Addressing public safety effectively requires incorporating diverse stakeholder perspectives, particularly those of the community, which are often underrepresented compared to other stakeholders. This study presents a comprehensive analysis of the community's general public safety concerns, their view of existing surveillance technologies, and their perception of AI-driven solutions for enhancing safety in urban environments, focusing on Charlotte, NC. Through a survey approach, including in-person surveys conducted in August and September 2023 with 410 participants, this research investigates demographic factors such as age, gender, ethnicity, and educational level to gain insights into public perception and concerns toward public safety and possible solutions. Based on the type of dependent variables, we utilized different statistical and significance analyses, such as logit regression and ordinal logistic regression, to explore the effects of demographic factors on the various dependent variables. Our results reveal demographic differences in public safety concerns. Younger females tend to feel less secure yet trust existing video surveillance systems, whereas older, educated individuals are more concerned about violent crimes in malls. Additionally, attitudes towards AI-driven surveillance differ: older Black individuals demonstrate support for it despite having concerns about data privacy, while educated females show a tendency towards skepticism.

翻译：有效解决公共安全问题需要纳入多元利益相关者的视角，特别是常被其他利益相关者忽视的社区视角。本研究以美国北卡罗来纳州夏洛特市为焦点，对社区普遍关注的公共安全问题、对现有监控技术的看法，以及他们对利用人工智能驱动方案提升城市安全性的认知进行了全面分析。通过采用调查方法（包括2023年8月至9月开展的410名参与者实地问卷调查），本研究考察了年龄、性别、族裔和教育水平等人口统计学因素，以揭示公众对公共安全及潜在解决方案的感知与关切。根据因变量类型，我们运用了Logit回归和有序逻辑回归等统计与显著性分析方法，探究人口统计学因素对各类因变量的影响。研究结果揭示了公共安全关切方面的人口统计学差异：年轻女性安全感较低但更信任现有视频监控系统，而年龄较大、受教育程度更高的个体对购物中心暴力犯罪更为担忧。此外，对人工智能驱动监控技术的态度存在分化：年长黑人群体在担忧数据隐私的同时对此类技术表示支持，而受教育程度较高的女性则倾向于持怀疑态度。