Human and AI judgements of restorative responses to sexual abuse: The role of perceived severity

Purpose: Restorative justice (RJ) offers an alternative or complement to criminal justice in sexual abuse cases, yet the public remains ambivalent. Given that survivors increasingly turn to large language models (LLMs) for information and support when coping with sensitive justice-related decisions, the present study examined whether the guidance LLMs provide regarding RJ in sexual abuse cases resembles human views. Methods: Participants (N = 469) were randomly assigned to read a vignette describing intra- or extrafamilial sexual abuse and rated offence severity and support for RJ. Their responses were compared with those generated by Gemini, Grok and DeepSeek (120 total outputs). Results: Both humans and LLMs perceived intrafamilial sexual abuse as more severe than extrafamilial abuse. However, in both contexts, LLMs produced significantly higher numeric ratings of RJ support than did humans under the structured prompting conditions used. Among humans, perceived severity fully mediated the relationship between abuse context and RJ support, such that higher severity reduced openness to RJ. Among LLMs, this mediation was partial, such that heightened severity did not serve as a categorical veto against RJ. Significant differences were observed between models. Conclusions: Under structured prompting conditions, LLM-generated outputs acknowledged abuse severity while remaining relatively open to RJ, a pattern consistent with a cautious, descriptive use of the ‘mirror cleaner’ metaphor. The findings have important implications for survivors who turn to AI systems for information and support, underscoring both their potential and the need for careful ethical curation.