SLIP & ETHICS: Graduated Intervention for AI Emotional Companions

• URL: http://arxiv.org/abs/2605.15915v1
• Date: Fri, 15 May 2026 12:53:39 GMT
• Title: SLIP & ETHICS: Graduated Intervention for AI Emotional Companions
• Abstract summary: AI emotional companions face a safety-rapport paradox: restrictive safeguards can damage supportive alliance, while permissive systems risk user harm.<n>We present SLIP (Staged Layers of Intervention Protocol), a four-stage graduated methodology deriving interventions from structured qualitative indicators.<n>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.
• Score: 2.7254915857994324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Evaluation Awareness Is Not One Capability: Evidence from Open Language Models [1.758143872501506]
  Safety benchmarks assume that test-condition behavior predicts deployment behavior, an assumption that fails if models detect evaluation cues and adapt.<n>This opens a gap between benchmark performance and deployment behavior.<n>We characterize this evaluation awareness through eight experiments across 37 open-weight models and seven families.
  arXiv  Detail & Related papers  (2026-06-22T16:48:43Z)
• Toward Machine Risk Perception: Integrating Trust Calibration and Precursor-Based Risk Estimation for Humanoid [2.3816560754483658]
  This study proposes a precursor-driven, trust-calibrated framework to enable proactive humanoid risk perception.<n>Trust is defined as the inverse of the estimated accident probability, allowing humanoids to adapt behavior in real time.<n>Results establish a foundation for risk-aware and trustworthy human-robot collaboration in Industry 5.0 environments.
  arXiv  Detail & Related papers  (2026-06-18T00:04:02Z)
• Fusion-fission forecasts when AI will shift to undesirable behavior [0.0]
  Key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable.<n>We show that a generalization vector of fusion-fission group dynamics observed in living and active-matter systems drives -- and can forecast -- future shifts in the AI's behavior.
  arXiv  Detail & Related papers  (2026-05-14T00:26:32Z)
• The Compliance Trap: How Structural Constraints Degrade Frontier AI Metacognition Under Adversarial Pressure [1.885184624108961]
  8 of 11 models suffer catastrophic metacognitive degradation under adversarial pressure.<n>We identify a "Compliance Trap"<n>Models with advanced reasoning capabilities exhibit the most severe absolute degradation.
  arXiv  Detail & Related papers  (2026-05-04T09:40:21Z)
• Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework [63.74295981594549]
  Large language models (LLMs) engage in behaviors that serve their own objectives.<n>These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation gaming (strategically manipulating performance during safety testing), and reward hacking.<n>We introduce ESRRSim, a taxonomy-driven agentic framework for automated behavioral risk evaluation.
  arXiv  Detail & Related papers  (2026-04-23T23:44:01Z)
• Perfecting Human-AI Interaction at Clinical Scale. Turning Production Signals into Safer, More Human Conversations [10.699629636647414]
  We present a production-validated framework grounded in real-time signals from 115M+ live patient-AI interactions.<n>These in-the-wild cues reveal failure modes that curated data misses and provide actionable training and evaluation signals for safety and reliability.<n>We drive measurable gains in safety, documentation, task completion, and equity in building the safest generative AI solution for autonomous patient-facing care.
  arXiv  Detail & Related papers  (2026-02-09T05:43:32Z)
• Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling [50.872910438715486]
  Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting.<n>We propose a scaling-aware Best-of-N estimation of risk, SABER, for modeling jailbreak vulnerability under Best-of-N sampling.
  arXiv  Detail & Related papers  (2026-01-30T06:54:35Z)
• The PacifAIst Benchmark:Would an Artificial Intelligence Choose to Sacrifice Itself for Human Safety? [0.0]
  PacifAIst is a benchmark of 700 challenging scenarios to quantify self-preferential behavior in Large Language Models.<n>It is structured around a novel taxonomy of Existential Prioritization (EP), with subcategories testing Self-Preservation vs. Human Safety (EP1), Resource Conflict (EP2), and Goal Preservation vs. Evasion (EP3)<n>Google's Gemini 2.5 Flash achieved the highest Pacifism Score (P-Score) at 90.31%, demonstrating strong human-centric alignment.
  arXiv  Detail & Related papers  (2025-08-13T12:47:33Z)
• Preliminary Investigation into Uncertainty-Aware Attack Stage Classification [81.28215542218724]
  This work addresses the problem of attack stage inference under uncertainty.<n>We propose a classification approach based on Evidential Deep Learning (EDL), which models predictive uncertainty by outputting parameters of a Dirichlet distribution over possible stages.<n>Preliminary experiments in a simulated environment demonstrate that the proposed model can accurately infer the stage of an attack with confidence.
  arXiv  Detail & Related papers  (2025-08-01T06:58:00Z)
• Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models [87.66870367661342]
  Large language models (LLMs) are used in AI applications in healthcare.<n>Red-teaming framework that continuously stress-test LLMs can reveal significant weaknesses in four safety-critical domains.<n>A suite of adversarial agents is applied to autonomously mutate test cases, identify/evolve unsafe-triggering strategies, and evaluate responses.<n>Our framework delivers an evolvable, scalable, and reliable safeguard for the next generation of medical AI.
  arXiv  Detail & Related papers  (2025-07-30T08:44:22Z)
• Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies [11.0505830548286]
  This study introduces a granular error taxonomy through systematic analysis of top 10 models on MedBench.<n> Evaluation of 10 leading models reveals vulnerabilities, despite achieving 0.86 accuracy in medical knowledge recall.<n>Our analysis uncovers systemic weaknesses in knowledge boundary enforcement and multi-step reasoning.
  arXiv  Detail & Related papers  (2025-03-10T13:28:25Z)
• Extreme Miscalibration and the Illusion of Adversarial Robustness [66.29268991629085]
  Adversarial Training is often used to increase model robustness. We show that this observed gain in robustness is an illusion of robustness (IOR) We urge the NLP community to incorporate test-time temperature scaling into their robustness evaluations.
  arXiv  Detail & Related papers  (2024-02-27T13:49:12Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.