Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

This work explores the security and privacy perceptions, practices, and challenges Pakistani immigrants face in the US. We also explore how parent-child dynamics affect immi-grants' learning about and adaptation to security and privacy practices in the US. Through 25 semi-structured interviews with Pakistani immigrants, we find that first-generation immigrants perceive heightened risks of discrimination, surveillance, and isolation due to their status as Muslim immigrants. They also report tensions regarding self-expression and self-censorship in online settings. In contrast, second-generation immigrants quickly adapt to life in the US and do not perceive most of these challenges. We find that first- and second-generation immigrants mutually support each other in learning to use technology and reacting to perceived threats. Our findings underscore an urgent need for tailored digital safety initiatives and designs that consider the unique needs of at-risk populations to ensure their security and privacy. Recognizing and addressing these challenges can foster more inclusive digital landscapes, empowering immigrant populations with resilience and agency.