next few years, society as a whole will need to address what core values it wishes to protect when dealing with technology. Anthropology, a field dedicated to the very notion of what it means to be human, can provide some interesting insights into how to cope and tackle these changes in our Western society and other areas of the world. It can be challenging for social science practitioners to grasp and keep up with the pace of technological innovation, with many being unfamiliar with the jargon of AI. This short guide serves as both an introduction to AI ethics and social science and anthropological perspectives on the development of AI. It intends to provide those unfamiliar with the field with an insight into the societal impact of AI systems and how, in turn, these systems can lead us to rethink how our world operates.