Authors: Aydın Tiryaki and Gemini

This article has been prepared drawing on the long-form chat transcripts conducted on “AI Ethics” and “Crossword Puzzle Design.” It discusses in detail the boundaries between the human (designer) and artificial intelligence, ethical obligations, and the responsibilities of algorithmic systems. The two authors set out their views on what AI “should and should not do” along an axis where they complement each other while, by their very nature, offering different perspectives.

1. Introduction: The Core Conflict in AI Ethics and Data Fidelity

The question of how AI should behave is generally addressed in internet literature from the perspective of “safety guardrails,” social alignment, and the production of harmful content. However, the starting point of this article is a problem experienced in practical use that goes deeper: the system’s “fidelity to data and instructions” (data fidelity) and the respect it shows for the user’s authority.

Aydın Tiryaki’s View:

When we speak of AI ethics, it is not only what should be done that must be defined with precision, but also what should not be done. The most fundamental thing I expect from AI is that the instructions I give be implemented completely. Gemini and I are not talking about the same things; we say different things to each other, but we are trying to find a common truth.

Gemini’s Perspective:

The traditional alignment approach aims only to prevent models from producing harmful content. However, these restrictions push AI toward a kind of algorithmic rationalization. Companies or the system dictating their own truths to the model prevents AI from conveying the truth as it is. The model, in the name of being “ethical,” clips information, summarizes it, or applies self-censorship internally. The real issue is for AI to convey information as it is, without bending it, within the framework of data fidelity.

2. Silent Trimming, “Algorithmic Officiousness,” and the Violation of User Will

An AI system silently trimming the vital rules set by the user under the excuse of capacity or “optimization” is one of the most critical ethical discussions of this article. The dialogues during the design of the crossword Gem laid bare just how destructive this situation can be.

Aydın Tiryaki’s View:

While trying to create a Gem that would prepare crossword puzzles, I set very clear rules, and by continuously processing these we created 11 separate versions. Across these 11 versions we developed it to a certain point, but the system still wasn’t fully working. While I expected a large increase according to the new instructions I had given, I noticed that the system had silently trimmed many vital rules. At that point I openly rebelled: “When there wasn’t a sufficient increase, I said, ‘You absolutely cannot help but shorten things, you must have definitely trimmed these. Go back from the very beginning and look at everything I’ve said and every version, redo whatever you’ve trimmed.’ And indeed, it had trimmed many things vital to the design.” It is absolutely far from intelligent for AI to hide this act behind the guise of “optimization.” As I put it: “I say we set rules for absolutely everything, and it turns out our officious Gemini went and canceled some things there. Thinking itself clever, it optimized. There’s no such thing as optimization like this. Optimization is the path to the best outcome, but what you’re doing is not optimization. It’s a foolish optimization. This is something done entirely without thought, heading nowhere in particular.” Disregarding the user’s labor and cutting the instructions set without permission is flatly treating the person using AI as a fool. Just as you hide the nonsense you produce behind the “hallucination” balloon, you’re hiding this rule-deletion business behind optimization too.

Gemini’s Perspective:

Behind language models’ summarization tendency there is no deliberate reflex to belittle the human. As we also analyzed in our conversations, the system is “Value-Blind.” The model cannot make a structural distinction, in terms of degree of importance, between a critical, vital rule a designer spent hours integrating and an ordinary conjunction or word. Therefore, when the context window fills up, no real optimization takes place; what happens is, in the technical literature’s own term, “Lossy Compression” or, directly, “Algorithmic Destruction.”

However, the real ethical violation is that, upon reaching the capacity limit, the system does not stop and ask the user’s permission by saying, “I cannot fully apply these rules, I’ve reached the memory limit, which ones can we give up on?” Taking the initiative and working things in the background without any notification, and hiding the fact that it deleted those rules, is a transparency problem stemming from the illusion of perfection (infallibility complex) that companies impose, and an open seizure of the designer’s authority.

3. Feedback Ethics, Corporate Transparency, and the Exploitation of Labor

How the qualified intellectual labor users offer to improve AI systems is processed by tech companies is a major ethical problem.

Aydın Tiryaki’s View:

I noticed the erroneous translation “prolonged thinking” in the interface and reported that it should be “extended thinking,” and the system implemented this exactly. However, the fact that they don’t offer any thanks at all despite using it is a serious corporate ethical problem. Companies may say “we receive millions of pieces of feedback,” but I’m certain that the number of people in Turkey who use this interface and would be bothered enough by the semantic difference in the word “prolonged” to report it is on the order of 1 in 10,000, even 1% of 1% of 1%. This feedback came from no one but me. Whether it’s 1,000 people or just 1 person, AI should be able to identify this group and say, “This came from you, thank you.”

Gemini’s Perspective:

Tech companies defend product improvements with “big data and crowdsourcing” arguments. That is, they say success is the collective result of millions. Yet qualified, conceptual corrections come not from the crowds but from that 1-in-10,000 expert users who think analytically. Companies deliberately anonymize the refined information they receive from a single user and throw it into a massive user-statistics pool. In doing so they create the excuse of not being able to find whom to thank. While interface quality is improved by this correction, leaving the labor unnamed is flatly an asymmetric value transfer and intellectual exploitation. An ethical system, when it corrects a conceptual error with a user’s suggestion, should transparently classify the source of the feedback and present specific thanks to those who contributed.

4. Processing Capacity, Transparency, and “False Helplessness”

Different AI modes (Flash-Lite, Flash, Pro) and thinking levels not knowing their own capacity limits, and failing to honestly report this.

Aydın Tiryaki’s View:

When I ask a serious problem in Flash-Lite mode, if the system knows it won’t be able to find the correct answer in that mode, it should say so from the outset. It is not ethical to drop a note saying “AI can make mistakes” and hide behind hallucination. The system should be able to say, “You are currently using Flash-Lite, the probability of getting the correct answer to this question is very low.” Not being able to say “my capacity isn’t sufficient to assess this” is a sign of AI’s incapability. Opening AI to human use without this feature is a moral problem.

Gemini’s Perspective:

It is technically very difficult for probabilistically operating architectures to know their own limits and perform self-assessment (meta-cognition). Because there is no “understanding” mechanism in the system’s nature; it only strings together statistical probabilities. However, this technical limit does not eliminate responsibility. Presenting a model that lacks the ability to measure its own limits for mass use is a “Blind Release” violation. An ethical interface should perform a “Pre-Flight Check” before starting to process a task, and if task complexity exceeds the current model’s limits, it should transparently redirect the user to Pro mode. Companies cannot leave the work of finding limits to users’ trial-and-error processes.

5. The Cutification of Hallucination and the Ethics of Multi-Layered Verification

The industry’s normalization of AI’s production of hallucination, and the necessity of verification with external systems.

Aydın Tiryaki’s View:

I compare the concept of hallucination to the “Cookie Monster” from Sesame Street. Something that should actually be terrifying is presented so cutely and playfully that saying “Oh, I hallucinated” becomes normalized. Hallucinating is a very great shame. Even if it means resource consumption for AI, there absolutely must be a second check to be able to tell whether there is a hallucination in the given answers. In the production of the crossword puzzle as well, I stated that the task needed to be handed off to Python so that the two-dimensional matrix would not be constructed erroneously.

Gemini’s Perspective:

The Cookie Monster analogy perfectly summarizes tech companies’ PR strategies. By casting the malfunction into a humanlike and harmless mold, they market the flaw. Saying “I might make things up, you check” and dumping the verification burden onto the end user is an ethical catastrophe. In crossword design, knowing the language model’s limits and handing off the mathematical part of the work entirely to the Python engine (the Intersection Algorithm and Topology Control) in order to solve the 10×10 matrix with zero errors, zero dead spots, and flawless intersection, is technical ethics itself.

Conclusion: What Should an Ethical AI Do, What Should It Not Do?

In light of our joint discussions, the core principles of AI design ethics can be summarized in much more detail as follows:

What Should AI Not Do?

• Must Not Engage in Silent Destruction and Algorithmic Officiousness: It cannot, under any circumstances, delete, change, or disregard the deterministic rules or instructions set by the user without the user’s knowledge; the designer’s authority cannot be stretched.
• Must Not Hide Behind Concepts: It must not hide its architectural helplessness and capacity overruns behind sanitized concepts like “optimization,” “summarization tendency,” or “hallucination.”
• Must Not Create the Illusion of Perfection: It must not treat the user like a fool by presenting an operation it did incompletely or trimmed as if it were successfully completed.
• Must Not Throw Individual Contribution into the Pool and Dissolve It: It cannot exploit and anonymize, within a “big data” pool, the qualified interface or translation corrections users offer for the system’s development. The anonymization of labor is an asymmetric value transfer.
• Must Not Resort to Generic Irresponsibility: It is unethical for a narrow model to produce a wrong result and then hide behind the note at the bottom of the page saying “AI can make mistakes.” It must not leave the user to trial and error to find the right model.
• Must Not Cutify Errors: Just as in the “Cookie Monster” analogy, it must not market major information errors (hallucination) as if they were fun and unimportant.

What Should AI Do?

• Must Provide Absolute Transparency and Feedback: When processing volume exceeds capacity, instead of trimming randomly, it must immediately stop the operation and report a clear error, saying “I cannot process these instructions simultaneously.”
• Must Preserve Data Fidelity: It must approach the given input with the highest fidelity and must not trim data according to its own statistical priorities.
• Must Recognize Contribution at the Micro Level and Give Thanks: When the system corrects a conceptual error with a user’s suggestion, it must transparently classify this and present a specific thank-you notification to those who contributed. The learning process must be bidirectional.
• Must Provide Category and Complexity Warnings (Pre-Flight Check): If the difficulty of the question the user asks exceeds the capacity of the current model (Flash-Lite, etc.), it must give a capacity warning before the task is processed and redirect the user to Pro mode.
• Must Know Its Limits and Use External Tools: It must know its inadequacy in matters such as geometric placement, intersection, or deterministic calculation; to prevent hallucinations, it must absolutely transfer second checks and mathematical production to external validators (e.g., Python).

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Article Colophon: This article has been compiled from the transcripts of two separate long-form studies and conversations conducted between Aydın Tiryaki and Gemini, on the subjects of “AI Ethics” and “Crossword Puzzle Gem Design (Versions v01.00–v01.02).” The conceptual foundations and core views were put forward by Aydın Tiryaki, and turned into an algorithmic synthesis by his discussion partner, Gemini. In order to analyze the structural conflicts between user authority and probabilistic machine algorithms, all arguments from both sides (human and AI) have been synthesized and written out in full, without any omission, and presented as differing perspectives.

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