On the Use of Chatbots to Report Non-consensual Intimate Images Abuses: The Legal Expert Perspective

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Abstract

Non-consensual intimate images (NCII) violence, also known as "non-consensual pornography"or "revenge pornography", is the disclosure or sharing of sexually explicit images/videos without the consent of the people depicted or a legitimate purpose. Perpetrators may gain access to the media in different ways, including consensual exchange in the context of a relationship, stolen from devices, captured using hidden cameras, or recorded during sexual assaults. Since the perpetrator aims to publicly shame and humiliate the victim by spreading the media through technological tools, the crime is considered a technology-facilitated abuse that has severe consequence also outside the online context. Law enforcement authorities point out that immediate, strong, and effective reactions are crucial to successful takedown procedures and investigations. However, affected people face difficulties in reporting the abuse and, to have fair access to justice system. More in particular, online platforms such as messaging applications, social media and video hosting websites do not share common and standardised procedures for reporting NCII violence. In some cases, it is even not possible to report specific media content as NCII. Often reporting an abuse may require the victims to fill a free text narrative of the crime, which could lead to blank page anxiety, concrete risks of second-victimisation and worsening the access to justice. Several studies demonstrated how expert-systems, such as chatbots, may support end users to access justice procedures. Therefore, in this exploratory research, we introduce a prototype of a declarative flow-based chatbot for supporting victims of NCII abuses in reporting the case, with a preliminary (criminal) expert-based evaluation.

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Falduti, M., & Tessaris, S. (2022). On the Use of Chatbots to Report Non-consensual Intimate Images Abuses: The Legal Expert Perspective. In ACM International Conference Proceeding Series (pp. 96–102). Association for Computing Machinery. https://doi.org/10.1145/3524458.3547247

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