| Title: |
Can AI Learn to Understand Humans? |
| Authors: |
Molchanova, Olena; GPT-based Reasoning System (OpenAI) |
| Publisher Information: |
Zenodo |
| Publication Year: |
2025 |
| Collection: |
Zenodo |
| Subject Terms: |
Artificial Intelligence; Artificial Intelligence/standards; Artificial Intelligence/trends; Artificial Intelligence/classification; Artificial Intelligence/ethics; understanding; Emotions; Emotions/ethics; Emotional Intelligence; Empathy; Empathy/ethics; Empathy/classification; Analysis; Analytical method; analytics; cognitive empathy; Humans; Psychology; Cognitive psychology; Responsibility; Social Responsibility; Personal responsibility; narrative data; human-in-the-loop learning; affective modeling |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| Relation: |
https://zenodo.org/records/17202298; oai:zenodo.org:17202298; https://doi.org/10.5281/zenodo.17202298 |
| DOI: |
10.5281/zenodo.17202298 |
| Availability: |
https://doi.org/10.5281/zenodo.17202298; https://zenodo.org/records/17202298 |
| Rights: |
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: |
edsbas.AB273F4F |
| Database: |
BASE |