Large Language Models (LLMs) offer potential benefits for increasing access to digital well-being support, yet their application raises important questions about risks and responsible implementation. This paper examines a critical, often overlooked, dimension of LLM safety: cultural and social alignment in underrepresented contexts. We investigate how LLM-mediated emotional support can be adapted for a specific cultural setting, using Saudi Arabia as a case study. We present CSESC, a Culturally Sensitive Emotional Support Chatbot, developed as a technology probe to explore user perceptions of culturally sensitive responses. Our adaptation process was grounded in emotional support frameworks and guided by multicultural guidelines and local expertise. User evaluations demonstrate that cultural alignment enhances users’ sense of relatedness, while also surfacing tensions between empathy and sociocultural norms. We discuss the notion of “minimum cultural alignment,” contributing to HCI literature on culturally responsive LLM design and broadening the understanding of LLM safety.

Young Saudi women (YSW) face increasing mental health challenges, often hidden behind social barriers and stigma. While digital support has been investigated in Western contexts to offer discreet alternatives, its application in Saudi Arabia remains understudied, particularly in addressing unique cultural needs. To address this gap, we conducted remote interviews with 20 YSW to explore their lived experiences and challenges with existing Arabic apps, followed by remote co-design workshops with 38 YSW to explore design preferences. Using thematic analysis, our findings reveal that many apps rely on surface-level translation and fail to align with users’ cultural values, limiting their adoption. Concerns about incomplete anonymity, fear of being discovered, and a lack of guidance further challenged their use. Participants also framed online privacy as a collective moral value, rather than an individual concern. We discuss these sociocultural sensitivities and propose design considerations for future design targeting Saudi and broader Arab audiences.

Mental health disorders are prevalent worldwide, yet they remain stigmatized, especially in the Middle East. While mHealth has the potential to circumvent traditional barriers, research on its application remains scarce in Arab countries. To address this gap, we conducted a mixed-methods study of mental health apps availability, adoption, and perceptions in the Kingdom of Saudi Arabia (KSA) where digital health transformation is rapidly progressing. We interviewed twelve psychiatrists and psychologists to elicit their views on local barriers and opportunities for digital mental health support. We further systematically reviewed the Saudi app market, analysing 110 Arabic mental health apps. Our findings indicate that whilst fear of stigma and cultural factors hindered help-seeking, the privacy and anonymity enabled by technology created new opportunities for accessing mental support in the KSA. We revealed tensions between experts’ professional and practical perspectives, explored technology-exacerbated challenges and provided considerations for improving Saudi digital mental healthcare experience.

Sensor-specific calibration functions offer superior performance over global models and single-step calibration procedures but require prohibitive levels of sampling in the input feature space. Sensor self-calibration by gathering training data through collaborative calibration or self-analyzing predictive results allows these sensors to gather sufficient information. Resource-constrained edge devices are then stuck between high communication costs for transmitting training data to a centralized server and high memory requirements for storing data locally. We propose online dataset quantization that maximizes the diversity of input features, maintaining a representative set of data from a larger stream of training data points. We test the effectiveness of online dataset quantization on two real-world datasets: air quality calibration and power prediction modeling. Online Dataset Quantization outperforms reservoir sampling and performs equally to offline methods.