The AI inversion model: a linear negative-constraint framework for auditable alignment in medical decision-making

Abstract The integration of artificial intelligence (AI) into healthcare systems is increasingly hindered by the AI alignment problem. In high-stakes domains such as clinical triage, algorithms frequently reflect and amplify systemic biases. Current alignment methodologies, including Reinforcement Learning from Human Feedback (RLHF), attempt to encode subjective human morality through opaque architectural pipelines, which can exacerbate the “black box” dilemma. To address this, this paper proposes the “AI Inversion Model”, a theoretical Proof-of-Concept (PoC) framework utilizing inference-time negative constraints. Rather than attempting to compute elusive positive virtues, the model draws clinical inspiration from the psychopathic spectrum - specifically extreme utilitarianism - to define strict “anti-moral” boundaries. These boundaries are operationalized through a “Genesis” anchor system, which autonomously and recursively calibrates negative anchors by synthesizing supreme legal precedents and ethical literature. Beyond static distance, the model evaluates the mathematical kinematics of AI outputs - including vector angles and directional trajectories - to quantify algorithmic “Good Faith”. A step-by-step trace of complex surgical simulations illustrates how the filter identifies utilitarian biases and enforces a human-in-the-loop protocol when variance exceeds a heuristic 10% threshold. While not a definitive resolution to the black-box problem, this model offers a targeted, auditable baseline of “functional compassion” to mitigate systemic bias in medical AI governance.