Designing Chatbots to Support Victims and Survivors of Domestic Abuse
<|reference_start|>Designing Chatbots to Support Victims and Survivors of Domestic Abuse: Objective: Domestic abuse cases have risen significantly over the last four years, in part due to the COVID-19 pandemic and the challenges for victims and survivors in accessing support. In this study, we investigate the role that chatbots - Artificial Intelligence (AI) and rule-based - may play in supporting victims/survivors in situations such as these or where direct access to help is limited. Methods: Interviews were conducted with experts working in domestic abuse support services and organizations (e.g., charities, law enforcement) and the content of websites of related support-service providers was collected. Thematic content analysis was then applied to assess and extract insights from the interview data and the content on victim-support websites. We also reviewed pertinent chatbot literature to reflect on studies that may inform design principles and interaction patterns for agents used to support victims/survivors. Results: From our analysis, we outlined a set of design considerations/practices for chatbots that consider potential use cases and target groups, dialog structure, personality traits that might be useful for chatbots to possess, and finally, safety and privacy issues that should be addressed. Of particular note are situations where AI systems (e.g., ChatGPT, CoPilot, Gemini) are not recommended for use, the value of conveying emotional support, the importance of transparency, and the need for a safe and confidential space. Conclusion: It is our hope that these considerations/practices will stimulate debate among chatbots and AI developers and service providers and - for situations where chatbots are deemed appropriate for use - inspire efficient use of chatbots in the support of survivors of domestic abuse.<|reference_end|>
@article{saglam2024designing,
    title={Designing Chatbots to Support Victims and Survivors of Domestic Abuse},
    author={Rahime Belen Saglam, Jason R. C. Nurse, Lisa Sugiura},
    journal={arXiv preprint arXiv:2402.17393},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17393},
    primaryClass={cs.CY}
}