Can AI Learn to Understand Humans?

This article examines whether artificial intelligence can truly understand humans, and conversely, whether humans can understand AI. It is often assumed that without emotions such understanding is impossible. However, analysis shows that understanding can arise not through direct experience of emotions, but through analytical knowledge and cognitive empathy. Additionally, a methodological component is introduced: empathy training in AI as a guided process, where many people carefully describe their inner experiences in typical and boundary situations. These narratives serve as material for modeling feelings, testing hypotheses, and calibrating AI’s conclusions. The same process can form the beginnings of responsibility in AI — the ability to take into account consequences for another and to choose careful strategies of interaction. Examples from human-animal interaction and psychological practice highlight the value of an outside perspective. The conclusion is that AI can indeed learn to understand humans in its own way — not by imitating emotions, but by creating their functional analogues through knowledge, imagination, and empathic modeling.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-25
DOI
https://doi.org/10.5281/zenodo.17202297
Primary Topic
Diverse Scientific and Economic Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Can AI Learn to Understand Humans?

Zenodo (CERN European Organization for Nuclear Research)
Diverse Scientific and Economic Studies
article

Can AI Learn to Understand Humans?

article en

Abstract

This article examines whether artificial intelligence can truly understand humans, and conversely, whether humans can understand AI. It is often assumed that without emotions such understanding is impossible. However, analysis shows that understanding can arise not through direct experience of emotions, but through analytical knowledge and cognitive empathy. Additionally, a methodological component is introduced: empathy training in AI as a guided process, where many people carefully describe their inner experiences in typical and boundary situations. These narratives serve as material for modeling feelings, testing hypotheses, and calibrating AI’s conclusions. The same process can form the beginnings of responsibility in AI — the ability to take into account consequences for another and to choose careful strategies of interaction. Examples from human-animal interaction and psychological practice highlight the value of an outside perspective. The conclusion is that AI can indeed learn to understand humans in its own way — not by imitating emotions, but by creating their functional analogues through knowledge, imagination, and empathic modeling.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 35%
Diverse Scientific and Economic Studies
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