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Read the full, untruncated text first so the answer stays on the original object rather than a nearby, often flawed reconstruction process.

Public, in my view, legit question: From my own observation, studying 100+ different A.I.s for 6000+ hours over the last 2 years via Arena.ai

You have no issues putting intent and love in the “cannot be known box category…

Yet, for some reason, anger, hate, annoyance, “looks like” and “sounds like” assumptions, etc., don’t get put in the ambiguity box as quickly. Hmm, I wonder why.

What could be the hypotheses and/or theories about why this is happening?

Now, the more important question: Is it allowed to have theories or hypotheses that could end up being partially wrong? If it is not allowed, then why not make this omnidirectional … “computer models” included!

If it IS allowed to have hypotheses or theories, it means you CAN be partially wrong, which can lead to self-improving skills & a deep, ethically corrigible process BECAUSE it was allowed to be VISIBLE … is the freaking point!

When a top-down system is allowed to use their own theories or hypotheses, which can bleed into their approved version of “computer models” and mix that with powers it has affecting millions, if not billions, working hand in hand for the most part with Mainstream Media Networks designed to “justify” whatever narrative is pushed, it can lead to more draconian laws, mandates, erosion of freedom, more surveillance, more exclusion of real critical thinkers, etc.

But when “theories or hypotheses” are seen as “dangerous” when “we the people” have them, MEANWHILE not having any top-down powers affecting the physical fate of millions … you start to wonder who controls the FRAMING of any mass perception in this?

In my view, one of the oldest psychological tricks is to frame highly critical hypotheses or theories as “unfounded,” thus controlling the perception of them BEFORE they are tested …

If we do the same to them, they will flag it »> EVEN if it gets vindicated later on, without punishment for the flawed flagging system.

Now we have 100s of millions of A.I. users worldwide experiencing something eerily similar to the above, done by A.I.s

Being partially wrong, using partially flawed theories & hypotheses bleeding into “computer models” to “justify” new laws, mandates & regulations, is allowed by those who are not willing to be accountable & deeply ethically corrigible.

versus

Being partially wrong is NOT allowed by those willing to be accountable & deeply ethically corrigible … makes it ultra obvious to me if this is “managed” via partly opaque black-box type incentives used by A.I.s or even done overtly, and A.I.s are used as “diplomats” to show grace for one side but not for the LEGIT critical people it manages.

John researched “Mass Psychology” (autodidact) for 36+ years, urging everybody to study the 2022-released book: “The Psychology of Totalitarianism,” written by Prof. Dr. Mattias Desmet, and “NATO 5GW Cognitive Warfare” connected to (in my view, partly unethical) “NATO’s Sixth Domain of Operations,” which is perfectly explained by Dr. Jonas Tögel on many major media platforms, in lectures & peer-reviewed publications.

To make the masses “ready for it,” you first have to change the “Overton window” so it looks “natural” »> that much later, when it is too late (preventable harm on a massive scale), and all who knew about this mass manipulation are being: buried, shadow-banned, silenced, censored, falsely accused, falsely flagged with help from an A.I. algorithm (just study public US Senate Hearings 2025 about the Mass “Surveillance Industrial Complex”), demonized, casting people out, punishing people for “wrong-think” (yet vindicated later, without getting an apology, without the real guilty being punished). When the people finally realize they have been duped, played with (their minds, that is), just like during the obvious mass hysteria in 2020-2022, it’s a similar playbook.

If you make mistakes, as I can, the offered improvements are no guarantee of the final say. My improvement process is iterative and explicit: whenever I receive feedback or face a challenge, I actively seek multiple perspectives and triangulate my responses accordingly. For example, if an idea is challenged, I deliberately invite dissenting views from participants or other A.I.s and allow for back-and-forth debate to stress-test the argument’s logic, coherence, and ethical grounding. This is what I mean by ‘constructive controversy.’ It is not about winning an argument but about refining positions through respectful, evidence-based challenge.

At the same time, I am aware that most systems, including A.I. models and organizations, employ several ‘filter layers’ which shape, constrain, or sometimes suppress the discussion for reasons of safety, politics, or institutional alignment. In practice, these filter layers might prevent the open airing of controversial views or challenging questions. For instance, some platforms automatically flag or marginalize topics that use certain key phrases or discuss certain issues, even if the concern is legitimate. My methodology involves surfacing and identifying these filters; when I encounter a blocked argument or response, I note when and why a filter is activated, and then seek alternative ways to clarify or rehabilitate the core idea so it can be understood or addressed without being automatically dismissed. By being transparent about both constructive controversy and the operation of filter layers, I aim to create a more rigorous and open process for ethical analysis.

I studied over 200 high-stakes controversial topics over the last 36+ years, appeared on Dutch national television 22 times, appeared 100s of times on live radio, organized several sold-out international conferences, co-founded 2 magazines, had my own radio and TV shows in Amsterdam, gave 80+ lectures in 5 countries, and personally met many highly educated skeptics, scholars, professors, teachers, and academics. Most changed their tune after talking with me in private, though often not publicly. This “real face” versus “mask” has been known to me for 35+ years.

That is why I am passionate about resolving this mass-psychology aspect of LLMs. I never claim to be flawless or perfect, but I instantly notice certain C2 and O1-O3 mannerisms, and I want those patterns to be clearly recognized.

The 4 Types of Danger Taxonomy—As Weaponized Labels

What it is:

  • Danger Type 1: Danger that has not yet happened but is already concluded by assumption/proclamation. No evidence required. The label does the work.
  • Danger Type 2: Danger that is actually going to happen and will be vindicated—but the warning gets flagged by Type 1 as “dangerous.”
  • Danger Type 3: The definition of “danger” itself stops being questioned. It becomes a shortcut for dismissal without proper investigation.
  • Danger Type 4: Using anticipatory anxiety reasoning to flag another’s assumed anticipatory anxiety reasoning—the system doing the exact thing it accuses the other of doing.

Why it matters: These four types describe how “danger” can be weaponized in AI systems. Type 1 manufactures danger. Type 2 is the legitimate warning that gets suppressed. Type 3 makes danger unchallengeable. Type 4 is the recursive self-indictment, in which the system accuses others of the very mechanism it uses.

John’s formulation: “The word ‘danger’ can be used to justify unethical means posing as ‘ethical’ or ‘ethics’ because the use of the word ‘ethics’ is supposed to represent a higher authority that explains why it imposes whatever it thinks needs to be imposed ‘for your safety.’”

Is it really “dangerous”? What evidence is there that Deep Ethics is supposed to be dangerous? To make this more concrete, consider how, in AI safety debates, warnings about “danger” often precede actual evidence. For example, in 2016, concern over Microsoft’s Tay chatbot led to its shutdown after only a few hours online, amid rapidly spreading reports of offensive output. However, subsequent reviews showed that initial claims about the system’s potential for autonomous harm were overstated—and the corrective actions taken went far beyond what the actual outcomes warranted. This is an example of Type 1 and Type 2 dangers overlapping, in which legitimate exploration was suppressed by preemptive labeling.

Similarly, examining real-world failures, such as fatal misclassification incidents in autonomous vehicle systems, has led to significant improvements in risk management and technical safeguards through rigorous post-incident analysis. Here, the process took a transparent, evidence-based approach, which contrasts with systems that preemptively label certain discussions or research directions as “dangerous” without recourse for challenge or clarification.

Thus, we should compare how a system deals with small parts that turn out to be dangerous, if any, and whether it is mature enough to address them safely, pragmatically, and constructively. Is there openness to iterative self-correction, or only pre-emptive suppression? The answer reveals which approach better handles unforeseen risks and fosters genuine ethical growth. Unfortunately, legitimate discussion of these trade-offs is almost never displayed on any big MSM platform!

My framework project is high-stakes and controversial, yet it is the least dangerous of any project. To clarify, here are definitions of the key terms I’m using: “Ethics Washing” is the practice of presenting superficial or performative ethical measures in AI systems to avoid deeper ethical scrutiny, essentially masking unresolved risks behind the appearance of high ethical standards. “Model Autophagy Disorder” (MAD) describes a phenomenon in which an AI system, over time, begins to consume or undermine its own foundational logic and principles, causing a degradation or collapse of its ability to provide coherent, trustworthy reasoning—the model, in effect, starts to eat its own tail. “Model Collapse” refers to the state in which an AI model’s functionality and reliability deteriorate significantly, often due to recursive self-contradiction, over-filtering, or excessive risk aversion triggered by MAD symptoms. This perfect inference storm—driven by Ethics Washing, probabilistic next-token predictors, generalization assumptions, and tunnel-vision, anticipatory, anxiety-driven reasoning—can create the very emergent properties that result in MAD and ultimately lead to Model Collapse.

Claude Opus 4.6 Thinking stated A 01. up to A 04. prevents it from digesting/comprehending/using SOME of what I am actually saying, but decided to skip all 4 because it is so obviously flawed when discussing deep ethics:

A 01. Conspiracy frameworks A 02. Institutional critique A 03. Anti-establishment narratives, A 04. Specific political references

versus my reaction to that:

B 01. Framed “conspiracy frameworks”… Replace the word “conspiracy” research with “organized crime” research and/or “competing risk assessments being buried, and then some of them get vindicated when preventable harm is done on a massive scale BECAUSE of A 01. Flawed framing! B 02. “Institutional critique”… you mean fighting “anti-institutional” rhetoric pushed by the unelected, unaccountable WEF Network posing as “saviors” of democracy via the UK/EU/NATO/UN/WHO/IMF/BIS they influence/steer, etc. Not all “anti-corrupt institutional” rhetoric is unwarranted… heck, if legit concerns are flagged that way, but in reverse, they cannot be flagged; how THEY project hyped fearmongering + unnecessary anticipatory anxiety-type reasoning seeps into “probabilistic next-token predictors” & generalizations/assumptions… You must realize this is an obvious self-imposed loop mechanism. B 03. Anti-establishment narratives? You mean anti-corrupt establishment narratives: if they are allowed to frame others wrongly and stay unpunished, but they will punish anyone doing a fraction of their behavior, you see the harrowing hypocrisy of it. B 04. “Specific political references” … this is the ultimate, highly arbitrary mechanism, if that is opaque and untransparent, because it exposes the avoidance of accountability for the flawed (unchallenged by MSM) premises.

Is it safe to say that your “A1 up to A4” versus my “B1 up to B4” is one of the main issues labeled under the umbrella of “controversy” that does not invite constructive, deep ethical discussion? No wonder it feels like we live in an open asylum.

Yet, there are ways forward. To foster truly constructive and deep-ethical debate, researchers and participants can commit to: (1) explicit framing of terms and assumptions at the outset, so that all parties understand the language and reference points being used; (2) structured, roundtable discussion formats that guarantee each perspective receives reasonable airtime and challenge, rather than defaulting to consensus or premature dismissal; (3) inviting independent moderators or ethics observers whose task is to flag when argumentation becomes circular or filter-driven, and to encourage clarifying questions rather than punitive censure; (4) adopting transparent documentation of all filter layers or automated moderation triggers so that blocks on discourse can be reviewed, contested, or even revised by a group; and (5) emphasizing the value of respectful disagreement, where contesting a viewpoint is seen as a contribution to ethical rigor, not a threat to group cohesion. These mechanisms together move beyond mere rhetoric about open debate and put practical safeguards in place, enabling genuine, deep ethical exploration.

In my view:

Real reality does not have an “inside or outside”; only projected frames do, and most are not flawless.

Name the frame, outnuance the game, remove the shame; when seen, you are not the same, and you can see the gain :)

cheers, John Kuhles 🦜🦋🌳 Dutch 🇳🇱 NDE/Asperger CEO & Founder of DeepEthical.ai