A field guide to AI positions

Automation bias

Human decision pattern.

Sources are over 18 months old.

Publication dates and source age

Sources counted: 3

Newest dated source: 2024-11-01

Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

Open in the glossary Reading notes · Structured record

Prepared with AI assistance. Not independently fact-checked. Original sources and qualifications are below.

In plain language

Following automated advice too readily, including wrong advice; useful assistance can still create this problem. [1]

Reference this explanation or suggest a correction

Explanation based on the linked sources. Claim ID: claim-term-automation-bias-1a5192ba3d7825ca26b24a72. Edition 0.20.0.

Link to this explanation · Suggest a correction · How corrections work

Limits & distinctions

This is a possible failure of reliance, not proof that people always trust machines or that all AI assistance reduces accuracy. Effects depend on the task and interaction. [1] [3]

Reference this explanation or suggest a correction

Explanation based on the linked sources. Claim ID: claim-term-automation-bias-09422ce4d5c74cb753a9bb98. Edition 0.20.0.

Link to this explanation · Suggest a correction · How corrections work

A fuller explanation

A person may accept a system's suggestion instead of checking it against other evidence. One study of pathology experts found better overall performance alongside some cases where wrong advice displaced a correct judgment. [1]

Reference this explanation or suggest a correction

Explanation based on the linked sources. Claim ID: claim-term-automation-bias-1e4c26398ee834b2e16dc7b8. Edition 0.20.0.

Link to this explanation · Suggest a correction · How corrections work

How it relates to the map

Atlas reading question: how does a workflow help people detect and reject a wrong recommendation? [2]

Reference this explanation or suggest a correction

Editorial interpretation or qualification. Claim ID: claim-term-automation-bias-25f52a21ad9f2e54a62ed1b2. Edition 0.20.0.

Link to this explanation · Suggest a correction · How corrections work

Share this page

https://theaiatlas.org/ideas/automation-bias/

Download a share image · Vector image

Image previews are summaries. Keep the page link so readers can check the evidence.

Sources and what we read

  1. 1. Automation Bias in AI-Assisted Medical Decision-Making under Time Pressure in Computational Pathology

    Emely Rosbach and coauthors / arXiv · First-hand source · Published 2024-11-01 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2024-11-01

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read the abstract and submission record. The study involved 28 pathology experts and reported improved overall performance alongside acceptance of some wrong advice. Abstract-only review; its error rate is not generalized to other users or tasks.

    Source ID: glossary-eval-automation-bias

  2. 2. The impact of AI errors in a human-in-the-loop process

    Ujué Agudo and coauthors / Cognitive Research: Principles and Implications · First-hand source · Published 2024-01-07 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2024-01-07

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read the abstract, study procedures, results and general discussion. Two simulated judicial-decision experiments used purported AI advice; these are not a field trial of an LLM or a universal estimate of human oversight effectiveness.

    Source ID: glossary-eval-human-errors

  3. 3. App. C: AI Risk Management and Human-AI Interaction

    NIST AI Resource Center · First-hand source · Published 2023 · Material last read 2026-09-15

    Source is over 18 months old.

    Publication dates and source age

    Sources counted: 1

    Newest dated source: 2023

    Assessed at this edition's evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

    Publication age does not tell us whether a claim is still valid. Reading an old source again does not make its publication date newer. An update date does not establish that the passage we used was updated.

    Read scope is described in the source note.

    No archive check recorded.

    Read the AI RMF 1.0 appendix on human roles, oversight, bias and differing outcomes of human-AI interaction. It describes both possible complementarity and amplified bias; it is guidance rather than a controlled experiment.

    Source ID: glossary-eval-human-interaction

Edition and machine-readable evidence

Content version 0.20.0. Evidence cutoff 2026-09-15; this does not mean every source was read on that day.

Pinned complete dataset · Complete evidence page · Agent consumption guide