ArticleCognitive research: principles and implications2024
The impact of AI errors in a human-in-the-loop process.
Article in Cognitive research: principles and implications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 39 citations in OpenAlex.
- Doctors vs. Algorithms: Physicians, too, struggle to learn from evidence that contradicts AI suggestions.PLOS digital health · 2026Article
- AI as a Sparring Partner in Professional Settings: Counselors' Views on ChatGPT's Role and Potential in Pregnancy Termination Counseling.Archives of sexual behavior · 2026Article
- Warning people about the risk of AI error mitigates human acquisition of AI bias.Cognitive research: principles and implications · 2026Article
- Human-in-the-Loop Artificial Intelligence: A Systematic Review of Concepts, Methods, and Applications.Entropy (Basel, Switzerland) · 2026Review
- Autonomous language-image generation loops converge to generic visual motifs.Patterns (New York, N.Y.) · 2026Article
- Statistical process control for performance monitoring and continuous quality assurance of deep learning segmentations in radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Cyber-Creativity: A Decalogue of Research Challenges.Journal of Intelligence · 2025Article
- Representation of Demographics in Otolaryngology by Artificial Intelligence Text-to-Image Platforms.Laryngoscope investigative otolaryngology · 2025Article
- Metacognitive sensitivity: The key to calibrating trust and optimal decision making with AI.PNAS nexus · 2025Article
- Toward viewing behavior for aerial scene categorization.Cognitive research: principles and implications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Automated decision-making is becoming increasingly common in the public sector. As a result, political institutions recommend the presence of humans in these decision-making processes as a safeguard against potentially erroneous or biased algorithmic decisions. However, the scientific literature on human-in-the-loop performance is not conclusive about the benefits and risks of such human presence, nor does it clarify which aspects of this human-computer interaction may influence the final decision. In two experiments, we simulate an automated decision-making process in which participants judge multiple defendants in relation to various crimes, and we manipulate the time in which participants receive support from a supposed automated system with Artificial Intelligence (before or after they make their judgments). Our results show that human judgment is affected when participants receive incorrect algorithmic support, particularly when they receive it before providing their own judgment, resulting in reduced accuracy. The data and materials for these experiments are freely available at the Open Science Framework: https://osf.io/b6p4z/ Experiment 2 was preregistered.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.