Reference health

Cognitive challenges in human-AI collaboration: Investigating the path towards productive delegation

https://doi.org/10.2139/ssrn.3368813
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2 of 46 checkable references need attention · checked 2026-09-09

At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.

6 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

References needing attention

does not resolve to a known work10.5465/amr.1990.4308227
does not resolve to a known work10.1109/CVPR.2015
The 44 checked references that resolve
resolves10.3758/s13428-021-01588-4
Turking in the time of COVID
resolves10.1080/13645579.2018.1563966
How serious is the ‘carelessness’ problem on Mechanical Turk?
resolves10.1162/003355303322552801
The Skill Content of Recent Technological Change: An Empirical Exploration
resolves10.25300/misq/2021/15882
The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts
resolves10.2307/2523991
Norms of Distributive Justice in Interest Arbitration
resolves10.1097/pts.0000000000000255
Does Physician's Training Induce Overconfidence That Hampers Disclosing Errors?
resolves10.1257/pandp.20181019
What Can Machines Learn and What Does It Mean for Occupations and the Economy?
resolves10.1126/science.aap8062
What can machine learning do? Workforce implications
resolves10.1037/e527772014-223
Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data?
resolves10.3758/s13428-019-01273-7
Online panels in social science research: Expanding sampling methods beyond Mechanical Turk
resolves10.1017/psrm.2018.10
Generalizing from Survey Experiments Conducted on Mechanical Turk: A Replication Approach
resolves10.1037/0003-066x.34.7.571
The robust beauty of improper linear models in decision making.
resolves10.1561/2000000039
Deep Learning: Methods and Applications
resolves10.1037/xge0000033
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
resolves10.1287/mnsc.2016.2643
Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them
resolves10.1145/2736277.2741685
The Dynamics of Micro-Task Crowdsourcing
resolves10.1080/014492999118832
User agreement with incorrect expert system advice
resolves10.1038/nature21056
Dermatologist-level classification of skin cancer with deep neural networks
resolves10.1126/science.1201765
Metaknowledge
resolves10.5195/jmla.2017.88
Open Science Framework (OSF)
resolves10.1287/isre.2021.1009
Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending
resolves10.1037/a0012638
The role of short-term memory capacity and task experience for overconfidence in judgment under uncertainty.
resolves10.1145/3173574.3174023
A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk
resolves10.1145/3173574
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
resolves10.1109/MSP.2012.2205597
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
resolves10.1007/s10683-011-9273-9
The online laboratory: conducting experiments in a real labor market
resolves10.1287/isre.2020.0980
Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence
resolves10.25300/misq/2016/40.2.09
A Multiagent Competitive Gaming Platform to Address Societal Challenges1
resolves10.1027/2192-0923/a000114
A Review of Debriefing Practices
resolves10.1037/0033-2909.107.3.296
Why we still use our heads instead of formulas: Toward an integrative approach.
resolves10.1016/s0933-3657(01)00077-x
Machine learning for medical diagnosis: history, state of the art and perspective
resolves10.1287/mnsc.1110.1382
Demand Forecasting Behavior: System Neglect and Change Detection
resolves10.1038/nature14539
Deep learning
resolves10.1111/poms.12841
Running Behavioral Operations Experiments Using Amazon's Mechanical Turk
resolves10.1016/j.obhdp.2018.12.005
Algorithm appreciation: People prefer algorithmic to human judgment
resolves10.4135/9781446279212.n1
From Social Cognition to Metacognition
resolves10.1037/11281-000
Clinical versus statistical prediction: A theoretical analysis and a review of the evidence.
resolves10.1073/pnas.1708274114
The preregistration revolution
resolves10.1016/s0191-8869(97)00028-7
Overconfidence: Feedback and item difficulty effects
resolves10.1371/journal.pone.0226394
Tapped out or barely tapped? Recommendations for how to harness the vast and largely unused potential of the Mechanical Turk participant pool
resolves10.1007/s11263-015-0816-y
ImageNet Large Scale Visual Recognition Challenge
resolves10.1016/j.neunet.2014.09.003
Deep learning in neural networks: An overview
resolves10.1109/cvpr.2016.308
Rethinking the Inception Architecture for Computer Vision
resolves10.1613/jair.1.11345
Viewpoint: Human-in-the-loop Artificial Intelligence
The 6 references without a DOI — listed, not checked
no DOI — not checkedPolanyis' paradox and the shape of employment growth
no DOI — not checkedBig data's biggest challenge? convincing people not to trust their judgement
no DOI — not checkedref40
no DOI — not checkedref43
no DOI — not checkedManaging overconfidence
no DOI — not checkedref48
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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