AI Insights

• Adolescents favored relational style over transparent style in AI chatbots, seeing the former as more human-like, likable, trustworthy, and emotionally close. [3]
• The relational style was associated with increased anthropomorphism, which may enhance trust, likability, and emotional closeness. [3]
• Adolescents with lower relationship quality and higher distress were more drawn to the relational chatbot, suggesting a link between social deprivation and AI use. [3]
• Relational style: using language and responses that simulate social relationships and emotional support, making the chatbot feel more like a social other. [3]
• The study provides insights into how adolescents and parents evaluate relational versus transparent conversational styles in AI chatbots, highlighting the importance of balancing anthropomorphic language with clear boundaries and transparency. [3]
• The findings suggest that relational style is attractive to adolescents and acceptable to many parents, but also raises concerns about emotional reliance on AI and displacement of human relationships. [3]
• The study highlights the importance of designing AI chatbots that balance anthropomorphic language with clear boundaries and transparency. [2]
• Parents were more likely to prefer transparent style, emphasizing safety and boundary clarity, but many still preferred relational style for their adolescents. [1]

Abstract
General-purpose conversational AI chatbots and AI companions increasingly provide young adolescents with emotionally supportive conversations, raising questions about how conversational style shapes anthropomorphism and emotional reliance. In a preregistered online experiment with 284 adolescent-parent dyads, youth aged 11-15 and their parents read two matched transcripts in which a chatbot responded to an everyday social problem using either a relational style (first-person, affiliative, commitment language) or a transparent style (explicit nonhumanness, informational tone). Adolescents more often preferred the relational than the transparent style, whereas parents were more likely to prefer transparent style than adolescents. Adolescents rated the relational chatbot as more human-like, likable, trustworthy and emotionally close, while perceiving both styles as similarly helpful. Adolescents who preferred relational style had lower family and peer relationship quality and higher stress and anxiety than those preferring transparent style or both chatbots. These findings identify conversational style as a key design lever for youth AI safety, showing that relational framing heightens anthropomorphism, trust and emotional closeness and can be especially appealing to socially and emotionally vulnerable adolescents, who may be at increased risk for emotional reliance on conversational AI.

Why we are recommending this paper?
Due to your Interest in: AI for Social Good

This paper directly addresses concerns around adolescent mental health and the potential impact of AI, aligning with interests in social good and vulnerable populations. Understanding how AI interacts with young people’s emotional needs is crucial for responsible AI development and addressing issues like loneliness and social isolation.

AI Insights

• The Social Responsibility Stack (SRS) is a framework for ensuring that AI systems are designed and deployed in a responsible manner. [2]

Abstract
Artificial intelligence systems are increasingly deployed in domains that shape human behaviour, institutional decision-making, and societal outcomes. Existing responsible AI and governance efforts provide important normative principles but often lack enforceable engineering mechanisms that operate throughout the system lifecycle. This paper introduces the Social Responsibility Stack (SRS), a six-layer architectural framework that embeds societal values into AI systems as explicit constraints, safeguards, behavioural interfaces, auditing mechanisms, and governance processes. SRS models responsibility as a closed-loop supervisory control problem over socio-technical systems, integrating design-time safeguards with runtime monitoring and institutional oversight. We develop a unified constraint-based formulation, introduce safety-envelope and feedback interpretations, and show how fairness, autonomy, cognitive burden, and explanation quality can be continuously monitored and enforced. Case studies in clinical decision support, cooperative autonomous vehicles, and public-sector systems illustrate how SRS translates normative objectives into actionable engineering and operational controls. The framework bridges ethics, control theory, and AI governance, providing a practical foundation for accountable, adaptive, and auditable socio-technical AI systems.

Why we are recommending this paper?
Due to your Interest in: AI for Social Good

Given your interest in AI for social good, this paper’s focus on governance and control mechanisms for AI systems is highly relevant. It provides a framework for ensuring AI deployment aligns with societal values and addresses potential harms, particularly concerning large-scale social impact.

AI Insights

• There are 34 papers listed, covering various topics such as menstrual health, digital domestic labor economies, e-commerce livestreaming, and algorithmic content moderation. [3]
• Several authors have received awards or recognition for their work, including Best Paper Awards and Honorable Mentions. [3]
• The conference focuses on Human-Computer Interaction (HCI) with a particular emphasis on the experiences and perspectives of women in Asia. [2]

Abstract
Feminist HCI has been rapidly developing in East Asian contexts in recent years. The region's unique cultural and political backgrounds have contributed valuable, situated knowledge, revealing topics such as localized digital feminism practices, or women's complex navigation among social expectations. However, the very factors that ground these perspectives also create significant survival challenges for researchers in East Asia. These include a scarcity of dedicated funding, the stigma of being perceived as less valuable than productivity-oriented technologies, and the lack of senior researchers and established, resilient communities. Grounded in these challenges and our prior collective practices, we propose this meet-up with two focused goals: (1) to provide a legitimized channel for Feminist HCI researchers to connect and build community, and (2) to facilitate an action-oriented dialogue on how to legitimize, develop, and sustain Feminist HCI in the East Asian context. The website for this meet-up is: https://feminist-hci.github.io/

Why we are recommending this paper?
Due to your Interest in: Female Empowerment

This research offers valuable insights into the intersection of technology, gender, and social justice, aligning with your interest in female empowerment and addressing systemic inequalities. The focus on localized digital feminism practices is particularly pertinent to understanding diverse experiences of technology use.

AI Insights

• Corruption: The abuse of power or position for personal gain, often at the expense of others. [3]
• Gini coefficient: A measure of income inequality, with higher values indicating greater disparity between rich and poor. [3]
• The study explores the relationship between inequality and the environment using datasets from the United Nations (UN) and the World Bank (WB). [2]

Abstract
The relationship between inequality and the biosphere has been hypothesized to mutual dependecies and feedbacks. If that is true, such feedbacks may give rise to inequality regimes and potential tipping points between them. Here we explore synergies and trade-offs between inequality and biosphere-related sustainable development goals. We used the openly available SDG datasets by the World Bank (WB) and United Nations (UN) and applied ordination methods to distill interactions between economic inequality and the environmental impact across countries. Our results confirm the existence of inequality regimes, and we find preliminary evidence that corruption may be a candidate driver of tipping between regimes.

Why we are recommending this paper?
Due to your Interest in: Measureable ways to end poverty

This paper’s investigation into inequality and sustainable development goals directly addresses your interest in poverty and measuring effective solutions. The use of data to identify ‘inequality traps’ offers a potentially measurable approach to tackling systemic issues.

AI Insights

• The study was conducted on the campus of École Polytechnique Fédérale de Lausanne (EPFL), which has a diverse community of 14,012 students and 6,477 employees from over 130 nationalities. [3]
• CO2eq: carbon dioxide equivalent 𝑌𝑖,𝑐,𝑑: outcome (vegetarian indicator or CO2eq emissions) for transaction 𝑖, customer 𝑐 on date 𝑑 Treatment 𝑡,𝑐,𝑑: indicator variables for the three pricing interventions The study found that discounting prices for vegetarian meals increased the probability of selecting a vegetarian meal by 12.5%. [3]
• Surcharging non-vegetarian meals decreased the probability of selecting a non-vegetarian meal by 10.3%. [3]
• Three pricing interventions were tested: discounting prices for vegetarian meals, surcharging non-vegetarian meals, and a combination of both. [2]
• The food ecosystem on campus features 12 main cafeterias, as well as food trucks and off-campus options such as a fast-food restaurant and a grocery store. [1]

Abstract
Meat consumption is a major driver of global greenhouse gas emissions. While pricing interventions have shown potential to reduce meat intake, previous studies have focused on highly constrained environments with limited consumer choice. Here, we present the first large-scale field experiment to evaluate multiple pricing interventions in a real-world, competitive setting. Using a sequential crossover design with matched menus in a Swiss university campus, we systematically compared vegetarian-meal discounts (-2.5 CHF), meat surcharges (+2.5 CHF), and a combined scheme (-1.2 CHF=+1.2 CHF) across four campus cafeterias. Only the surcharge and combined interventions led to significant increases in vegetarian meal uptake--by 26.4% and 16.6%, respectively--and reduced CO2 emissions per meal by 7.4% and 11.3%, respectively. The surcharge, while effective, triggered a 12.3% drop in sales at intervention sites and a corresponding 14.9% increase in non-treated locations, hence causing a spillover effect that completely offset environmental gains. In contrast, the combined approach achieved meaningful emission reductions without significant effects on overall sales or revenue, making it both effective and economically viable. Notably, pricing interventions were equally effective for both vegetarian-leaning customers and habitual meat-eaters, stimulating change even within entrenched dietary habits. Our results show that balanced pricing strategies can reduce the carbon footprint of realistic food environments, but require coordinated implementation to maximize climate benefits and avoid unintended spillover effects.

Why we are recommending this paper?
Due to your Interest in: Econometrics for Social Good

Considering your interest in healthy society and addressing environmental concerns, this research explores the use of economic incentives – a ‘carrot’ – to promote sustainable food choices. Understanding how pricing impacts behavior is key to tackling issues like climate change and food security.