Computers and Society Papers of the Week

In collaboration with civil society organizations (CSOs) from Jordan, Francophone West Africa, and Kenya, we developed a community-led approach to crowdsource culturally and linguistically relevant instances of TFGBV in Levantine Arabic, African French, and Swahili.

Two foundational aspects of human cognition, abstractness and gender, are typically examined as separable. Here, we find they are systematically linked throughout the entire space of concepts spanning single (N = 40,000) and multi-word expressions (N = 67,000): across billions of words and millions of images on the internet -- and across numerous measurement techniques -- more abstract concepts are more associated with men, an effect which replicates in languages around the world.

This paper introduces a purpose-driven technology transfer (PDTT) framework that systematically connects simulation insights with the needs of urban stakeholders.

We introduce the Voice Identity Perception benchmark (VIPBench), which contains 124,876 same-or-different judgments from 1,290 listeners on 9,800 recording pairs from 100 public figures.

We introduce ALPS (Align Long-term by Planning Short-term), a hierarchical simulated student conditioned on observable, environment-specific behavior profiles.

By relying on short-horizon feedback, this approach ignores the profound, longitudinal externalities of persistent human-AI interaction, which can lead to adverse outcomes such as cognitive atrophy, social erosion, and diminished human agency. To address this, we propose a paradigm shift toward long-horizon alignment that accounts for how AI reshapes human experience over time.

However, prior work has highlighted that red-teaming is a fairly open-ended task, laden with myriad design choices that can shape its findings. In this work, we focus on the instructions used to conduct red-teaming exercises and hypothesize that they can significantly affect outcomes in a manner analogous to the framing effect in psychology, where the format in which information is presented greatly impacts how people respond.

We present a mixed-methods study of 190 TikTok videos tagged with hashtag harassment and their 66,337 comments.

Language models face two opposing concerns: monoculture, in which models err in correlated ways, and multiplicity, in which equally accurate models make conflicting decisions.

However, existing infrastructures for collecting such data reproducibly, sustainably, and with strong privacy guarantees remain limited: commercial solutions lack transparency and data governance, while academic tools are typically designed for narrow, short-term use cases and do not support general-purpose academic panel data collections. We address this gap with GESIS Surf, a research infrastructure for large-scale longitudinal collection of web behavioral data in naturalistic environments.

We conducted 37 semi-structured expert interviews to examine how frontier AI evaluations reduce societal-level AI risks.

As society, ethics, and professional practice become increasingly integrated across the computer science curriculum, technical courses need practical ways to support ethical reasoning. Ethical essay assignments offer one approach, but little is known about how students use GenAI when writing such essays.

Existing meta-analyses can help reconcile these findings, but they require substantial manual effort and cannot readily incorporate new evidence as it emerges. To address this challenge, we introduce AutoAnalysis: an automated, self-updating meta-analysis of when humans and AI work better together, and when they work better alone.

The mobilisation brought participants into a rapidly assembled network of Telegram chats, but disagreements over its direction and protest tactics became visible after the first demonstrations. We study how this online environment formed and subsequently fragmented.

Models performed better when inferring more observable states than participants' subjective appraisals of social experience, such as perceived social support.

At the same time, existing methods that target global accuracy guarantees pay a price exponential in the data dimension, $d$. In this work, we address this ``curse of dimensionality'' by presenting a new DP synthetic data algorithm, SPLICE, that comes with a compelling global distributional guarantee, even in high-dimensions, and moreover, is practically state-of-the-art.

Studying these effects in cities is challenging because, unlike online resources, travel constraints and limited venue capacity shape where people can go, while each recommended visit also changes whom other visitors encounter. To capture such features, we build a long-term simulation framework based on fine-grained human mobility data from four U.S. metropolitan areas, with distance and capacity constraints.