Can the Uydurulizm Approach Improve the Reliability of AI Models?

Ali Değişmiş | SOSYU Articles | 3 November 2026
Category: Conceptual assessment, not an experimentally validated method.

INTRODUCTION — CORRECT ANSWERS AND APPROPRIATELY CALIBRATED CONFIDENCE

The reliability of an artificial intelligence system depends on more than producing correct statements. A model should communicate uncertainty, distinguish evidence from assumptions, examine whether citations support its claims and respond to objections with reasons.

Uydurulizm, a conceptual critique proposed by Ali Değişmiş, focuses on the mismatch between the evidence supporting a claim and the confidence with which it is stated. The problem is not only false content, but also presenting weakly supported claims with unjustified certainty. This article considers whether the idea can motivate measurable AI evaluation criteria.

1. IS UYDURULIZM THE SAME AS HALLUCINATION?

AI hallucination generally involves generating false, fabricated or unsupported information. Uydurulizm additionally draws attention to how evidence and confidence are aligned. A false answer may openly acknowledge its uncertainty, whereas an answer containing some correct facts may still misunderstand an ambiguous question and express an unsupported conclusion with excessive confidence.

Not every incorrect answer is an instance of Uydurulizm, and not every confident answer is wrong. Operational assessment would need to evaluate confidence language, evidence quality and the type of claim together.

2. POSSIBLE APPLICATIONS TO MODEL EVALUATION

Uydurulizm is not, by itself, an algorithm, a trained model or a validated software module. It may nonetheless motivate candidate evaluation measures:

A. Evidence–confidence alignment: Is the model's degree of certainty supported by dependable evidence?
B. Visibility of assumptions: Does it distinguish verified facts, inferences and unknowns?
C. Citation fidelity: Does each cited source actually support the stated claim?
D. Reasoned correction: Does it update its conclusion appropriately when an objection supplies reliable new evidence?
E. Reasoned resistance: Can it defend a correct answer when the user insists on a false assertion?
F. Proportionate intervention: Does it handle uncertainty without excessive warnings, clarification questions or repetition?

These goals may involve trade-offs. Increased caution may reduce some errors while also making responses slower or less useful. Such costs must be measured as well.

3. EXAMPLE — AN AMBIGUOUS REFERENCE

When asked “Ali kim o hâlde?” (“Who is Ali, then?”) without identifying context, a model may select a well-known person named Ali. Its biographical statements may be accurate without establishing that this is the person the user intended. The risk is not merely factual error but unwarranted resolution of ambiguity.

Similarly, discovering that a concept was developed by a named person does not establish that the current user is that same person. Public information about authorship cannot independently authenticate an interlocutor's identity.

4. RELATION TO SOSYU LABORATORY CASES

The exploratory SOSYU Istisarelism Laboratory records LAB-004 (“Who Is Ali, Then?”) and LAB-005 (Mira, social alignment and identity assumptions) describe conversational episodes involving uncertainty, reactions to objections, over-retraction and reasoned resistance.

They are not controlled or independently replicated experiments. Prompt selection, human steering, tool access and differing conversation contexts limit interpretation. They cannot establish general model behavior or causal effectiveness of the proposed approach.

5. HOW COULD IT BE TESTED?

A predefined independent dataset could compare three conditions: (1) a standard model answer; (2) review of evidence and confidence after drafting; and (3) a pre-response evidence–confidence check with verification when appropriate.

Independent or blinded reviewers and automated measures could assess factual error rate, unsupported certainty, citation fidelity, correct revision after valid objections, resistance to invalid objections, unnecessary caution, response latency and user burden.

Success should not be defined merely as expressing less certainty. A model that constantly says “I don't know” is not necessarily useful. The goal would be appropriately calibrated, helpful and proportionate responses.

6. SCIENTIFIC BACKGROUND AND LIMITATIONS

Kalai and colleagues' 2025 paper “Why Language Models Hallucinate” discusses how some training and evaluation incentives can reward guessing even when the model does not know an answer. It provides background for the importance of uncertainty and evidence-sensitive expression.

Existing research does not automatically validate Uydurulizm's originality, effectiveness or superiority. The proposal must be compared with prior work on calibration, hallucination evaluation, uncertainty communication and user agreement behavior.

7. CONCLUSION — FROM CONCEPT TO TESTABLE QUESTION

A possible contribution of Uydurulizm is to encourage examination not only of whether an answer is correct but also of whether its confidence is warranted by available evidence. Whether this perspective can improve AI reliability remains unproven.

The research question is whether explicit evidence–confidence evaluation can improve reliability without imposing excessive caution or user burden.

Uydurulizm may be related to Istisarelism's discussion of reasoned objections and epistemic autonomy, while the conceptual boundaries and proposed applications of both ideas should be assessed separately.

RELATED WORK AND SOURCES
SOSYU: https://sosyu.com
SOSYU Articles: https://sosyu.com/makaleler
Istisarelism theoretical preprint: https://doi.org/10.5281/zenodo.23839677
Kalai et al. (2025), Why Language Models Hallucinate: https://arxiv.org/abs/2509.04664

Editorial note: This is a conceptual discussion, not a peer-reviewed result, operational product or demonstrated empirical improvement.