The DOI matches its Crossref record; 46 of the 48 references it cites were found in a registry or an index; none of the 28 sources its claims cite is checked for support yet.
Understanding, explaining, and utilizing medical artificial intelligence
Open accessAbstract
Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artificial intelligence is driven by both the subjective difficulty of understanding algorithms (the perception that they are a 'black box') and by an illusory subjective understanding of human medical decision-making. In five pre-registered experiments (1-3B: N = 2,699), we find that people exhibit an illusory understanding of human medical decision-making (study 1). This leads people to believe they better understand decisions made by human than algorithmic healthcare providers (studies 2A,B), which makes them more reluctant to utilize algorithmic than human providers (studies 3A,B). Fortunately, brief interventions that increase subjective understanding of algorithmic decision processes increase willingness to utilize algorithmic healthcare providers (studies 3A,B). A sixth study on Google Ads for an algorithmic skin cancer detection app finds that the effectiveness of such interventions generalizes to field settings (study 4: N = 14,013).
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
People report greater subjective understanding of medical decisions made by human than algorithmic providers, but their objective understanding is similarly poor; this illusory understanding contributes to algorithm aversion. Brief explanations of how medical AI works increase subjective understanding and willingness to use algorithmic providers, including in a Google Ads field study.
Key findings
- In study 1 (N = 297), making people explain provider decision processes reduced subjective understanding for human providers (Mpre = 5.08 vs Mpost = 4.52, ΔH = −0.56, P < 0.001) but not for algorithmic providers (Mpre = 3.99 vs Mpost = 3.83, ΔA = −0.16, P = 0.14); the interaction was significant (ΔH − ΔA = −0.40, P = 0.009).
- In studies 2A/B, participants claimed greater subjective understanding of human than algorithmic providers (2A: β = −0.82, P < 0.001; 2B: β = −0.46, P = 0.001), while objective understanding did not differ by provider (2A: β = 0.02, P = 0.91; 2B: β = 0.11, P = 0.43).
- In studies 3A/B, interventions explaining decision processes increased subjective understanding and reduced algorithm aversion; utilization intentions increased more for algorithmic providers (3A: β = 0.83, P < 0.001; 3B: β = 0.70, P < 0.001) than for human providers (3A: β = 0.34, P = 0.008; 3B: β = 0.14, P = 0.41).
- In study 4 (N = 14,013 impressions), the intervention advertisement had a higher click-through rate (6.36%) than the control advertisement (3.29%; β = 0.69, z = 8.17, P < 0.001, d = 0.38).
Methods
- Five pre-registered online experiments (studies 1, 2A/B, 3A/B; N = 2,699) plus a sixth pre-registered online field study on Google Ads (study 4; N = 14,013). Participants were US residents recruited from Lucid, Amazon Mechanical Turk, and Google Ads, in a skin cancer detection context.
- Designs included: study 1, 2 (provider: human vs algorithm) × 2 (rating order: pre- vs post-intervention) mixed design; studies 2A/B, 2 (provider) × 2 (understanding type: subjective vs objective) between-subjects; studies 3A/B, 2 (provider) × 2 (intervention: control vs intervention) between-subjects; study 4, intervention vs control advertisement.
- Analyses used regressions, moderated mediation with 5,000 bootstrap samples, and logistic regression for click-through rate. Pre-registrations, raw data, and Stata syntax are available at https://osf.io/taqp8/.
Limitations
- The paper states that mediation analysis has limitations and invites replication with alternative manipulations, designs, and measures; it also notes that the ancillary objective understanding measure may have been sensitive, and that wider-range questions might detect subtle changes. For study 4, the authors state that ad assignment was not random because Google Ads optimized for clicks, which may have generated unintended variance, though they believe replication in studies 3A/B makes it unlikely the results are solely due to ad optimization.
- Evident from the text: studies 1–3B measure subjective understanding and healthcare utilization intentions rather than actual clinical uptake, and study 4 measures ad click-through rather than real patient utilization; the samples are online panels and Google Ads users, not a clinical patient population.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
22 Own method · 20 Own result · 16 Not checked yet · 15 Own interpretation · 2 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A logistic regression revealed that the click-through rate was higher in the intervention advertisement (6.36%) compared with the control advertisement (3.29%, β = 0.69, z = 8.17, P < 0.001, d = 0.38).”
- Also stated in: § Discussion · plain
“Indeed, we find increased advertising efficacy for SkinVision, a skin cancer detection app, when advertising included language explaining how it works.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Discussion · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but they exhibit similar objective understanding of decisions made by both providers (people actually know as little about both decision processes).”
- Also stated in: § Studies 2A,B. · plain
“whereas their objective understanding did not differ by provider (2A: β = 0.02, t = 0.11, P = 0.91; 2B: β = 0.11, t = 0.79, P = 0.43)”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Participants' self-reported subjective understanding accurately reflects their subjective understanding.
- Also stated in: § Studies 2A,B. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It increased subjective understanding of the decision made by algorithmic providers (3A: Δ = 1.42, t = 12.51, P < 0.001; 3B: Δ = 0.56, t = 6.21,P < 0.001) and human providers (3A: Δ = 1.04, t = 11.08,P < 0.001; 3B: Δ = 1.39, t = 11.85,P < 0.001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the indirect effect of healthcare provider on utilization intentions through subjective understanding was significantly stronger (3A: Δ = 0.20, 95% CI [0.05, 0.37]; 3B: Δ = 0.60, 95% CI [0.35, 0.89]) in the control condition”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Healthcare utilization intentions are a valid proxy for actual healthcare utilization.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, we find that people claim greater subjective understanding for medical decisions made by human than AI providers (people feel that they know more), but they exhibit similar objective understanding of decisions made by both providers”
- Also stated in: § Greater reductions in pre-intervention to post-intervention ratings indicate a larger illusion of understanding. · plain
“Participants claimed greater subjective understanding of a human than an algorithmic decision processes (pre-intervention: β = 1.08, z = 5.02, P < 0.001; post-intervention: β = 0.69, z = 3.19, P = 0.001).”
- Also stated in: § Studies 2A,B. · plain
“Participants claimed greater subjective understanding of medical decisions made by human than algorithmic providers (2A: β = -0.82,t = -6.01,P < 0.001; 2B: β = -0.46,t = -3.33,P = 0.001)”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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- Rests on an assumption: Participants' self-reported subjective understanding accurately reflects their subjective understanding.
- Also stated in: § Greater reductions in pre-intervention to post-intervention ratings indicate a larger illusion of understanding. · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the intervention increased utilization intentions to a greater extent for algorithmic providers (3A: β = 0.83, t = 6.46,P < 0.001; 3B: β = 0.70, t = 4.09, P < 0.001) than for human providers (3A: β = 0.34, t = 2.66, P = 0.008; 3B: β = 0.14, t = 0.82, P = 0.41).”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Healthcare utilization intentions are a valid proxy for actual healthcare utilization.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A simple one-page visual or sentence that explains the criteria or process used to make medical decisions increased acceptance of an algorithm-based skin cancer diagnostic tool”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The difference in subjective understanding of the decision made by the human and algorithmic provider was smaller in the intervention condition (3A: β = -0.70,t = -5.41,P < 0.001; 3B: β = -1.10,t = -6.97,P < 0.001) than in the control condition (3A: β = -0.22,t = -1.74,P = 0.08; 3B: β = -0.08,t = -0.52,P = 0.60).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Algorithm aversion in healthcare utilization (greater reluctance to utilize algorithmic than human providers) was smaller in the intervention condition (3A: β = -0.58,t = -4.50,P < 0.001; 3B: β = -0.01,t = -0.05,P = 0.96) than in the control condition (3A: β = -1.08,t = -8.32,P < 0.001; 3B: β = -0.57,t = -3.31,P = 0.001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Healthcare utilization intentions are a valid proxy for actual healthcare utilization.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Explanations reduce the difference in subjective understanding between algorithmic and human decision-making in study 3A (a) and 3B (c)”
plaindirectn = 798- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 22
- Probably not a substantial claim. 24
- The quote only partly states this claim. 3
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the intervention significantly reduced reported subjective understanding of decisions made by human providers (M pre = 5.08 versus M post = 4.52, Δ H = -0.56,z = -5.27,P < 0.001), but not of decisions made by algorithmic providers (M pre = 3.99 versus M post = 3.83, Δ A = -0.16,z = -1.47,P = 0.14).”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The advertisements generated 14,013 impressions (that is, how many times the ad was viewed across the following demographics: age: 18-24 years: 6.9%, 25-35 years: 5.7%, 35-44 years: 7.3%, 45-54 years: 15.1%, 55-64 years: 35.6%, 65+ years: 29.4%; gender: 78.8% female) and generated 698 clicks.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Click-through rate on Google Ads is a valid measure of healthcare utilization.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“creating the predicted provider × understanding interaction (2A: β = -0.80,t = -4.19,P < 0.001; 2B: β = -0.35,t = -1.78,P = 0.08).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Indeed, in an ancillary study examining the marketing of skin cancer smartphone applications (Supplementary Appendix 8), we find that performance-related keywords were used to describe 57-64% of the applications, whereas process-related keywords were used to describe 21% of the applications.”
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Results from this experiment show that, relative to a control condition, the interventions used in studies 3A and 3B significantly increased subjective understanding (respectively, β = 0.67, t = 4.90, P < 0.001; β = 0.82, t = 6.05,P < 0.001) but did not influence objective understanding (respectively, P = 0.78 and P = 0.89; all interactions, ts > 3.60, ps < 0.001).”
plaindirectn = 601 ·- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The analyses revealed the predicted significant interaction (Δ H -Δ A = -0.40,z = -2.61,P = 0.009; Fig. 1).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Making people explain provider decision processes reduces their subjective understanding of decisions made by human providers, but not decisions made by algorithmic providers (study 1; N = 297).”
plaindirectn = 297- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Decision processes used by algorithms are perceived to be opaque by patients, because they are often actually opaque to medical professionals and even their creators, who cannot explain them 15,16 .”
What the sources say
- Not checked yetcites [15] Castelvecchi, D. Can we open the black box of AI? Nature 538, 20–23 (2016). source
- Not checked yetcites [16] Kroll, J. A. et al. Accountable algorithms. Univ. Pa. Law Rev. 165, 633 (2016).
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Algorithmic-based healthcare services (medical 'artificial intelligence' (AI)) are cost-effective and scalable and provide expert-level accuracy in applications ranging from the detection of skin cancer 5 and emergency department triage 6,7 to diagnoses of COVID-19 from chest X-rays 8 .”
- Also stated in: § Understanding, explaining, and utilizing medical artificial intelligence · plain
“Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it.”
What the sources say
- Not checked yetcites [5] Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). source
- Not checked yetcites [6] Laranjo, L. et al. Conversational agents in healthcare: a systematic review. J. Am. Med. Inform. Assoc. 25, 1248–1258 (2018). source
- Not checked yetcites [7] Goto, T., Camargo, C. A. Jr, Faridi, M. K., Freishtat, R. J. & Hasegawa, K. Machine learning-based prediction of clinical outcomes for children during emergency department triage. JAMA Netw. Open 2, e186937–e186937 (2019). source
plain- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Understanding, explaining, and utilizing medical artificial intelligence · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we developed a quiz in consultation with experts in the relevant medical domain (preliminary visual inspection of moles): a team of dermatologists at a medical school in the Netherlands, and a team of developers of a popular skin cancer detection application in Europe.”
plain- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Condition assignments were random in all our studies, with randomization administered by Qualtrics software.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we ran an additional three (intervention: control, study 3A, study 3B) × 2 (understanding: subjective, objective) between-subjects pre-registered experiment with 601 participants on Amazon Mechanical Turk.”
plainn = 601- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All conditions, measures and exclusions are reported; data and pre-registrations are available at https://osf.io/taqp8/.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Amazon Mechanical Turk is an online sample recruiting platform that, although not nationally representative, yields high data quality through the use of attention checks 44 and worker reputation scores 45 .”
What the sources say
- Not checked yetcites [44] Paolacci, G., Chandler, J. & Ipeirotis, P. G. Running experiments on Amazon Mechanical Turk. Judgm. Decis. Mak. 5, 411–419 (2010). source
- Not checked yetcites [45] Peer, E., Vosgerau, J. & Acquisti, A. Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behav. Res. Methods 46, 1023–1031 (2014). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Following prior research, we selected participants with an approval rating above 95% on Amazon Mechanical Turk 45 .”
What the sources say
- Not checked yetcites [45] Peer, E., Vosgerau, J. & Acquisti, A. Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behav. Res. Methods 46, 1023–1031 (2014). source
plain- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Studies 1-3B were approved for use with human participants by the Institutional Review Board on the Charles River Campus at Boston University (protocol 3632E); informed consent was obtained for all participants.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“All manipulations and measures are reported.”
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results suggest an egocentric bias in this illusion of understanding, that the illusion may loom largest in assessments of people-the causal systems most similar to ourselves.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We suggest that these coupled effects make people aware of their limited understanding of medical decisions made by algorithmic providers, but also lead them to overestimate their understanding of (similarly opaque) decisions made by human providers.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Pre-registrations, raw data and Stata syntax files are available on the Open Science Framework at https://osf.io/ taqp8/.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We tested our predictions in a set of five pre-registered online experiments with nationally representative and convenience samples (studies 1, 2A,B and 3A,B: N = 2,699), and a sixth pre-registered online field study on Google Ads (study 4: N = 14,013).”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which could be easily adapted to other domains and procedures.”
hedged- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants were randomly assigned to one of the two provider conditions between subjects, and ratings of subjective understanding were repeated within subjects.”
- Also stated in: § Studies 2A,B. · plain
“Participants were randomly assigned to one of four conditions in a 2 (provider: human, algorithm) × 2 (understanding type: subjective, objective) between-subject design.”
- Also stated in: § Studies 2A,B. · plain
“In both studies, participants were randomly assigned to one of four conditions in a 2 (provider: human, algorithm) × 2 (intervention: control, intervention) between-subject design.”
plain- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Studies 2A,B. · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Improving subjective understanding of how medical AI works may then not only provide beneficent insights for increasing consumer adoption but also for firms seeking to improve their positioning.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Providers tend to present AI provider decisions as a 'black box' because the technologies used in algorithmic-based medical applications are complex.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All experiments were approved for use with human subjects by institutional review boards.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This study was approved by the Erasmus University Erasmus Research Institute of Management Internal Review Board.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“It is impossible to identify, interact with and obtain consent from individual participants.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cronbach's α = 0.65 in the human condition and α = 0.67 in the algorithm condition”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Study 4: Allocation from Google platform”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The present research involved no more than minimal risks, and all study participants were 18 years of age or older.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We screened out N = 211 participants in study 1, N = 255 participants in study 2A, N = 260 participants in study 3A and N = 378 participants in study 3B.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No exclusions in data analyses”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No participant declined participation”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Pre-registrations, raw data and syntax files are available on the Open Science Framework at https://osf.io/taqp8/”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In studies 1, 2A and 3A,B, as pre-registered, we programmed our surveys to automatically exclude participants who failed an attention check at the very beginning of the survey and prior to any manipulation.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Finally, people do exhibit an illusion of explanatory depth of causal systems [21][22][23]42 in many domains, claiming to better understand a variety of ideas, technologies and biological systems than they objectively do.”
What the sources say
- Not checked yetcites [21][22][23]42
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Randomization Studies 1, 2A, 2B, 3A, 3B: Qualtrics randomizer”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The literature on algorithm aversion finds that people are generally averse to using algorithms for tasks that are usually done by humans 34,35 , on the grounds that algorithms are perceived as unable to learn and improve from their mistakes 36 , unsuitable for subjective or experiential tasks 37,38 , unable to adapt to unique or mutable circumstances 10,39 and unable to carry responsibility for”
What the sources say
- Not checked yetcites [34] Dawes, R., Faust, D. & Meehl, P. Clinical versus actuarial judgment. Science 243, 1668–1674 (1989). source
- Not checked yetcites [35] Yeomans, M., Shah, A., Mullainathan, S. & Kleinberg, J. Making sense of recommendations. J. Behav. Decis. Mak. 32, 403–414 (2019). source
- Not checked yetcites [36] Dietvorst, B. J., Simmons, J. P. & Massey, C. Algorithm aversion: people erroneously avoid algorithms after seeing them err. J. Exp. Psychol. Gen. 144, 114 (2015). source
- Not checked yetcites [37] Castelo, N., Bos, M. W. & Lehmann, D. R. Task-dependent algorithm aversion. J. Mark. Res. 56, 809–825 (2019). source
- Not checked yetcites [38] Longoni, C. & Cian, L. Artificial intelligence in utilitarian vs. hedonic contexts: the “word-of-machine” effect. J. Market. https://doi.org/10.1177/0022242920957347 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Healthcare utilization intentions are a valid proxy for actual healthcare utilization.
“Healthcare utilization intentions were measured in study 3A with the item”
“they reported their healthcare utilization intentions.”
The paper relies on this without support.
The skin cancer detection context is representative of medical AI decision-making generally.
“In the same skin cancer detection context as study 1”
“medical AI”
The paper relies on this without support.
Human and algorithmic providers in the studies are comparable in quality or accuracy.
“comparable human providers”
The paper relies on this without support.
Participants' self-reported subjective understanding accurately reflects their subjective understanding.
“participants reported their subjective understanding”
“Subjective understanding was measured in study 3A with the item used in study 2A.”
The paper relies on this without support.
Click-through rate on Google Ads is a valid measure of healthcare utilization.
“tested the impact of that intervention on ad click-through rates.”
The paper relies on this without support.
The Google Ads platform serves ads according to the campaign goal (maximizing clicks), so the frequency of appearance of different ads was not random.
“The frequency of appearance of the different ads was not random, because the Google Ads platform serves ads according to the goal of the campaign (maximizing clicks, in our case).”
The paper relies on this without support.
Providers tend to present AI provider decisions as a 'black box' because the technologies used in algorithmic-based medical applications are complex.
“Because the technologies used in algorithmic-based medical applications are complex, providers tend to present AI provider decisions as a 'black box' .”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Decisions made by human physicians or AI are probably equally unobservable to a patient—but because patients feel that they can understand the decision-making as explained by human providers, they ultimately penalize and resist the clinical use of AI (122).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [122]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“First, it may prevent the formation of reliable case-by-case expectations of AI accuracy [65 , 66] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [65]
Experimental — accuracy not yet measuredIn the citing paper
“Algorithmic aversion refers to a phenomenon where clinicians or healthcare professionals exhibit reluctance or skepticism toward relying on AI algorithms for making diagnostic or treatment decisions in medical imaging.46”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [46]
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentz = -2.61reported p = .009 · recomputed p = .009
“,z = -2.61,p = .009”
- Consistentz = 5.02reported p < .001 · recomputed p < .001
“z = 5.02, p < .001”
- Consistentz = 3.19reported p = .001 · recomputed p = .001
“z = 3.19, p = .001”
- Consistentz = -5.27reported p < .001 · recomputed p < .001
“,z = -5.27,p < .001”
- Consistentz = -1.47reported p = .14 · recomputed p = .142
“,z = -1.47,p = .14”
- Consistentz = 8.17reported p < .001 · recomputed p < .001
“z = 8.17, p < .001”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Studies 2A,B.
“The difference in subjective understanding of the decision made by the human and algorithmic provider was smaller in the intervention condition”
§ Studies 2A,B.
“(3A: β = -0.70,t = -5.41,P < 0.001; 3B: β = -1.10,t = -6.97,P < 0.001) than in the control condition (3A: β = -0.22,t = -1.74,P = 0.08; 3B: β = -0.08,t = -0.52,P = 0.60).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Studies 2A,B.
“Participants claimed greater subjective understanding of medical decisions made by human than algorithmic providers (2A: β = -0.82,t = -6.01,P < 0.001; 2B: β = -0.46,t = -3.33,P = 0.001)”
§ Studies 2A,B.
“We then compared them by regressing understandings on provider (0 for algorithm, 1 for human), understanding type (0 for subjective, 1 for objective) and their interaction.”
Citations
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Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
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“Amazon Mechanical Turk is an online sample recruiting platform that, although not nationally representative, yields high data quality through the use of attention checks 44 and worker reputation scores 45 .”
BackgroundAlgorithm aversion: People erroneously avoid algorithms after seeing them err.
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Cited by
Sorted by citations
Items 101–200 of 338
Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration
Sobaih, A. E. E., Chaibi, A., Brini, R., & Ibrahim, T. M. A. (2025) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe15010006 · Cited by 33
“Cadario et al. (2021) identified resistance to medical AI, attributing it to challenges in consideration of algorithms and misleading consideration of human medical decision-making, proposing interventions to promote AI adoption.”
Jagemann, I., Stegemann, M., von Brachel, R., & Hirschfeld, G. H. F. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-06134-y · Cited by 17
AI-disclosure and brand modernity: the role of “AI=novelty” lay belief
Xia, Q., He, X., & Gong, S. (2025) · Journal of Product & Brand Management · doi:10.1108/jpbm-11-2024-5575 · Cited by 19
Winder, P. D., Hildebrand, C., & Hartmann, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325459 · Cited by 25
Chantziantoniou, N. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100182 · Cited by 16
“quantitating aspects of subjectively interpreted diagnostic screening practices as, for instance, detection of skin cancer in dermatopathology, or Covid-19 in pulmonary radiography.(1) Equally, innovative systems as the BestCyte Cell Sorter Imaging System (BestCyte) (CellSolutions LLC,”
BackgroundAI-induced indifference: Unfair AI reduces prosociality
Zhang, R. Z., Kyung, E. J., Longoni, C., Cian, L., & Mrkva, K. (2024) · Cognition · doi:10.1016/j.cognition.2024.105937 · Cited by 21
Self-citationKumar, N., Patel, M., Nguyen, T. T., Lee, J., Choi, C., Bhatnagar, P. K., & Kim, J. (2024) · Advanced Materials · doi:10.1002/adma.202411420 · Cited by 15
Judging in the Dark: How Delivery Riders Form Fairness Perceptions Under Algorithmic Management
Xiang, Y., Du, J., zheng, X., Long, L., & Xie, H. Y. (2024) · Journal of Business Ethics · doi:10.1007/s10551-024-05883-w · Cited by 17
“In particular, the platform gives everyone the same information, but people may understand it differently depending on their trust in technology, level of knowledge, work history, and other factors (Cadario et al., 2021).”
Wang, H., Sun, Q., Gu, L. C., Lai, K., & He, L. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1006173 · Cited by 14
“One potential explanation for this pertains to the complexity and heterogeneity of medical AI use (Cadario et al., 2021).”
BackgroundMethodInfluentialRyu, J., Heisig, S., McLaughlin, C., Katz, M., Mayberg, H. S., & Gu, X. (2023) · iScience · doi:10.1016/j.isci.2023.106860 · Cited by 19
“Indeed, the lack of interpretability of model features in machine learning has been raised as a culprit for clinicians’ reluctance to utilize artificial intelligence in health care.(15)”
BackgroundSilber, D., Hoffmann, A. O. I., & Belli, A. (2025) · International Journal of Bank Marketing · doi:10.1108/ijbm-10-2024-0647 · Cited by 22
Zhang, W.-Z., & Lian, R. (2025) · BMC Psychology · doi:10.1186/s40359-025-02741-w · Cited by 20
“As a result, resistance to algorithms is reinforced [6, 20, 21].”
Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation
Hoebel, K., Bridge, C. P., Ahmed, S., Akintola, O. O., Chung, C. D., Huang, R. Y., Johnson, J. M., Kim, A. E., Ly, K. I., Chang, K., et al. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.220231 · Cited by 15
López‐López, V., Morise, Z., Albaladejo‐González, M., Gómez‐Gavara, C., Goh, B. K. P., Koh, Y. X., Sijberden, J. P., Hilal, M. A., Mishima, K., Krüger, J. A. P., et al. (2024) · Surgical Endoscopy · doi:10.1007/s00464-024-10681-6 · Cited by 13
“AI has been incorporated into our day-to-day lives influencing many of the decisions we make, in many cases without us being aware of it ourselves [11, 12].”
BackgroundThe Impact of Recommender Type on Tourist Price Sensitivity to Travel Plans
Xu, J., Tang, X., Lin, H., & Yong, L. (2024) · Journal of Travel Research · doi:10.1177/00472875241261633 · Cited by 15
“Tourists’ illusions that they possess a better understanding of humans than of AI aligns with the concept of the illusion of understanding (Cadario et al., 2021), a phenomenon in which individuals overestimate their understanding of how others make judgments and decisions (Bonezzi et al., 2022;…”
InfluentialThe Impact of Generative Artificial Intelligence on Socioeconomic Inequalities and Policy MakingPreprint
Capraro, V., Lentsch, A., Acemoğlu, D., Akgün, S., Akhmedova, A., Bilancini, E., Bonnefon, J., Brañas‐Garza, P., Butera, L., Douglas, K. M., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4666103 · Cited by 14
Self-citationYao, J., Zhou, Z., Cui, H., Ouyang, Y., & Han, W. (2025) · BMC Medical Ethics · doi:10.1186/s12910-025-01300-7 · Cited by 24
“Previous research on medical AI adoption has primarily drawn on technology acceptance perspectives, emphasizing performance and ease of use as drivers of usage intentions [6, 39].”
The promise of artificial intelligence in health: Portrayals of emerging healthcare technologies
Watson, A., & Wozniak‐O’Connor, V. (2024) · Sociology of Health & Illness · doi:10.1111/1467-9566.13840 · Cited by 20
Groh, R., Lei, Z., Martignetti, L., Li‐Jessen, N. Y. K., & Kist, A. M. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202100284 · Cited by 11
From Stigma to Acceptance: Ethical Implications of Anthropomorphic Design in Healthcare Chatbots
Chen, F., Pang, Y., & Wang, L. (2025) · Journal of Business Ethics · doi:10.1007/s10551-025-06052-3 · Cited by 12
“Given the pervasiveness of mental health problems, AI-based chatbots have emerged as a viable solu-tion for providing accessible healthcare support to consumers, ranging from diagnosis to treatment (Cadario et al., 2021; Timmons et al., 2023; Zhu et al., 2022).”
Chen, C., & Zheng, Y. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2279658 · Cited by 13
“…recommendations (e.g. word-of-mouth, customer reviews), algorithms cannot explain their decision-making processes and the logic of AI recommendations to their users, i.e. the question of why and how AI algorithms generate such outputs is unclear (Cadario, Longoni, and Morewedge 2021; Shin 2021).”
Bulut, O., Wongvorachan, T., He, S., & Lee, S. (2024) · Discover Education · doi:10.1007/s44217-024-00209-4 · Cited by 15
When You Feel You Own AI Assistants: How Consumer Ownership Enhances Consumers' Adoption Intention
Zhang, X., Chen, H.-Y., & Ma, Y. (2025) · Journal of Consumer Behaviour · doi:10.1002/cb.2491 · Cited by 11
How Algorithms Constrain Consumer Experience
Kaliyamurthy, A. K., & Schau, H. J. (2025) · Journal of Consumer Research · doi:10.1093/jcr/ucaf016 · Cited by 13
Augmenting Human Teams with Robots in Knowledge Work Settings: Insights from the Literature
Ren, Y., & Clement, J. (2024) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3649884 · Cited by 18
“For example, explanations by a robot or transparency increased human trust but not necessarily human adoption or compliance with the robot [ 158 –160 ].”
BackgroundKim, M.-W., Kang, D., Kim, M. S., Choe, J. C., Lee, S. H., Ahn, J. H., Oh, J., Choi, J. H., Lee, H. C., Cha, K. S., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae114 · Cited by 13
A single latent channel is sufficient for biomedical glottis segmentation
Kist, A. M., Breininger, K., Dörrich, M., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-17764-1 · Cited by 10
“However, these deep neural networks commonly have a black box character, lowering their acceptance in a clinical environment 11 .”
BackgroundEU Regulation of Artificial Intelligence: Challenges for Patients’ Rights
van Kolfschooten, H. (2022) · Common Market Law Review · doi:10.2139/ssrn.3997366 · Cited by 44
Leading AI Adoption in Organizations: Introducing a Behavioral Human-Centered Approach
Schweitzer, S., Narayanan, D., McGuire, J., & Cremer, D. D. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2531287 · Cited by 11
“In the context of medical AI, people tended to (incorrectly) believe that they understand human decision-making better than AI decision-making, leading them to be less receptive to algorithmic medical providers (Cadario et al., 2021).”
Ding, X., & Xing, C. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105945 · Cited by 9
Measuring trustworthiness is crucial for medical AI tools
Kundu, S. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01711-9 · Cited by 9
Misinformation does not reduce trust in accurate search results, but warning banners may backfire
Williams‐Ceci, S., Macy, M. W., & Naaman, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-61645-8 · Cited by 13
“This scale has been used to measure the strength of people’s attitudes in uncertain contexts 43,44 , including attitudes about the trustworthiness of Covid-19 information 45 .”
MethodPredicting working alliance in psychotherapy: A multi-modal machine learning approach
Doorn, K. A., Cicconet, M., Cohn, J. F., & Aafjes, M. (2025) · Psychotherapy Research · doi:10.1080/10503307.2024.2428702 · Cited by 11
“Indeed, the lack of interpretability of model features in machine learning has been raised as a culprit for clinicians’ reluctance to utilize artificial intelligence in health care (Cadario et al., 2021).”
Social avoidance needs boost AI's nonsocial attribute valuation in secret consumption
Li, J., Zhao, J., & Huang, J. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.102817 · Cited by 9
Algorithm Delegation: How Embedded AI Facilitates Agency Transference in Medical Services
Wagner, R., Pinto, D. C., Hildebrand, D. F. N., Pacheco, N. A., Dhillon, G. S., & Herter, M. M. (2025) · Psychology and Marketing · doi:10.1002/mar.70016 · Cited by 10
“Prior research also indicates that the discomfort when dele-gating medical decisions to AI agents may stem from consumers' view of AI as a distant, detached, and abstract entity – a “ distant other ” , or a “ black box ” (Cadario et al. 2021).”
InfluentialLim, M. J. R. (2021) · Neurosurgery · doi:10.1093/neuros/nyab337 · Cited by 9
Sonographer interaction with artificial intelligence: collaboration or conflict?
Day, T. G., Matthew, J., Budd, S., Hajnal, J. V., Simpson, J. M., Razavi, R., & Kainz, B. (2023) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.26238 · Cited by 10
“The causes of this phenomenon are not fully understood, but may relate to a belief that humans can better understand the reasons underlying a decision made by a fellow human, and so are more confident in assessing the likely accuracy of that prediction 37 .”
BackgroundOvercome Medical Algorithm Aversion: Conditional Joint Effect of Direct and Indirect Information
Zhao, Y., & Xiao, C. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2344146 · Cited by 10
“Clarifying the diagnostic process significantly improves people’s understanding of AI decisions, which further increases people’s willingness to adopt medical AI systems (Cadario et al., 2021).”
InfluentialI Care That You Don’t Share: Confidentiality in Student-Robot Interactions
Mennens, K., Becker, M., Briker, R., Mahr, D., & Steins, M. M. H. M. G. (2024) · Journal of Service Research · doi:10.1177/10946705241295849 · Cited by 10
“Even simple explanations of how a robot performs its task are often suf fi cient to reduce worries of using it (Cadario, Longoni, and Morewedge 2021).”
How AI sources can increase openness to opposing views
Lu, L., Tormala, Z. L., & Duhachek, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-00791-z · Cited by 20
Enhancing AI-Assisted Purchase Decisions: The Role of the Sense of Autonomy
Hou, J., Yang, S., Xiong, G., & Pavlou, P. A. (2025) · MIS Quarterly · doi:10.25300/misq/2025/17607 · Cited by 8
Baldus, S. G., Wiesmann, M., Habel, U., Gerhards, A., Hasan, D., Weyland, C. S., Truhn, D., Hasl, M. M., Clemens, B., & Nikoubashman, O. (2026) · Insights into Imaging · doi:10.1186/s13244-025-02159-3 · Cited by 11
“Hence, AI is often perceived as a “ black box ” and therefore rejected [23].”
Proximity bias: Interactive effect of spatial distance and outcome valence on probability judgments
Hong, J. S., Longoni, C., & Morwitz, V. G. (2023) · Journal of Consumer Psychology · doi:10.1002/jcpy.1341 · Cited by 9
Self-citationAcceptance of Automated Vehicles Is Lower for Self than Others
Agarwal, S., Freitas, J. D., Ragnhildstveit, A., & Morewedge, C. K. (2024) · Journal of the Association for Consumer Research · doi:10.1086/729900 · Cited by 9
“…2018; Dietvorst, Simmons, and Massey 2018; Longoni, Bonezzi, and More-wedge 2019; Wertenbroch et al. 2020), fl exibility in learning from mistakes (Reich, Kaju, and Maglio 2022), complexity (Yeomans et al. 2019), and opaqueness — aka their “ black box ” nature (Cadario, Longoni, and Morewedge 2021).”
BackgroundSelf-citationXian, T., Mehandjiev, N., Constantinides, P., Chen, Y., Quboa, Q., & Kitchen, G. B. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106287 · Cited by 10
AI Pilot in the Cockpit: An Investigation of Public Acceptance
Gao, S., Lu, Z., Luan, H., Yin, M., & Wang, L. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2301856 · Cited by 7
“Different inspirations are widely used to establish a scene in a participant’s cognition and build an identity as a passenger in public perceptions about emerging technology (Cadario et al., 2021).”
Folk Beliefs of Artificial Intelligence and Robots
Xu, L., Zhang, Y., Yu, F., Ding, X., & Wu, J. (2024) · International Journal of Social Robotics · doi:10.1007/s12369-024-01097-2 · Cited by 13
Beyond Algorithm Aversion in Human-Machine Decision-Making
Burton, J. W., Stein, M., & Jensen, T. B. (2023) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-30085-1_1 · Cited by 11
Clement, J., Ren, Y., & Curley, S. P. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3961156 · Cited by 6
Made with AI: Consumer Engagement with Social Media Containing AI DisclosuresPreprint
Carney, S., Riveros, I., & Tully, S. M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4988760 · Cited by 7
Gong, Y., Tang, X., & Peng, H. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1437915 · Cited by 8
“For example, a study of a skin cancer App found that 57–64% of the descriptions in the App were performance-related, while only 21% were process-related (Cadario et al., 2021).”
InfluentialLiao, G., Wang, F., Zhu, W., & Zhang, Q. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01121-0 · Cited by 10
“The permissibility adopts the scale developed by Bigman and Gray [13], the liking for algorithms employs the scale by Jago [60], and the utilization intention use the scale from Cadario et al. [23].”
BackgroundMethodMoral psychological exploration of the asymmetry effect in AI-assisted euthanasia decisions
Laakasuo, M., Kunnari, A., Francis, K., Košová, M. J., Kopecký, R., Buttazzoni, P., Koverola, M., Palomäki, J., Drosinou, M., & Hannikainen, I. R. (2025) · Cognition · doi:10.1016/j.cognition.2025.106177 · Cited by 6
“…“ algorithm aversion ” and what is the mechanistic logic behind it and why it does not creep up in every decision and in every situation systematically. by better educating people about how algorithms work in these contexts and thus creating subjective understanding of them (Cadario et al., 2021).”
Augmented Learning for Joint Creativity in Human-GenAI Co-Creation
Luan, Y. L., Kim, Y. J., & Zhou, J. (2025) · Information Systems Research · doi:10.1287/isre.2024.0984 · Cited by 12
Nair, G., Vedula, A., Johnson, E. T., Thomas, J., Patel, R., Cheng, J., & Vedula, R. (2024) · Endocrine Practice · doi:10.1016/j.eprac.2024.08.001 · Cited by 6
He, X., Xia, Q., & Feng, W. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104397 · Cited by 6
Wang, B. C., Chen, M. H., Liu, B. J., Li, B. C., Jiao, B. W., Guo, B. Q., & Zhang, P. Q. (2023) · Advanced ultrasound in diagnosis and therapy · doi:10.37015/audt.2023.230012 · Cited by 7
Transforming Human Resources With AI
Massoud, M. F., Maaliky, B., Fawal, A. E., Mawlawi, A. H., & Yahkni, F. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1046-5.ch011 · Cited by 6
Understanding Consumer Preferences for Explanations Generated by XAI AlgorithmsPreprint
Ramon, Y., Vermeire, T., Toubia, O., Martens, D., & Evgeniou, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02624 · Cited by 6
“Prior work indicates that people tend to be less averse to recommender systems when they are provided with explanations about how they work [11, 87], and that in advertising, people tend to accept personalized advertisements more easily when they understand why they are seeing an ad [26].”
BackgroundDong, M., Conway, J. R., Bonnefon, J., Shariff, A., & Rahwan, I. (2023) · doi:10.31234/osf.io/pjvqt · Cited by 5
The Challenges of Deploying an Algorithmic Pricing Tool: Evidence from AirbnbPreprint
Foroughifar, M., & Mehta, N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4857320 · Cited by 6
The Challenges of Deploying an Algorithmic Pricing Tool: Evidence from AirbnbPreprint
Foroughifar, M., & Mehta, N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4891482 · Cited by 6
Abdelaal, Y., Aupetit, M. J. M., Baggag, A., Bashir, M., & Al‐Thani, D. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2400388 · Cited by 6
“Trust in AI applications within healthcare faces significant challenges due to its inherent complexity, where understanding how algorithms learn and produce outputs is inherently difficult (Cadario et al., 2021; Gille et al., 2020).”
Howe, S., Gregoor, A. M. S., Groot, C. A. U., Wakkee, M., Nijsten, T. E. C., & Wehrens, R. (2024) · Digital Health · doi:10.1177/20552076241292390 · Cited by 7
Madanay, F. L., O’Donohue, L. S., & Zikmund‐Fisher, B. J. (2025) · Journal of Medical Internet Research · doi:10.2196/68823 · Cited by 7
“Whereas previous research has focused on patients’trust or mistrust of medical AI [7,8,10], few studies have examined patients’ trust in the clinician of the human-AI team.”
Jin, X., & Li, J. (2025) · International Journal of Consumer Studies · doi:10.1111/ijcs.70133 · Cited by 9
Zheng, X., Li, J., Yue, X., & Li, Y. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104572 · Cited by 6
Du, G., Zhou, C., & Cheng, X. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115959 · Cited by 7
Dimensions of AI de-anthropomorphization based on impression integration theory
Li, J., Huang, J., & Ni, B. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103352 · Cited by 6
Perfalk, E., Bernstorff, M., Danielsen, A. A., & Østergaard, S. D. (2024) · European Psychiatry · doi:10.1192/j.eurpsy.2024.1790 · Cited by 11
“Consistent with our study, it was found that a small amount of information about ML algorithms reduced “ algo-rithm aversion ” , i.e., reduced the reluctance to utilize algorithmic support compared to human providers [8].”
InfluentialSirokha, D., Волинець, В. В., КОВАЛЕНКО, Р. І., Pohorielova, O., & Tkachuk, A. O. (2024) · Przegląd Prawniczy Uniwersytetu im Adama Mickiewicza · doi:10.14746/ppuam.2024.16.03 · Cited by 8
A three-dimensional motivation model of algorithm aversion
ZAHNG, Y., Xu, L., Yu, F., Xiaojun, D. I. N. G., Wu, J., Zhao, L., 邬, 家., & 赵, 靓. (2022) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2022.01093 · Cited by 6
Meyers, E. A., Gretton, J. D., Budge, J. R. C., Fugelsang, J. A., & Koehler, D. J. (2023) · Judgment and Decision Making · doi:10.1017/jdm.2023.24 · Cited by 8
“Despite not definitively understanding how it works mechanistically, researchers continue to examine the consequences of exposing an illusion of explanatory depth (e.g., Cadario et al., 2021; Crawford & Ruscio, 2021; Littrell et al., 2022; Meyers et al., 2020; Sloman & Vives, 2022).”
BOHM, R. M., Jörling, M., Reiter, L., et al. (2023) · doi:10.31234/osf.io/swfn6 · Cited by 5
Artificial intelligence and health equity in primary care: A qualitative study with key stakeholders
d’Elia, A., Gabbay, M., Frith, L., Rodgers, S., & Kierans, C. (2023) · doi:10.1101/2023.10.25.23297533 · Cited by 5
“In the context of medical AI, previous research has shown a link between PPI to improve public understanding of AI and acceptability of a hypothetical AI product, predicting that this would lead to more successful implementation 36 .”
BackgroundBeyond algorithm aversion: The impact of psychological readiness on algorithmic advice
Kim, H. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108824 · Cited by 7
Sreejesh, S., & Singha, S. D. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104660 · Cited by 5
Engaging the unengaged: Differential effects of AI-driven climate communication across audiences
Plechatá, A., Makransky, G., & BOHM, R. M. (2025) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2025.102892 · Cited by 5
Lay beliefs about AI assessment of interpersonal skills in personnel selection
Cheong, I., Huh, Y. E., & Puntoni, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-10358-7 · Cited by 8
“Similarly, providing explanations about how AI works (e.g., improving understanding of AI’s healthcare diagnoses) could enhance understanding and, consequently, willingness to adopt AI applications 10,35 .”
Predicting the future with humans and AI
Mellers, B. A., Lu, L., & McCoy, J. P. (2022) · Consumer Psychology Review · doi:10.1002/arcp.1089 · Cited by 6
News from Artificial Intelligence is Believed LessPreprint
Longoni, C., Fradkin, A., Cian, L., & Pennycook, G. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3787064 · Cited by 6
Self-citationSocial Preferences Towards Machines and HumansPreprint
von Schenk, A., Klockmann, V., & Köbis, N. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4145868 · Cited by 4
McRae, C., Anderson, M. L., Turner, L. M., Savage, A., Mohammad, S., Cowan, R., & Graham, L. V. (2025) · Skin Health and Disease · doi:10.1093/skinhd/vzaf086 · Cited by 4
“9 Longoni et al . identified ‘uniqueness neglect’ as an important driver of medical AI resistance, where patients worry that AI cannot adequately account for their individual characteristics and circumstances; however, in this study, this resistance was eliminated when AI was positioned as…”
Reduced symptom reporting quality during human–chatbot versus human–physician interactions
Reis, M., Reis, F., Kim, Y. J., Demir, A., Lim, J., Gröschel, M. I., Boie, S. D., & Kunde, W. (2026) · Nature Health · doi:10.1038/s44360-026-00116-y · Cited by 4
“Potential explanations could be the so-called uniqueness neglect (the belief that AI cannot take the unique characteristics of the user into account) 10 as well as a perceived lack of transparency regarding AI’s decision-making processes 11 .”
Çelebi, Ö. F., Kabadayı, E. T., Aksoy, N. C., & Alan, A. K. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2544000 · Cited by 4
“…context of AI in healthcare, individuals resist it mainly due to financial issues (Chaibi & Zaiem, 2022), low diagnostic accuracy, privacy concerns, legal responsibilities (Yang et al., 2024), difficulties understanding algorithms (Cadario et al., 2021), and autonomy (Williamson & Prybutok, 2024).”
Unveiling the Mind of the MachinePreprint
Clegg, M., Hofstetter, R., de Bellis, E., et al. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4564832 · Cited by 3
Discretion Over Information: Evidence from pretrial decisionsPreprint
Davenport, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4580390 · Cited by 3
van Alphen, A. M. I. A., Sülz, S., Lingsma, H. F., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0294026 · Cited by 2
Rolison, J. J., Gooding, P., Russo, R., & Buchanan, K. E. (2024) · PLoS ONE · doi:10.1371/journal.pone.0292944 · Cited by 3
“In general, people lack insight into how medical decisions are made by algorithms, perceiving them as a ‘black box’ [57].”
BackgroundImproving Advisor Systems: Algorithms that Explain Themselves
Neumann, M., & Kuncel, N. R. (2025) · Journal of Business and Psychology · doi:10.1007/s10869-025-10014-x · Cited by 2
“…design and development (Glikson & Woolley, 2020; Langer & König, 2023), although previous research showed somewhat mixed findings that being transparent about the algorithm’s design increases trust and use of algorithmic advice (Cadario et al., 2021; Dzindolet et al., 2003; Lehmann et al., 2022).”
On preferring people to algorithms
Kaiser, M., Sunstein, C. R., & Reisch, L. A. (2025) · Journal of Risk and Uncertainty · doi:10.1007/s11166-025-09466-6 · Cited by 4
“Many people do not know how algorithms work, and their lack of knowledge on that count breeds distrust (Cadario et al., 2021).”
Alsaad, A. K., Alanezi, S., & Melhim, L. K. B. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2589498 · Cited by 3
“Algorithmic aversion can occur even when AI performs more effectively (Cadario et al., 2021; Dietvorst et al., 2015; Longoni et al., 2019).”
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Transparency and Trust in Simple Algorithmic Hiring Procedures
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“1, Setzen et al., 2023) and 2) the presence of well-established phenomena, such as AI-or algorithm-aversion (e.g., Cadario et al., 2021; Dang & Liu, 2024; Dietvorst et al., 2015), leading humans to avoid and distrust AI-related products or outputs.”
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Ideological plurality and misinformation dynamics in Indonesia’s digital islamic sphere
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Metadata
- Authors
- Cadario, R., Longoni, C., & Morewedge, C. K.
- Affiliations
- Erasmus University Rotterdam (NL)
- Boston University (US)
- Corresponding author
- Cadario, Romain
- Venue
- Nature Human Behaviour
- Published
- 2021-06-28
- Volume
- 5
- Issue
- 12
- Pages
- 1636-1642
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W3095202475
- Semantic Scholar
- 2dbd832cce2b900341e0634995e5a093c23ccb0b
- Cited by
- 342
- References
- 48
- Citations by year
- 2026: 74 · 2025: 93 · 2024: 87 · 2023: 54 · 2022: 23 · 2021: 11
- FWCI
- 15.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion, public-domain)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (acceptedVersion, public-domain)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Archivio istituzionale della Ricerca - Bocconi (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- Erasmus Research Institute of Management
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- artificial intelligence in healthcare, algorithmic decision-making, trust in AI, resistance to AI adoption, algorithmic transparency, brief intervention, skin cancer detection, Google AdWords
- MeSH terms
- Clinical Decision-Making, Adult, Algorithms, Artificial Intelligence, Delivery of Health Care, Female, Humans, Male, Middle Aged
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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