TL;DR
An independent researcher submitted a white paper to the EU AI Office proposing the Judgment Transparency Principle (JTP) and a public-domain metric called State Discrepancy (D) to quantify how much an AI system shifts a user's intent. The proposal includes a threshold-based control algorithm (Algorithm V1) and a call for engineers and researchers to test, critique, and implement the idea.
What happened
An independent researcher posted a white paper to the EU AI Office (CNECT-AIOFFICE) describing a formal framework aimed at protecting human autonomy when interacting with opaque AI systems. The paper, titled the Judgment Transparency Principle (JTP), introduces State Discrepancy (D) as a measurable variable representing the gap between a user's expressed intent and the system's resulting state. The submission includes Algorithm V1 (Algorithm 1 in the paper, pp.16–17), which computes D from VisualState and LogicalState and then applies tiered responses using thresholds α, β and γ: small discrepancies reduce update frequency; moderate ones trigger proportional visual or haptic cues; larger gaps prompt input modulation or synchronization; extreme cases invoke defensive security protocols. The researcher frames this as a public-domain Safe Harbor for designers, argues it can prevent erosion of agency and regulatory uncertainty, and explicitly asks the global engineering community to stress-test and refine the model. The full paper is hosted on Zenodo (doi:10.5281/zenodo.18206943).
Why it matters
- Attempts to convert vague notions of “manipulation” into a concrete engineering metric could give regulators and designers a common operational standard.
- A measurable discrepancy between user intent and system actions could make AI interactions more auditable and less opaque.
- Providing a math-based Safe Harbor aims to let product teams innovate UI/UX while maintaining built-in integrity checks.
- The researcher warns that unchecked erosion of agency could provoke social rejection of AI; the proposal frames prevention as a matter of maintaining trust.
Key facts
- Paper title: The Judgment Transparency Principle (JTP).
- Submitted to: EU AI Office (CNECT-AIOFFICE).
- Proposed metric: State Discrepancy (D), a public-domain variable to quantify how much an AI system changes user intent.
- Algorithm V1 (Algorithm 1 in the paper) computes D = CalculateDistance(VisualState, LogicalState) and applies actions based on thresholds α, β, γ.
- Tiered responses in the algorithm: reduce update rate; apply visual/haptic modifier proportional to D; modulate input/synchronize; execute defensive protocol.
- Algorithm 1 appears on pages 16–17 of the white paper.
- The author frames the metric as a ‘Safe Harbor’ to allow UX innovation with integrity-by-design.
- The full manuscript is available on Zenodo at DOI 10.5281/zenodo.18206943.
- The researcher invited the engineering community to stress-test, critique, and help implement V1 into a living standard.
What to watch next
- Responses and technical critiques from the engineering and research community on the Hacker News thread and elsewhere, as requested by the author.
- Efforts to translate Algorithm V1 into high-dimensional, production systems and any prototype or implementation work that emerges.
- not confirmed in the source
- not confirmed in the source
Quick glossary
- State Discrepancy (D): A numerical measure proposed to quantify the difference between a user's intended state and the AI-driven outcome.
- Judgment Transparency Principle (JTP): The proposed framework and white paper concept aiming to ground user autonomy in a measurable, engineering-oriented principle.
- VisualState vs LogicalState: Conceptual inputs to the metric where VisualState reflects the presented interface state and LogicalState represents the inferred or intended user state.
- Safe Harbor: A design or legal concept offering clear boundaries that let practitioners innovate while complying with specified integrity or safety requirements.
- Black-box AI: AI systems whose internal decision processes are opaque or not directly interpretable by users or operators.
Reader FAQ
Who authored the paper?
An independent researcher; no personal name is provided in the source.
What is the core goal of the proposal?
To replace vague legal and philosophical notions of manipulation with a concrete engineering variable (State Discrepancy) to protect human autonomy.
Where can I read the full paper?
The paper is available on Zenodo at DOI 10.5281/zenodo.18206943.
Is there an implementation or timeline for adoption?
not confirmed in the source
Hi HN, I recently submitted a white paper on State Discrepancy (D) to the EU AI Office (CNECT-AIOFFICE). This paper, "The Judgment Transparency Principle (JTP)," is my attempt to provide…
Sources
- Show HN: Is AI hijacking your intent? A formal control algorithm to measure it
- hckr news – Hacker News sorted by time
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