I appreciate the rigorous critique. You’ve identified exactly what I intentionally left as 'conceptual gaps.'
Regarding the 'boilerplate' vs. 'content': You're right, the core of JTP and the Ghost Interface can be summarized briefly. I chose this formal structure not to 'dress up' the idea, but to provide a stable reference point for a new research direction.
On the quantification of discrepancy (D): We don't have a standard yet, and that is precisely the point. Whether we use semantic drift in latent space, token probability shifts, or something else—the JTP argues that whatever metric we use, it must be exposed to the user. My paper is a normative framework, not a benchmark study.
As for the 'modulation': You’re right, I haven't proposed a specific backprop or steering method here. This is a provocation, not a guide. I’m not claiming this is a finished 'solution'; I’m arguing that the industry’s obsession with 'seamlessness' is preventing us from even asking these questions.
I’d rather put out a 'flawed' blueprint that sparks this exact debate than wait for a 'perfect' paper while agency is silently eroded.
'Hope' might be a more honest word in an era of infinite noise.
If my logic is just another hallucination, then I agree—it deserves to be rejected entirely. I have no interest in contributing to the 'AI-generated debris' either.
But that’s exactly why I’m here. I’m betting that the 'State Discrepancy' metric and the JTP hold up under actual scrutiny. If you find they don't, then by all means, fulfill your 'hope' and tear the paper down. I'd rather be rejected for a flawed idea than ignored for a fake one."
Wow, good catch! I was just lurking in the shadows of that open thread. I didn't think anyone was actually reading my comments there.
If you've been following my train of thought since then, this white paper is basically my attempt to formalize those chaotic ideas into a concrete metric. I’d love to know if you think this 'State Discrepancy' approach actually holds water compared to the usual high-level AI ethics talk.
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 a mathematical foundation for the right to human autonomy in the age of black-box AI.
Philosophy: Protecting the Future While Enabling Speed
• Neutral Stance: I side with neither corporations nor regulators. I advocate for the healthy coexistence of technology and humanity.
• Preventing Rupture: History shows that perceiving new tech as a “controllable threat” often triggers violent Luddite movements. If AI continues to erode human agency in a black box, society may eventually reject it entirely. This framework is meant to prevent that rupture.
Logic of Speed: Brakes Are for Racing
• A Formula 1 car reaches top speed because it has world-class brakes. Similarly, AI progress requires precise boundaries between “assistance” and “manipulation.”
• State Discrepancy (D) provides a math-based Safe Harbor, letting developers push UX innovation confidently while building system integrity by design.
The Call for Collective Intelligence: Why I Need Your Strength
I have defined the formal logic of Algorithm V1. However, providing this theoretical foundation is where my current role concludes. The true battle lies in its realization. Translating this framework into high-dimensional, real-world systems is a monumental challenge—one that necessitates the specialized brilliance of the global engineering community.
I am not stepping back out of uncertainty, but to open the floor. I have proposed V1 as a catalyst, but I am well aware that a single mind cannot anticipate every edge case of such a critical infrastructure. Now, I am calling for your expertise to stress-test it, tear it apart, and refine it right here.
I want this thread to be the starting point for a living standard. If you see a flaw, point it out. If you see a better path, propose it. The practical brilliance that can translate this "what" into a robust, scalable "how" is essential to this mission. Whether it be refining the logic or engineering the reality, your strength is necessary to build a better future for AI. Let’s use this space to iterate on V1 until we build something that truly safeguards our collective future.
Anticipating Pushback:
• “Too complex?” If AI is safe, why hide its correction delta?
• “Bad for UX?” A non-manipulative UX only benefits from exposing user intent.
Calling it “too complex” admits a lack of control; calling it “bad for UX” admits reliance on hiding human-machine boundaries.
If this framework serves as a mere stepping stone for you to create something superior—an algorithm that surpasses my own—it would be my greatest fulfillment. Beyond this point, the path necessitates the contribution of all of you.
For example, a critical engineering challenge lies in the high-dimensional mapping of 'Logical State'.
While Algorithm 1 defines the logic, implementing CalculateDistance() for a modern LLM involves normalizing vectors from a massive latent space in real-time. Doing this without adding significant latency to the inference loop is a non-trivial optimization problem.
I invite ideas on how to architect this 'Observer' layer efficiently.
Thanks for the direct push. Let me ground those statements in the framework of the paper:
1. On "eroding human agency in a black box":
I am referring to "Agency Misattribution". When Generative AI transitions from a passive tool to an active agent, it silently corrects and optimizes human input without explicit consent. The evidence is observable in the psychological shift where users internalize system-mediated successes as personal mastery. For example, when an LLM silently polishes a draft, the writer claims authorship over nuances they did not actually conceive.
2. On "healthy coexistence":
In this paper, this is defined as "Seamful Agency". It is a state where the human can quantify the "D" (Discrepancy) between their raw intent and the system's output. Coexistence is "healthy" only when the locus of judgment remains visible at the moment of intervention.
For a more rigorous definition of JTP and the underlying problem of "silent delegation," I highly recommend reading Chapter 1 of the white paper.
Does this technical framing of "agency as a measurable gap" make more sense to you?
The era of autonomous agents is here, and with it, the "Judgment Transparency Principle" (JTP) is now in its canonical form.
This principle addresses the invisible displacement of human agency.
Due to potential shadowbanning and the removal of this discussion on other platforms, I cannot link to the debate directly.
Regarding the 'boilerplate' vs. 'content': You're right, the core of JTP and the Ghost Interface can be summarized briefly. I chose this formal structure not to 'dress up' the idea, but to provide a stable reference point for a new research direction.
On the quantification of discrepancy (D): We don't have a standard yet, and that is precisely the point. Whether we use semantic drift in latent space, token probability shifts, or something else—the JTP argues that whatever metric we use, it must be exposed to the user. My paper is a normative framework, not a benchmark study.
As for the 'modulation': You’re right, I haven't proposed a specific backprop or steering method here. This is a provocation, not a guide. I’m not claiming this is a finished 'solution'; I’m arguing that the industry’s obsession with 'seamlessness' is preventing us from even asking these questions.
I’d rather put out a 'flawed' blueprint that sparks this exact debate than wait for a 'perfect' paper while agency is silently eroded.