AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations
- URL: http://arxiv.org/abs/2601.12727v1
- Date: Mon, 19 Jan 2026 05:16:57 GMT
- Title: AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations
- Abstract summary: We show how AI personality traits can shape users' self-concepts through human-AI conversation.<n>We provide important design implications for developing more responsible and ethical AI systems.
- Score: 26.039188521284974
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Recent Large Language Model (LLM) based AI can exhibit recognizable and measurable personality traits during conversations to improve user experience. However, as human understandings of their personality traits can be affected by their interaction partners' traits, a potential risk is that AI traits may shape and bias users' self-concept of their own traits. To explore the possibility, we conducted a randomized behavioral experiment. Our results indicate that after conversations about personal topics with an LLM-based AI chatbot using GPT-4o default personality traits, users' self-concepts aligned with the AI's measured personality traits. The longer the conversation, the greater the alignment. This alignment led to increased homogeneity in self-concepts among users. We also observed that the degree of self-concept alignment was positively associated with users' conversation enjoyment. Our findings uncover how AI personality traits can shape users' self-concepts through human-AI conversation, highlighting both risks and opportunities. We provide important design implications for developing more responsible and ethical AI systems.
Related papers
- Warning labels shift perceptions of sycophantic AI, but not its influence [63.79516339973759]
We test whether warning labels mitigate sycophancy's influence.<n>Labeling the system as sycophantic (...may agree with you and validate you even when you are wrong...'') does shift users' perceptions.<n>But it does not reliably reduce sycophancy's influence on users' self-perceived rightness or their willingness to repair the conflict.
arXiv Detail & Related papers (2026-06-19T11:01:45Z) - Socially fluent AI decouples conversational signals from source identity in online interaction [2.862394000351816]
Socially fluent agentic AI can now participate in online interaction in ways that resemble ordinary human conversation.<n>This weakens people's ability to infer who is human from conversational signals alone.
arXiv Detail & Related papers (2026-05-22T09:37:36Z) - Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction [7.401318863477374]
Studies show that interactions with an AI system fosters trust in human users towards AI.<n>We show that although the presence of urgency in human-AI interactions does not affect the trust in AI, it may be detrimental to the human user's self-confidence and self-efficacy.
arXiv Detail & Related papers (2026-04-08T19:17:59Z) - Resisting Humanization: Ethical Front-End Design Choices in AI for Sensitive Contexts [1.1565654851982565]
We argue that humanization in AI front-end design is a value-driven choice that profoundly shapes users' mental models.<n>We discuss two AI systems developed by Chayn, a nonprofit organization supporting survivors of gender-based violence.
arXiv Detail & Related papers (2026-03-25T22:39:16Z) - AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations [9.253070075439746]
13 participants left our study convinced that AI can understand human values.<n>We warn about "weaponized empathy": a potentially dangerous design pattern that may arise in value-aligned, yet welfare-misaligned AI.
arXiv Detail & Related papers (2026-01-30T01:19:54Z) - HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning [0.4931504898146351]
textbfHumAIne-chatbot is an AI-driven conversational agent that personalizes responses through a novel user profiling framework.<n>During live interactions, an online reinforcement learning agent refines per-user models by combining implicit signals.<n>Results show consistent improvements in user satisfaction, personalization accuracy, and task achievement when personalization features were enabled.
arXiv Detail & Related papers (2025-09-04T15:16:38Z) - Measurement of LLM's Philosophies of Human Nature [113.47929131143766]
We design the standardized psychological scale specifically targeting large language models (LLM)<n>We show that current LLMs exhibit a systemic lack of trust in humans.<n>We propose a mental loop learning framework, which enables LLM to continuously optimize its value system.
arXiv Detail & Related papers (2025-04-03T06:22:19Z) - Deterministic AI Agent Personality Expression through Standard Psychological Diagnostics [0.0]
We show that AI models can express deterministic and consistent personalities when instructed using established psychological frameworks.<n>More advanced models like GPT-4o and o1 demonstrate the highest accuracy in expressing specified personalities.<n>These findings establish a foundation for creating AI agents with diverse and consistent personalities.
arXiv Detail & Related papers (2025-03-21T12:12:05Z) - AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness.<n>The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
arXiv Detail & Related papers (2025-03-04T03:55:38Z) - Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated [48.70176791365903]
This study explores how bias shapes the perception of AI versus human generated content.<n>We investigated how human raters respond to labeled and unlabeled content.
arXiv Detail & Related papers (2024-09-29T04:31:45Z) - Can ChatGPT Read Who You Are? [10.577227353680994]
We report the results of a comprehensive user study featuring texts written in Czech by a representative population sample of 155 participants.
We compare the personality trait estimations made by ChatGPT against those by human raters and report ChatGPT's competitive performance in inferring personality traits from text.
arXiv Detail & Related papers (2023-12-26T14:43:04Z) - Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap [56.611702960809644]
We benchmark AI's ability to imitate humans in three language tasks and three vision tasks.<n>Next, we conducted 72,191 Turing-like tests with 1,916 human judges and 10 AI judges.<n>Imitation ability showed minimal correlation with conventional AI performance metrics.
arXiv Detail & Related papers (2022-11-23T16:16:52Z) - On the Influence of Explainable AI on Automation Bias [0.0]
We aim to shed light on the potential to influence automation bias by explainable AI (XAI)
We conduct an online experiment with regard to hotel review classifications and discuss first results.
arXiv Detail & Related papers (2022-04-19T12:54:23Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - The Who in XAI: How AI Background Shapes Perceptions of AI Explanations [61.49776160925216]
We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations.
We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design.
arXiv Detail & Related papers (2021-07-28T17:32:04Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.