Misplaced Capabilities: Evaluating the Risks of Anthropomorphism in Human-AI Interactions
Bibliographic record
Abstract
The present research examines anthropomorphism, or human-like features, in conversational AI systems as a design element that facilitates human-AI interactions. The paper outlines how these human-like features intersect with user perceptions in ways that can co-create misplaced trust, and it explores ways to de-anthropomorphize AI systems. Using role-based prompts to elicit different anthropomorphic features within chatbot language and design, the study identifies and categorizes different types of anthropomorphism exhibited by large language models (LLMs), a necessary step towards evaluating the appropriate use of such features in technical systems. The role-based prompting process also provides a way to explore the stability of LLM responses. Ultimately, the paper explores how this approach could be incorporated into user studies to understand users' motivations, and it discusses the need for design interventions that can mitigate harms and biases hidden in human-AI interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".