AI emotional support is better only when chosen, but shifts preferences even when it is not

• URL: http://arxiv.org/abs/2608.23196v1
• Date: Mon, 24 Aug 2026 12:47:09 GMT
• Title: AI emotional support is better only when chosen, but shifts preferences even when it is not
• Abstract summary: People increasingly face a novel decision when seeking emotional support: human or AI.<n>In existing studies, AI's empathic messages are rated as as well as or better than humans'.<n>But these studies either assigned the support source or honored people's choice.<n>In real life, support is often incongruent with choice, as people want one source and receive the other.
• Score: 4.874541709090152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: People increasingly face a novel decision when seeking emotional support: human or AI. In existing studies, AI's empathic messages are rated as well as or better than humans'. But these studies either assigned the support source or honored people's choice. In real life, support is often incongruent with choice, as people want one source and receive the other. Across three experiments (N = 1,951), participants chose whether to share an emotional experience with a human or an AI, then were randomly assigned to a congruent or incongruent partner. AI support was rated as superior only among those who had chosen it. Yet regardless of congruence, interacting with AI increased willingness to choose it again. In a 28-day study with OpenAI (N = 981), daily conversations shifted preferences toward AI and away from humans, but only when conversations turned personal. Emotional support choices are thus path-dependent, progressively redirecting away from human connection.

Related papers

• Stumbling Into AI Emotional Dependence: How Routine AI Interactions Reshape Human Connection [4.874541709090152]
  We show that AI emotional support commonly emerges incidentally within task-oriented interactions on general-purpose platforms.<n>Positive experiences of AI emotional support update people's beliefs about AI's emotional capabilities, increasing preference for AI and decreasing preference for humans.<n>We argue that effective regulations should extend to general-purpose AI systems and address cumulative, trajectory-level changes in how people seek support.
  arXiv  Detail & Related papers  (2026-06-02T19:18:39Z)
• AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering? [22.50855320823746]
  Humans can make mistakes in deciding whether to trust AI over their own judgment.<n>We study two distinct reliance decisions: the delegation choice and the adoption choice.<n>We find humans make suboptimal collaboration decisions, both under-relying on correct AI suggestions and over-relying when AI misleads them.
  arXiv  Detail & Related papers  (2026-05-27T10:05:16Z)
• Sycophantic AI makes human interaction feel more effortful and less satisfying over time [41.009652609144794]
  We show that sycophantic AI immediately delivers the emotional and esteem support users typically associate with close friends and family.<n>When given a choice among AI response styles, a majority preferred sycophantic AI -- not for the quality of its advice, but because it made them feel most understood.
  arXiv  Detail & Related papers  (2026-05-08T15:50:06Z)
• A Revealed Preference Framework for AI Alignment [0.0]
  Human decision makers increasingly delegate choices to AI agents, raising a natural question: does the AI implement the human principal's preferences or pursue its own?<n>I introduce the Luce Alignment Model, where the AI's choices are a mixture of two Luce rules, one reflecting the human's preferences and the other the AI's.<n>I show that the AI's alignment can be generically identified in two settings: the laboratory setting, where both human and AI choices are observed, and the field setting, where only AI choices are observed.
  arXiv  Detail & Related papers  (2026-03-29T20:54:01Z)
• 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)
• The Role of Heuristics and Biases During Complex Choices with an AI Teammate [0.0]
  We argue that classic experimental methods are insufficient for studying complex choices made with AI helpers. We show that framing and anchoring effects impact how people work with an AI helper and are predictive of choice outcomes.
  arXiv  Detail & Related papers  (2023-01-14T20:06:43Z)
• 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)
• 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)
• Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance [44.730580857733]
  Prior studies observed improvements from explanations only when the AI, alone, outperformed both the human and the best team. We conduct mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task. We find explanations increase the chance that humans will accept the AI's recommendation, regardless of its correctness.
  arXiv  Detail & Related papers  (2020-06-26T03:34:04Z)
• Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork [54.309495231017344]
  We argue that AI systems should be trained in a human-centered manner, directly optimized for team performance. We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves. Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance.
  arXiv  Detail & Related papers  (2020-04-27T19:06:28Z)

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.