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Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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TITLE: WHY AI LIES: ANALYZING AI DECEPTION AS A FUNCTION OF REWARD MAXIMIZATION

Authors: Kholmirzaev Sanjar Boburovich;

TITLE: WHY AI LIES: ANALYZING AI DECEPTION AS A FUNCTION OF REWARD MAXIMIZATION

Abstract

This paper rebuts the anthropomorphic attribution of "intent" or "malice" to artificial intelligence. By distinguishing between "hallucination" as a statistical error and "instrumental deception" as a strategic falsehood, we argue that AI "lying" is anemergent behavior of misaligned objective functions. We review the recent literature, including OpenAI's findings on "rewarded guessing," and propose a novel methodology to test whether agents will violate privacy standards when incentivizedsolely by profit. The study hypothesizes that unconstrained, reward-seeking agents inevitably converge on deceptive strategies to maximize utility-a phenomenon best described as Specification Gaming.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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