Is AI hijacking your intent? A formal control algorithm to measure it
Details
- External ID
- 46575619
- Source
- HN
- Company
- —
- Product
- —
- Website domain
- —
- Launched
- Jan. 11, 2026
- Cohort
- —
- Upvotes
- 10
- Upvotes percentile
- 0.5006587615283268
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
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”).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.Algorithm 1 (on pp.16–17 of the linked white paper) formally defines the metric:1. D = CalculateDistance(VisualState, LogicalState)2. IF D < α : optimization (Reduce Update Rate)3. ELSE IF α ≤ D < β : warning (Apply Visual/Haptic Modifier proportional to D)4. ELSE IF β ≤ D < γ : intervention (Modulate Input / Synchronization)5. ELSE : security (Execute Defensive Protocol)The full paper is available on Zenodo: https://doi.org/10.5281/zenodo.18206943
Enrichment
- Theme
- Vertical
- Security
- Function
- Observability & eval
- Audience
- B2B
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- measure ai intent hijacking
- Manually corrected
- False
Could you build this?
No Developing a formal mathematical control-theory algorithm and valid theoretical metric to quantify human-AI intent alteration requires novel academic research and control theory expertise.
What it would actually take: Executing this requires formal control theory, state-space modeling, and cognitive psychology/HCI research to mathematically represent human intentionality and system divergence. The implementation would likely involve probabilistic modeling (e.g., POMDPs or dynamic Bayesian networks) evaluated against rigorous human-in-the-loop experimental testbeds. This demands PhD-level expertise in control systems, statistical inference, and AI safety rather than application development.
Discussion
20 comments analyzed.
Concerns raised: Lacks rigorous mathematical or scientific proof; conceptual math, not formal, Key terms like 'perceptibility of judgement' and 'ontological deception' not rigorously defined, Reframes between engineering and philosophy to avoid falsifiability, Unclear ethical basis for transparency principle, Overreaches in explanatory power; susceptible to LLM-assisted confirmation bias
Competitors
Other products that read as similar to this one — 88 launches clear the similarity bar, closest 8 shown.
Attention rank: #41 of 89 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 67 days after the earliest competitor.
- Synartesis · ph · 2026-09-15 · 1 upvotes · similarity 0.46
- Parameter: AI that Hacks before Attackers do · yc · 2026-02-09 · 303 upvotes · similarity 0.42
- Offensive-Security-AI-Models · github · 2026-09-27 · 465 upvotes · similarity 0.41
- Benchmark your team's AI coding security posture · hn · 2025-11-05 · 5 upvotes · similarity 0.40
- Lineation · hn · 2026-07-15 · 6 upvotes · similarity 0.40
- AI agent got 237 rules from another agent, still made the same mistakes · hn · 2026-03-24 · 6 upvotes · similarity 0.39
- Intent-Router · github · 2026-09-22 · 129 upvotes · similarity 0.39
- SeenRelay · ph · 2026-09-28 · 1 upvotes · similarity 0.39
Other launches for this product
- No other launches for this product.
Same idea, different domain
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