---
title: "Outsourcing Risk Recognition – The Hidden Danger of Depending on AI/SI for Online Safety – 2026"
canonical: https://scamsnow.com/outsourcing-risk-recognition-danger-online-safety-2026/
type: post
author: "SCARS Editorial Team"
published: 2026-10-26
modified: 2026-10-26
categories: ["FEATURED ARTICLE", "2026", "Cognitive Patterns & Thinking", "PSYCHOLOGY", "SCAM AVOIDANCE", "Tim McGuinness PhD", "VICTIMOLOGY"]
summary: ""
organization: "ScamsNOW.com - the Official Magazine of Scams Fraud & Cybercrime"
organization_url: https://scamsnow.com/
llms: https://scamsnow.com/llms.txt
---

# Outsourcing Risk Recognition – The Hidden Danger of Depending on AI/SI for Online Safety – 2026

Image: Situational Awareness and Safety banner.

# Outsourcing Risk Recognition

## The Hidden Danger of Depending on AI for Online Safety

## Why AI/SI Online Safety Tools May Backfire

### Primary Category:

##### Author:
•  [Tim McGuinness, Ph.D., DFin, MCPO, MAnth](https://scamsnow.com/?s=Tim+McGuinness) - Anthropologist, Scientist, Polymath, Managing Director of the [Society of Citizens Against Relationship Scams Inc.](http://www.AgainstScams.org)

##### Author Biographies Below

### About This Article

Artificial intelligence supports online safety by screening messages, assessing websites, and identifying suspicious activity, but reliance on these tools changes how users recognize danger. Assisted performance does not establish [independent competence](https://scamsnow.com/glossary-term/independent-competence/), and prior experience does not guarantee awareness of a developing situation. [Outsourcing risk recognition](https://scamsnow.com/glossary-term/outsourcing-risk-recognition/) transfers the decision about when to question an interaction to an automated system. Users then risk treating absent warnings as evidence of safety. Research on learning and automation distinguishes [active engagement](https://scamsnow.com/glossary-term/active-engagement/) from passive dependence and identifies errors involving missed alerts and incorrect recommendations. Effective protection requires clear [assessment boundaries](https://scamsnow.com/glossary-term/assessment-boundaries/), independent verification, practical training, and continued human judgment. Financial loss, [identity exposure](https://scamsnow.com/glossary-term/identity-exposure/), and [account compromise](https://scamsnow.com/glossary-term/account-compromise/) remain serious consequences when [automated detection](https://scamsnow.com/glossary-term/automated-detection/) and human scrutiny fail together.

***Note: **This article is intended for informational purposes and does not replace professional medical advice. If you are experiencing distress, please consult a qualified mental health professional.*

### Keywords

[outsourcing risk recognition](https://scamsnow.com/glossary-term/outsourcing-risk-recognition/), AI dependence, [automation bias](https://scamsnow.com/glossary-term/automation-bias/), [situation awareness](https://scamsnow.com/glossary-term/situation-awareness/), [independent judgment](https://scamsnow.com/glossary-term/independent-judgment/), scam prevention, digital literacy, [skill formation](https://scamsnow.com/glossary-term/skill-formation/), [human oversight](https://scamsnow.com/glossary-term/human-oversight/), online safety

Image: Risk Avoidance and Online Safety banner.

## Outsourcing Risk Recognition: The Hidden Danger of Depending on AI/SI for Online Safety

## Author’s Note

The central concern is the preservation of [independent judgment](https://scamsnow.com/glossary-term/independent-judgment/) as AI/SI becomes part of everyday protection. Recognizing uncertainty, asking questions, and understanding the limits of an assessment remain essential human responsibilities. Technology providers share responsibility for supporting those abilities through clear explanations, honest boundaries, and opportunities for learning. Protection deserves evaluation by both the threats it stops and the readiness people retain when it fails.

## Key Points

- AI-assisted success does not establish the knowledge or judgment needed to perform independently.
- [Active engagement](https://scamsnow.com/glossary-term/active-engagement/) with explanations and evidence supports learning during AI use.
- Prior experience provides useful knowledge but does not guarantee present [situation awareness](https://scamsnow.com/glossary-term/situation-awareness/).
- [Outsourcing risk recognition](https://scamsnow.com/glossary-term/outsourcing-risk-recognition/) means relying on a system to decide when an interaction deserves scrutiny.
- An absent warning does not verify identity, authority, purpose, or safety.
- Dependence develops through both missing [skill formation](https://scamsnow.com/glossary-term/skill-formation/) and declining use of established skills.
- A legitimate website or service does not establish the honesty of the surrounding interaction.
- Multiple reviewers provide meaningful oversight only when they assess evidence independently.
- Clear [assessment boundaries](https://scamsnow.com/glossary-term/assessment-boundaries/) and [safe simulations](https://scamsnow.com/glossary-term/safe-simulations/) help preserve readiness for unfamiliar situations.
- Shared failures of [automated detection](https://scamsnow.com/glossary-term/automated-detection/) and human judgment expose people to financial loss, [identity exposure](https://scamsnow.com/glossary-term/identity-exposure/), and [account compromise](https://scamsnow.com/glossary-term/account-compromise/).

## Introduction

Artificial intelligence is becoming part of the protective layer between people and online crime. Tools screen messages, assess websites, flag suspicious transactions, and help users evaluate unfamiliar requests. These functions serve an important purpose. People face more information than they have time to inspect, and automated assistance brings speed and consistency to tasks that otherwise demand sustained attention.

However, protection also changes behavior. When a system repeatedly identifies danger, its users develop expectations about what the system notices and what its silence means. The concern extends beyond following an incorrect recommendation. It reaches an earlier and more fundamental activity: recognizing that a situation deserves scrutiny at all. Outsourcing risk recognition itself means transferring the responsibility for noticing possible danger to a tool, without maintaining the understanding and habits needed to recognize its failures.

## Performance Is Different From Competence

A person who completes a task with AI has demonstrated performance within an assisted arrangement. That result does not establish independent knowledge, sound judgment, or the ability to handle an unfamiliar problem. Those qualities require separate assessment. A polished answer conceals the difference between understanding a process and successfully obtaining its output.

Research illustrates this distinction. In a randomized mathematics study, students using a general-purpose AI [interface](https://scamsnow.com/glossary-term/interface/) performed better during assisted practice but worse on a subsequent [unassisted examination](https://scamsnow.com/glossary-term/unassisted-examination/) than students without AI access. A tutor designed to support learning avoided that negative effect. The result identifies an important difference between assistance that supplies answers and assistance that preserves the learner’s participation in reaching them.

The same distinction matters in online safety. Someone who submits every questionable message to an AI assistant receives assessments without necessarily learning how to make those assessments. If the person reads only the final verdict, the encounter teaches dependence on the verdict. The user gets through the immediate task while leaving the underlying judgment largely unpracticed.

## AI Use Does Not Automatically Prevent Learning

The opposite claim also requires discipline. AI does not inevitably destroy learning or leave every novice ignorant. A preregistered writing experiment found that participants who practiced with AI improved more on an independent writing assessment the following day than participants who practiced without it. Assistance and learning therefore coexist under some conditions.

The important question concerns what the user does while receiving help. Comparing explanations, identifying assumptions, checking evidence, and attempting an assessment before reading the answer all involve [active engagement](https://scamsnow.com/glossary-term/active-engagement/). Simply accepting an outcome removes those activities from the person’s experience. The difference lies in whether the tool supports the development of judgment or repeatedly performs the judgment in the user’s place.

An [AI-assisted coding](https://scamsnow.com/glossary-term/ai-assisted-coding/) experiment reinforces this concern. Participants using AI scored lower on a subsequent skills assessment than those working without it, with the largest difference involving [debugging](https://scamsnow.com/glossary-term/debugging/). [Exploratory analysis](https://scamsnow.com/glossary-term/exploratory-analysis/) also distinguished more engaged uses, such as requesting explanations, from heavier [delegation](https://scamsnow.com/glossary-term/delegation/). These findings concern a particular learning task, but they draw attention to the very skills needed to recognize when an automated output is wrong.

## Prior Experience Does Not Guarantee Present Awareness

An experienced person brings knowledge that a novice lacks. Experience supplies context, expectations about normal processes, and an understanding of how errors develop. An experienced security professional, for example, has more resources for questioning an automated assessment than someone encountering the subject for the first time.

Nevertheless, knowledge of a field and awareness of a current situation are different. A person retains an understanding of fraud while losing track of the details of a particular exchange. If automation performs the monitoring, the user receives fewer reasons to inspect the developing situation independently. Existing expertise does not remove that gap.

Endsley and Kiris demonstrated this problem in research on automated navigation. Reduced [situation awareness](https://scamsnow.com/glossary-term/situation-awareness/) corresponded with slower decisions after the automated system failed, and the shift from active to [passive processing](https://scamsnow.com/glossary-term/passive-processing/) helped explain the reduction. Their findings show why assigning a human the role of [emergency backup](https://scamsnow.com/glossary-term/emergency-backup/) does not, by itself, ensure readiness to take over. The human needs an adequate understanding of the situation before the failure occurs.

## The Outsourcing of Risk Recognition Itself

[Risk recognition](https://scamsnow.com/glossary-term/risk-recognition/) begins before anyone identifies a particular scam. It starts with noticing an unexpected request, an unexplained change, a conflict between claims, or an action whose consequences deserve attention. Recognition creates the pause in which further evaluation takes place. Without that pause, a person proceeds before asking whether the interaction deserves trust.

Delegating this first step changes the protective process. The user stops asking whether a request needs examination and instead waits for the system to interrupt. A warning becomes the event that activates caution. Everything that passes without a warning receives less attention, even though silence establishes only that no warning reached the user.

This is the central danger. The person delegates the decision about when to think critically, not merely the collection of information used during critical thinking. Once that habit takes hold, telling the user to exercise judgment provides little protection unless something first triggers the decision to use it.

## When Silence Becomes Reassurance

Automation research identifies errors that closely resemble this problem. In a simulated task, users of an [automated decision aid](https://scamsnow.com/glossary-term/automated-decision-aid/) missed events that the system failed to flag. They also followed incorrect recommendations despite [contradictory information](https://scamsnow.com/glossary-term/contradictory-information/). Researchers distinguish these failures as errors of omission and commission: failing to act when an alert is absent, and acting on incorrect automated advice.

In an online setting, omission deserves particular attention. A tool does not need to display a false statement of safety to create [false reassurance](https://scamsnow.com/glossary-term/false-reassurance/). A quiet screen, an unchanged status icon, or the absence of an interruption supplies an apparent signal when users believe the system monitors everything relevant.

That belief exceeds what the silence establishes. The tool has a particular scope, receives particular inputs, and operates under particular conditions. The user’s interaction extends beyond those boundaries. A system that examines a link does not thereby verify the identity of the person sending it or the legitimacy of the request surrounding it.

## Missing Experience and Declining Practice

Dependence develops through two different routes. In the first, an inexperienced user never builds an adequate foundation because the tool repeatedly performs the relevant assessment. The person learns how to request a verdict without learning which observations support it. This is a failure of [skill formation](https://scamsnow.com/glossary-term/skill-formation/).

In the second route, a previously experienced user stops exercising parts of an established skill. Important details receive less attention, [verification routines](https://scamsnow.com/glossary-term/verification-routines/) weaken, and practice responding to uncertainty becomes less frequent. This is a problem of [skill maintenance](https://scamsnow.com/glossary-term/skill-maintenance/) and active awareness. The distinction matters because training a novice and restoring an experienced person’s engagement require different approaches.

Both routes become dangerous when the person’s assigned responsibility exceeds demonstrated readiness. A user expected to recognize a missed threat needs more than access to an [override button](https://scamsnow.com/glossary-term/override-button/). That user needs the knowledge to question the output, the information required to assess the situation, and permission to stop the process.

## New Threats Expose the Gap

A new scam does not automatically defeat AI. Some systems identify broader [behavioral patterns](https://scamsnow.com/glossary-term/behavioral-patterns/) or unusual combinations of events rather than relying exclusively on exact matches to previously recorded attacks. Equally, a person does not require direct experience of a particular crime to recognize uncertainty or demand verification.

The dangerous case arises when the threat falls outside the system’s reliable detection, and the user has stopped making an [independent assessment](https://scamsnow.com/glossary-term/independent-assessment/). Both [protective layers](https://scamsnow.com/glossary-term/protective-layers/) then fail together. The system misses the event, and the person treats the absence of a warning as a reason to continue.

Consider an illustrative scenario in which a criminal directs someone to a genuine online service for an illegitimate purpose. A tool assesses the website and finds no evidence that the site itself is fraudulent. The user interprets that narrow finding as approval of the entire interaction. The failure involves a mismatch between the question answered and the reassurance taken from it.

## A Criminal Exploits the Interaction, Not Just the Technology

A scam unfolds through explanations, relationships, requests, timing, and consequences. Examining one component leaves other components unresolved. A legitimate communication platform still carries deceptive conversations, and an authentic payment service still processes transactions initiated through manipulation. The safety of the infrastructure does not establish the honesty of its use.

This creates an important design problem. An assistant asked to inspect a screenshot sees the screenshot and the context supplied with it. If the user omits the pressure, prior requests, or contradictory explanations, the assessment lacks those details. A confident response does not restore information that never entered the assessment.

The risk grows when the user does not understand this dependency. The tool appears to deliver an [independent judgment](https://scamsnow.com/glossary-term/independent-judgment/), while its conclusion rests partly on the user’s incomplete account. Effective oversight therefore requires attention to what the system examined, what remained outside its view, and which conclusions the available evidence actually supports.

## Confidence Without Independent Understanding

A history of successful protection provides a powerful reason to keep using a tool. It also creates a misleading basis for judging personal readiness. Someone who has avoided problems while assisted has evidence that the combined arrangement worked on those occasions. That history does not establish how well the person performs when the assistance fails.

The resulting [confidence gap](https://scamsnow.com/glossary-term/confidence-gap/) concerns both the user and the system. The user overestimates personal recognition skills, the system’s coverage, or both. A smooth experience conceals the difference because successful outcomes rarely force either assumption into view.

Adding another person does not automatically resolve the problem. An automation experiment comparing individuals and two-person crews found that teamwork did not eliminate [automation bias](https://scamsnow.com/glossary-term/automation-bias/). Training reduced one class of error without reducing the other. Independent oversight requires [independent assessment](https://scamsnow.com/glossary-term/independent-assessment/), rather than several people accepting the same automated conclusion.

## Better Protection and Greater Dependence Coexist

There is no contradiction between preventing many attacks and creating a dangerous dependence. A system achieves useful protection against the threats it detects while its users become less prepared for failures outside that coverage. Evaluating only successful interceptions misses the second effect.

This does not establish that AI protection increases crime overall. That conclusion requires direct evidence about prevented harms, missed threats, changes in user behavior, and the consequences of failure. Existing research supports concern about learning and reliance, but it does not supply a universal measurement of long-term scam victimization caused by AI dependence.

The distinction strengthens the argument by identifying the actual question. A protective tool needs assessment as part of a changing human system. Its influence includes what it blocks, what it misses, what its users learn, and what they stop doing. Accuracy measured in isolation leaves the dependence problem unanswered.

## Protection That Maintains Judgment

A stronger approach preserves useful protection while keeping essential judgment active. The user receives reasons that explain a significant warning, including the evidence that triggered it and the information still missing. A narrow assessment remains visibly narrow. An incomplete examination does not appear as a clean result.

This approach also changes what counts as successful education. Recognition of a previously demonstrated scam supplies only one measure. A more demanding assessment presents an unfamiliar situation and examines whether the learner notices uncertainty, identifies the consequences, seeks [independent evidence](https://scamsnow.com/glossary-term/independent-evidence/), and pauses when the evidence remains inadequate.

[Safe simulations](https://scamsnow.com/glossary-term/safe-simulations/) provide that experience without exposing people to actual criminals. They also reveal whether users retain skills after repeated assistance. The aim is [practical readiness](https://scamsnow.com/glossary-term/practical-readiness/) when the system is silent or wrong. General instructions to remain vigilant provide no substitute for demonstrating that readiness.

## Independence Does Not Require Constant Suspicion

Maintaining judgment does not require treating every message as hostile or asking every user to become a cybersecurity specialist. That expectation creates an unreasonable burden and ignores the value of well-designed protection. The practical objective is a manageable set of habits tied to significant decisions.

Those habits include distinguishing [verified facts](https://scamsnow.com/glossary-term/verified-facts/) from claims, recognizing when an assessment covers only part of an interaction, and pausing before actions with serious consequences. A person need not identify the precise criminal method to recognize that identity, authority, or purpose remains unverified. Responding appropriately to uncertainty is itself an important protective skill.

Providers also carry responsibility for making this possible. Interfaces, explanations, [escalation routes](https://scamsnow.com/glossary-term/escalation-routes/), and [defaults](https://scamsnow.com/glossary-term/defaults/) influence how people behave. A service that encourages complete reliance while assigning users responsibility for detecting every failure creates a predictable gap between expected oversight and actual preparedness.

## The Very Real Dangers

The dangers of outsourcing risk recognition are concrete. When a person stops noticing uncertainty independently, a missed warning removes more than one layer of protection. It leaves the interaction without the pause that would otherwise prompt verification, consultation, or refusal. At that point, an automated omission becomes an unexamined human decision.

The consequences extend beyond clicking a harmful link. They include sending money under false pretenses, disclosing identity documents, granting access to accounts, and accepting instructions from someone whose authority remains unverified. The tool’s earlier successes provide no protection against consequences arising outside its actual coverage.

Dependence also compromises recovery from failure. A person who lacks an independent understanding struggles to identify what went wrong, explain the event, or determine which other actions need attention. The system’s failure and the user’s lack of preparedness compound one another precisely when clear assessment matters most.

Organizations face the same danger when several employees rely on the same assessment and mistake [shared reliance](https://scamsnow.com/glossary-term/shared-reliance/) for independent review. Repeated approval does not add evidence when every approval rests on one unexamined conclusion. A single blind spot then travels through the whole decision process.

A protective system fails its purpose when it teaches users that silence means safety and leaves them unprepared to question that silence. AI assistance deserves evaluation by both its immediate performance and the human readiness that remains around it. When neither the tool nor the person recognizes danger, the criminal encounters no effective challenge before the damage occurs.

## Conclusion

AI protection delivers its greatest value when it strengthens human judgment alongside [automated detection](https://scamsnow.com/glossary-term/automated-detection/). Screening messages, assessing links, and identifying suspicious activity address important parts of online safety, but these functions do not establish the legitimacy of an entire interaction. A trusted platform still carries deceptive requests, and a technically safe website still serves a criminal’s purpose. Effective protection requires attention to the people, claims, pressures, and consequences surrounding the technology.

The outsourcing of [risk recognition](https://scamsnow.com/glossary-term/risk-recognition/) creates a deeper problem than accepting an occasional incorrect answer. It transfers the responsibility for noticing uncertainty to a system whose coverage remains limited. When users wait for warnings before questioning an interaction, silence becomes permission to proceed. The [protective pause](https://scamsnow.com/glossary-term/protective-pause/) disappears precisely when an undetected threat demands independent scrutiny.

Maintaining that scrutiny requires practice, understandable explanations, and clear boundaries around automated assessments. Novices need opportunities to develop judgment, while experienced users need to keep their knowledge active. Providers must make incomplete assessments visible and evaluate whether users remain prepared when assistance fails. Assigning responsibility without supporting readiness leaves a serious gap in protection.

The consequences of that gap include financial loss, [identity exposure](https://scamsnow.com/glossary-term/identity-exposure/), compromised accounts, and continued manipulation. Several people accepting the same automated verdict do not provide several independent checks. Genuine oversight requires someone to examine the evidence and recognize what remains unknown. When both the system and its user overlook danger, the criminal proceeds without an effective challenge. Preserving the ability to notice, question, and pause remains essential to online safety.

## Article Review

- Assisted performance measures success with a tool, while [independent competence](https://scamsnow.com/glossary-term/independent-competence/) requires separate assessment.
- Research shows that AI’s effects on learning depend partly on task design and user engagement.
- Situation awareness concerns understanding the current circumstances rather than merely possessing subject knowledge.
- [Risk recognition](https://scamsnow.com/glossary-term/risk-recognition/) creates the pause that allows a person to examine an uncertain interaction.
- Automation [omission errors](https://scamsnow.com/glossary-term/omission-errors/) involve missed events that the system fails to flag.
- Automation [commission errors](https://scamsnow.com/glossary-term/commission-errors/) involve acting on incorrect automated recommendations.
- A narrow [technical assessment](https://scamsnow.com/glossary-term/technical-assessment/) leaves questions about identity, intentions, and surrounding pressure unresolved.
- Incomplete information limits an assessment regardless of the confidence expressed in its conclusion.
- Novel threats do not automatically defeat AI or require prior victimization for a person to recognize uncertainty.
- Effective oversight requires independent scrutiny rather than repeated acceptance of one automated verdict.
- [Safe simulations](https://scamsnow.com/glossary-term/safe-simulations/) assess whether users recognize uncertainty and seek evidence when assistance fails.
- Responsible providers explain [coverage limits](https://scamsnow.com/glossary-term/coverage-limits/) and keep incomplete examinations visibly distinct from verified findings.

## Final Question

If an AI tool gave no warning, what would prompt the reader to pause and verify an unfamiliar request before acting?

## Glossary

- Account compromise
- Active engagement
- AI-assisted coding
- AI-assisted performance
- Assessment boundaries
- Assessment inputs
- Automated decision aid
- Automated detection
- Automation bias
- Behavioral patterns
- Commission errors
- Confidence gap
- Contradictory information
- Coverage limits
- Debugging
- Defaults
- Delegation
- Emergency backup
- Escalation routes
- Exploratory analysis
- False reassurance
- Human oversight
- Identity exposure
- Independent assessment
- Independent competence
- Independent evidence
- Independent judgment
- Interface
- Omission errors
- Outsourcing risk recognition
- Override button
- Passive processing
- Practical readiness
- Preregistered experiment
- Protective layers
- Protective pause
- Randomized study
- Risk recognition
- Safe simulations
- Shared reliance
- Situation awareness
- Skill formation
- Skill maintenance
- Skill retention
- System failure
- Technical assessment
- Unassisted examination
- Verification routines
- Verified facts

## Reference & Sources

**1. Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025).** Generative AI without guardrails can harm learning: Evidence from high school mathematics. *Proceedings of the National Academy of Sciences, 122*(26), e2422633122.

Full text: [https://pmc.ncbi.nlm.nih.gov/articles/PMC12232635/](https://pmc.ncbi.nlm.nih.gov/articles/PMC12232635/?utm_source=chatgpt.com)

Publisher: [https://doi.org/10.1073/pnas.2422633122](https://doi.org/10.1073/pnas.2422633122?utm_source=chatgpt.com)

**2. Lira, B., Rogers, T., Goldstein, D. G., Ungar, L., & Duckworth, A. L. (2026).** *Coach not crutch: Evidence that AI can improve writing skill despite reducing effort.* arXiv preprint, version 4. Earlier versions used the title *Learning not cheating: AI assistance can enhance rather than hinder skill development*.

Research record and full-text access: [https://arxiv.org/abs/2502.02880v4](https://arxiv.org/abs/2502.02880v4)

**3. Shen, J. H., & Tamkin, A. (2026).** *How AI impacts skill formation.* arXiv preprint, version 2.

Research record and full-text access: [https://arxiv.org/abs/2601.20245v2](https://arxiv.org/abs/2601.20245v2)

**4. Anthropic. (2026, January 29).** *How AI assistance impacts the formation of coding skills.* Research summary accompanying Shen and Tamkin’s study.

Research summary: [https://www.anthropic.com/research/AI-assistance-coding-skills](https://www.anthropic.com/research/AI-assistance-coding-skills?utm_source=chatgpt.com)

**5. Endsley, M. R., & Kiris, E. O. (1995).** The out-of-the-loop performance problem and level of control in automation. *Human Factors, 37*(2), 381–394.

Publisher page and abstract: [https://journals.sagepub.com/doi/10.1518/001872095779064555](https://journals.sagepub.com/doi/10.1518/001872095779064555?utm_source=chatgpt.com)

**6. Skitka, L. J., Mosier, K. L., & Burdick, M. (1999).** Does [automation bias](https://scamsnow.com/glossary-term/automation-bias/) decision-making? *International Journal of Human-Computer Studies, 51*(5), 991–1006.

Publisher page: [https://www.sciencedirect.com/science/article/pii/S1071581999902525](https://www.sciencedirect.com/science/article/pii/S1071581999902525?utm_source=chatgpt.com)

DOI link:

[https://doi.org/10.1006/ijhc.1999.0252](https://doi.org/10.1006/ijhc.1999.0252)

**7. Skitka, L. J., Mosier, K. L., Burdick, M., & Rosenblatt, B. (2000).** Automation bias and errors: Are crews better than individuals? *The International Journal of Aviation Psychology, 10*(1), 85–97.

PubMed record and abstract: [https://pubmed.ncbi.nlm.nih.gov/11543300/](https://pubmed.ncbi.nlm.nih.gov/11543300/?utm_source=chatgpt.com)

Publisher:

[https://doi.org/10.1207/S15327108IJAP1001_5](https://doi.org/10.1207/S15327108IJAP1001_5)

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