Show HN: Is AI hijacking your intent? A formal control algorithm to measure it
I’m an independent researcher proposing State Discrepancy, a public-domain metric to quantify how much an AI system changes a user’s intent (“the Ghost”).<p>The goal: replace vague legal and philosophical notions of “manipulation” with a concrete engineering variable. Without clear boundaries, AI faces regulatory fog, social distrust, and the risk of being rejected entirely.<p>Algorithm 1 (on pp.16–17 of the linked white paper) formally defines the metric:<p>1. D = CalculateDistance(VisualState, LogicalState)<p>2. IF D < α : optimization (Reduce Update Rate)<p>3. ELSE IF α ≤ D < β : warning (Apply Visual/Haptic Modifier proportional to D)<p>4. ELSE IF β ≤ D < γ : intervention (Modulate Input / Synchronization)<p>5. ELSE : security (Execute Defensive Protocol)<p>The full paper is available on Zenodo: <a href="https://doi.org/10.5281/zenodo.18206943" rel="nofollow">https://doi.org/10.5281/zenodo.18206943</a>
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I’m an independent researcher proposing State Discrepancy, a public-domain metric to quantify how much an AI system changes a user’s intent (“the Ghost”).<p>The goal: replace vague legal and philosophical notions of “manipulation” with a concrete engineering variable. Without clear boundaries, AI faces regulatory fog, social distrust, and the risk of being rejected entirely.<p>Algorithm 1 (on pp.16–17 of the linked white paper) formally defines the metric:<p>1. D = CalculateDistance(VisualState, LogicalState)<p>2. IF D < α : optimization (Reduce Update Rate)<p>3. ELSE IF α ≤ D < β : warning (Apply Visual/Haptic Modifier proportional to D)<p>4. ELSE IF β ≤ D < γ : intervention (Modulate Input / Synchronization)<p>5. ELSE : security (Execute Defensive Protocol)<p>The full paper is available on Zenodo: <a href="https://doi.org/10.5281/zenodo.18206943" rel="nofollow">https://doi.org/10.5281/zenodo.18206943</a>