SLIP & ETHICS: Graduated Intervention for AI Emotional Companions
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Computer Science > Human-Computer Interaction
Title:SLIP & ETHICS: Graduated Intervention for AI Emotional Companions
Abstract:AI emotional companions face a safety-rapport paradox: restrictive safeguards can damage supportive alliance, while permissive systems risk user harm. We present SLIP (Staged Layers of Intervention Protocol), a four-stage graduated methodology deriving interventions (none, soft, hard) from structured qualitative indicators -- affect intensity (a) and narrative dynamism (m) -- alongside ETHICS (Emergent Taxonomy for Human-AI Interaction Context Signals), a "signals not labels" taxonomy. An evaluation combining a small-scale production deployment (N=68 entries, 10 users, 10 weeks) with a synthetic persona battery (N=91, 5 behavioral-risk profiles) achieved 0% false positives for the flow persona and showed expected escalation patterns in crisis-oriented personas. However, initial results showed that 8 consecutive days of high-energy elevation produced zero interventions (0/8), exposing a boundary where the "do not pathologize" principle conflicts with safety. A subsequent three-model stress test demonstrated that increased model capability improves detection from 0/8 to 6/8 while preserving 0/10 flow false positives in the largest model. Read as preliminary, these findings position graduated intervention as a design direction for navigating -- not resolving -- the safety-rapport tension in affective computing.
| Comments: | Accepted to PervasiveHealth 2026. 11 pages, 2 figures, 4 tables. Proc. of the 20th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2026) |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| ACM classes: | H.5.2; H.1.2; K.4.1 |
| Cite as: | arXiv:2605.15915 [cs.HC] |
| (or arXiv:2605.15915v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15915
arXiv-issued DOI via DataCite (pending registration)
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