Synthetic ImprintingReference Project
Human-AI Trust

Automation Bias

Automation bias is a tendency to rely on automated advice in ways that overlook relevant information or introduce errors. It can include following an incorrect recommendation or failing to act when a system provides no prompt. Using automation is not itself biased; the concern is reliance that does not adequately account for the system's limitations.

Term statusEstablished Concept

A concept used in academic or professional literature. This label does not mean every claim about it is settled. About these labels.

Evidence for the claims on this page: Research synthesis

The review connects automation-related errors with attention and task conditions. The concept does not imply that using automation is inherently mistaken.

How to read this assessment

By SyntheticImprinting.com Editorial ProjectUpdated Revision history

On this page

What it is

Automation bias is a pattern of decision errors associated with reliance on automated aids. It can include commission errors, when incorrect advice is followed, and omission errors, when a needed action is missed. (Parasuraman and Manzey, 2010)

In everyday language

An automated recommendation can receive more weight than it deserves. Someone may follow an incorrect suggestion or miss a problem because the system did not flag it.

What it isn't

Using automation is not itself a bias. Reliance can be appropriate when it reflects a system's demonstrated capabilities and the consequences of error.

An example

Hypothetical example: an editor publishes a generated summary without checking the original report, overlooking a contradiction visible in the source.

Key features

  • Concerns decisions made with automated assistance
  • Can involve following bad advice or missing unflagged problems
  • Depends on the person, task, and system

How it relates to synthetic imprinting

This concept helps examine why someone might accept synthetic output. It concerns decision behavior; a claim about lasting changes in belief or memory requires additional measurement.

Research context

Parasuraman and Manzey review automation-related errors and the role of attention. Their account extends beyond AI and cautions against treating expertise or simple warnings as complete protection. (Parasuraman and Manzey, 2010)

Established Concept

Trust Calibration

Trust calibration is the alignment between someone's trust in a system and what that system can reliably do in a particular context.

Established Concept

Algorithm Appreciation

Algorithm appreciation is a tendency to give greater weight to advice believed to come from an algorithm than to comparable human advice.

Emerging Concept

AI Hallucination

An AI hallucination is generated output that presents false or unsupported information as if it were accurate.

Sources and what they support

  1. Complacency and Bias in Human Use of Automation: An Attentional Integration — Raja Parasuraman, Dietrich H. Manzey (2010). Human Factors: The Journal of the Human Factors and Ergonomics Society.

    Peer-reviewed research. Reviews automation-related decision errors and how attention, task conditions, and system characteristics contribute.

Cite this definition

SyntheticImprinting.com Editorial Project. (2026-09-27). Automation Bias. Version 1.4. SyntheticImprinting.com. https://syntheticimprinting.com/terms/automation-bias/

The version identifies the page revision. This address opens the current page; it is not an immutable archive. You can also select and copy the reference above.

APA: SyntheticImprinting.com. (2026). Automation Bias. https://syntheticimprinting.com/terms/automation-bias/

MLA: “Automation Bias.” SyntheticImprinting.com, 2026, https://syntheticimprinting.com/terms/automation-bias/. Accessed November 12, 2026.

Chicago: SyntheticImprinting.com. “Automation Bias.” Last modified 2026-09-27. https://syntheticimprinting.com/terms/automation-bias/.

Revision history

These are retained versions of the opening definition. Revision dates describe editorial work, not independent scientific validation.

  1. Version 1.4 · Current

    Added claim-level citations and a separate assessment of evidence support. The opening definition is unchanged.

    Read this version’s definition
    Automation bias is a tendency to rely on automated advice in ways that overlook relevant information or introduce errors. It can include following an incorrect recommendation or failing to act when a system provides no prompt. Using automation is not itself biased; the concern is reliance that does not adequately account for the system's limitations.
  2. Version 1.3

    Expanded the opening definition; added explicit boundaries, source context, related-term cards, and linked editorial attribution.

    Read the previous definition
    Automation bias is a tendency to rely on automated advice in ways that overlook relevant information or introduce errors. It can include following an incorrect recommendation or failing to act when a system provides no prompt. Using automation is not itself biased; the concern is reliance that does not adequately account for the system's limitations.
  3. Version 1.0

    Definition recorded in the previous release. Earlier edits are not reconstructed here.

    Read the previous definition
    Automation bias is a tendency to rely on automated advice in ways that overlook relevant information or introduce errors.

What the status means

Established Concept

A concept used in academic or professional literature. This label does not mean every claim about it is settled.

How evidence labels work
First published
Updated
Version
1.4
Review status
External expert review pending

Help improve this entry

Suggest a correction to Automation Bias. Your email app opens with the page address and version included. Add the passage, your suggested change, and a source if you have one.

You can also email contact@syntheticimprinting.com directly.