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In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions

Aria Pessianzadeh, Naima Sultana, Hildegarde Van den Bulck, David Gefen, Shahin Jabbari, Rezvaneh Rezapour · Oct 17, 2025 · Citations: 0

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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

The rise of generative AI (GenAI) has impacted many aspects of human life. As these systems become embedded in everyday practices, understanding public trust in them is also essential for responsible adoption and governance. Prior work on trust in AI has largely drawn from psychology and human-computer interaction, but there is a lack of computational, large-scale, and longitudinal approaches to measuring trust and distrust in GenAI and large language models (LLMs). This paper presents the first computational study of trust and distrust in GenAI, using a multi-year Reddit dataset (2022--2025) spanning 39 subreddits and 230,576 posts. Crowd-sourced annotations of a representative sample were combined with classification models to scale analysis. We find that trust and distrust are nearly balanced over time, although trust modestly outweighs distrust, with shifts around major model releases. Technical performance and usability dominate as dimensions, while personal experience is the most frequent reason shaping attitudes. Distinct patterns also emerge across trustors (e.g., experts, ethicists, and general users). Our results provide a methodological framework for large-scale trust analysis and insights into evolving public perceptions of GenAI.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
  • The abstract does not clearly describe the evaluation setup.
  • The abstract does not clearly name benchmarks or metrics.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"The rise of generative AI (GenAI) has impacted many aspects of human life."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"The rise of generative AI (GenAI) has impacted many aspects of human life."

Quality Controls

missing

Not reported

No explicit QC controls found.

"The rise of generative AI (GenAI) has impacted many aspects of human life."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"The rise of generative AI (GenAI) has impacted many aspects of human life."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"The rise of generative AI (GenAI) has impacted many aspects of human life."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Distinct patterns also emerge across trustors (e.g., experts, ethicists, and general users)."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Domain Experts
  • Expertise required: General

Evaluation Details

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

The rise of generative AI (GenAI) has impacted many aspects of human life.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • The rise of generative AI (GenAI) has impacted many aspects of human life.
  • As these systems become embedded in everyday practices, understanding public trust in them is also essential for responsible adoption and governance.
  • Prior work on trust in AI has largely drawn from psychology and human-computer interaction, but there is a lack of computational, large-scale, and longitudinal approaches to measuring trust and distrust in GenAI and large language models (LLMs).

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • The rise of generative AI (GenAI) has impacted many aspects of human life.
  • Prior work on trust in AI has largely drawn from psychology and human-computer interaction, but there is a lack of computational, large-scale, and longitudinal approaches to measuring trust and distrust in GenAI and large language models…

Why It Matters For Eval

  • The rise of generative AI (GenAI) has impacted many aspects of human life.
  • Prior work on trust in AI has largely drawn from psychology and human-computer interaction, but there is a lack of computational, large-scale, and longitudinal approaches to measuring trust and distrust in GenAI and large language models…

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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