Social media platforms are central to communication, yet their designs remain narrowly focused on engagement and scale. While researchers have proposed alternative visions for online spaces, these ideas are difficult to prototype within platform constraints. In this paper, we introduce a metaphor-driven system to help users imagine and explore new social media environments. The system translates users' metaphors into structured sets of platform features and generates interactive simulations populated with LLM-driven agents. To evaluate this approach, we conducted a study where participants created and interacted with simulated social media spaces. Our findings show that metaphors allow users to express distinct social expectations, and that perceived authenticity of the simulation depended on how well it captured dynamics like intimacy, participation, and temporal engagement. We conclude by discussing how metaphor-driven simulation can be a powerful design tool for prototyping alternative social architectures and expanding the design space for future social platforms.

Over 200 contractors who work on improving Google's AI products, including Gemini and AI Overviews, have been laid off, sources say. Workers enter a building on the Google headquarters campus on July 23, 2025, in Mountain View, California. More than 200 contractors who worked on evaluating and improving Google's AI products have been laid off without warning in at least two rounds of layoffs last month. The move comes amid an ongoing fight over pay and working conditions, according to workers who spoke to WIRED. In the past few years, Google has outsourced its AI rating work--which includes evaluating, editing, or rewriting the Gemini chatbot's response to make it sound more human and "intelligent"--to thousands of contractors employed by Hitachi-owned GlobalLogic and other outsourcing companies.

This paper introduces and overviews a multidisciplinary project aimed at developing responsible and adaptive multi-human multi-robot (MHMR) systems for complex, dynamic settings. The project integrates co-design, ethical frameworks, and multimodal sensing to create AI-driven robots that are emotionally responsive, context-aware, and aligned with the needs of diverse users. We outline the project's vision, methodology, and early outcomes, demonstrating how embodied AI can support sustainable, ethical, and human-centred futures.

Most existing social robot navigation techniques either leverage hand-crafted rules or human demonstrations to connect robot perception to socially compliant actions. However, there remains a significant gap in effectively translating perception into socially compliant actions, much like how human reasoning naturally occurs in dynamic environments. Considering the recent success of Vision-Language Models (VLMs), we propose using language to bridge the gap in human-like reasoning between perception and socially aware robot actions. We create a vision-language dataset, Social robot Navigation via Explainable Interactions (SNEI), featuring 40K human-annotated Visual Question Answers (VQAs) based on 2K human-robot social interactions in unstructured, crowded public spaces, spanning perception, prediction, chain-of-thought reasoning, action, and explanation. We fine-tune a VLM, Social-LLaVA, using SNEI to demonstrate the practical application of our dataset. Social-LLaVA outperforms state-of-the-art models like GPT-4V and Gemini, based on the average of fifteen different human-judge scores across 50 VQA. Deployed onboard a mobile robot, Social-LLaVA enables human-like reasoning, marking a promising step toward socially compliant robot navigation in dynamic public spaces through language reasoning.

The effects of AI on society are not just limited to the workplace. Recently, there has been a lot of talk about how AI will affect our social interactions and how we create and experience social spaces. AI-driven architecture for social spaces presents us with new opportunities as well as challenges. In his work, Babar Kasam Cazir explores the implications of how artificial intelligence could change socioeconomic dynamics - specifically in event spaces - through its ability to analyze patterns at scale. Cazir is a prominent Moorish American architect who has spent many years working in and around the entertainment and hospitality industry as the founder of AV hospitality, a casting associate at Sony Pictures, a brand ambassador at Armand de Brignac, and an event organizer.

In this work, we propose a Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors. The key feature of the model is a hierarchical prior distribution that allows us to represent the entire system jointly, achieving a compromise between dependent and independent networks. Among others things, such a specification easily allows us to visualize multilayer network data in a low-dimensional Euclidean space, generate a weighted network that reflects the consensus affinity between actors, establish a measure of correlation between networks, assess cognitive judgements that subjects form about the relationships among actors, and perform clustering tasks at different social instances. Our model's capabilities are illustrated using several real-world data sets, taking into account different types of actors, sizes, and relations.

Automation and the advancement of technology is reshaping the modern economy, how we consume and how we produce. Can the digitisation of daily interactions, whether transactional or relational, engender improved human relations? Digital technological development can be considered a liberating and connecting force in some contexts, while its threat to human livelihoods and liberties raises ethical issues as its integration with social and economic exchange grows. Developed societies are supported predominantly by a service economy, which is increasingly shaped by the nature and capabilities of the digital technologies that facilitate it. The nature and use of these technologies is disrupting traditional social behaviours and expectations.

Social influence has no small effect on the preferences and behavior of agents in a social space. Contrary to rationality, we sometimes compromise our own needs for those of others. Thus, social influence has important implications in agent cognitive modeling for multi-objective decision-making problems. Namely, where these activities occur within a social context, the intentional preferences or utility of an agent may be subsumed, to a greater or lesser degree, by the influences of other agents. In this paper, a socially-aware model predictive controller is proposed using a social influence network theory and applied to a HVAC control problem. It transforms individual agent utility to socially-influenced utility reflecting interagent influences due to their existing relationships.