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ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2025,10,18]]},"abstract":"<jats:p>As families face increasingly complex safety challenges in digital and physical environments, generative AI (GenAI) presents new opportunities to support household safety through multiple specialized AI agents. Through a two-phase qualitative study consisting of individual interviews and collaborative sessions with 13 parent-child dyads, we explored families' conceptualizations of GenAI and their envisioned use of AI agents in daily family life. Our findings reveal that families preferred to distribute safety-related support across multiple AI agents, each embodying a familiar caregiving role: a household manager coordinating routine tasks and mitigating risks such as digital fraud and home accidents; a private tutor providing personalized educational support, including safety education; and a family therapist offering emotional support to address sensitive safety issues such as cyberbullying and digital harassment. Families emphasized the need for agent-specific privacy boundaries, recognized generational differences in trust toward AI agents, and stressed the importance of maintaining open family communication alongside the assistance of AI agents. Based on these findings, we propose a multi-agent system design featuring four privacy-preserving principles: memory segregation, conversational consent, selective data sharing, and progressive memory management to help balance safety, privacy, and autonomy within family contexts.<\/jats:p>","DOI":"10.1145\/3757598","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:32:00Z","timestamp":1760635920000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Families' Vision of Generative AI Agents for Household Safety Against Digital and Physical Threats"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9163-7450","authenticated-orcid":false,"given":"Zikai","family":"Wen","sequence":"first","affiliation":[{"name":"School of Engineering and Technology, University of Washington, Tacoma, Washington, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2723-722X","authenticated-orcid":false,"given":"Lanjing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7900-9265","authenticated-orcid":false,"given":"Yaxing","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"e_1_2_1_1_1","article-title":"A Longitudinal Study of Child Wellbeing Assessment via Online Interactions with a Social Robot","volume":"14","author":"Abbasi Nida Itrat","year":"2025","unstructured":"Nida Itrat Abbasi, Guy Laban, Tamsin Ford, Peter B. 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