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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Dermatologist-level classification of skin cancer with deep neural networks
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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 .”
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Cited by
Sorted by citations
Artificial intelligence and illusions of understanding in scientific research
Messeri, L., & Crockett, M. J. (2024) · Nature · doi:10.1038/s41586-024-07146-0 · Cited by 707
“…to trust AI for tasks that share key features with scientific research: tasks that can be described as objective (as opposed to subjective) 85,86 , that require deliberation (as opposed to intuition) 87 , where human biases are undesirable 88,89 and under conditions of high workloads 90 and…”
The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., et al. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae191 · Cited by 371
“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).”
Self-citationFahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025) · European Journal of Medical Research · doi:10.1186/s40001-025-03196-w · Cited by 329
“Cadario et al. reported that insufficient understanding of AI algorithms and blurred decision-making roles drive resistance, recommending targeted education to strengthen engagement [178].”
Mannuru, N. R., Shahriar, S., Teel, Z. A., Wang, T., Lund, B. D., Tijani, S., Pohboon, C. O., Agbaji, D. A., Alhassan, J., Galley, J., et al. (2023) · Information Development · doi:10.1177/02666669231200628 · Cited by 426
“Patient reluctance or hesitancy toward AI-driven treatment is another challenge, particularly in developing countries, where technology innovations may be slower to gain acceptance (Cadario et al., 2021; Ghosh, 2022; Longoni et al., 2019; Shandlen, 2012).”
Artificial intelligence and consumer behavior: From predictive to generative AI
Hermann, E., & Puntoni, S. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114720 · Cited by 316
Psychological factors underlying attitudes toward AI tools
Freitas, J. D., Agarwal, S., Schmitt, B. H., & Haslam, N. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01734-2 · Cited by 271
“The researchers found that participants engaged more with the advertisement that provided an explanation, suggesting that people are more likely to adopt AI tools that they understand 23 .”
BackgroundInfluentialThumbs Up or Down: Consumer Reactions to Decisions by Algorithms Versus Humans
Williams, G. Y., Lim, S., Puntoni, S., & van Osselaer, S. M. J. (2021) · Journal of Marketing Research · doi:10.1177/00222437211070016 · Cited by 216
“For example, consumers are often reluctant to use algorithms to predict stock prices (Onkal et al. 2009), solicit medical advice (Cadario, Longoni, and Morewedge 2021; Longoni, Bonezzi, and Morewedge 2019; Promberger and Baron 2006), and predict people ’ s performance (Dietvorst, Simmons, and…”
BackgroundKoçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168
“46 This bias can manifest due to several reasons, such as trust issues on algorithms’ reliability, transparency, or inter-pretability or a lack of familiarity, fear of job displacement, or even ethical and legal concerns.”
The Moral Psychology of Artificial Intelligence
Bonnefon, J.-F., Rahwan, I., & Shariff, A. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-030123-113559 · Cited by 151
Wang, C., Li, Y., Fu, W., & Jin, J. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103325 · Cited by 126
BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions
Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125
Gordon, A. C., Alipanah, N., Bos, L. D. J., Dianti, J., Diaz, J. V., Finfer, S. R., Fujii, T., Giamarellos‐Bourboulis, E. J., Goligher, E. C., Gong, M. N., et al. (2024) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202311-2086so · Cited by 130
“Fifth,machine learning or AI models used to identify subphenotypes are sometimes viewed as black boxes because of internal mechanisms that are dif fi cult to understand (99).”
BackgroundAlgorithmic Transference: People Overgeneralize Failures of AI in the Government
Longoni, C., Cian, L., & Kyung, E. J. (2023) · Journal of Marketing Research · doi:10.1177/00222437221110139 · Cited by 121
“Ourresearch makes a second theoretical contribution to the broader literature on consumer responses to AI systems (Cadario, Longoni, and Morewedge 2021; Castelo, Bos, and Lehmann 2019; Gill 2020; Longoni, Bonezzi, and Morewedge 2019, 2020).”
BackgroundMethodSelf-citationResearch on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence
Gou, F., Liu, J., Xiao, C., & Wu, J. (2024) · Diagnostics · doi:10.3390/diagnostics14141472 · Cited by 129
Xie, Z., Yu, Y., Zhang, J., & Chen, M. (2022) · Psychology and Marketing · doi:10.1002/mar.21706 · Cited by 125
News from Generative Artificial Intelligence Is Believed Less
Longoni, C., Fradkin, A., Cian, L., & Pennycook, G. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533077 · Cited by 161
Self-citationExplainable AI improves task performance in human–AI collaboration
Senoner, J., Schallmoser, S., Kratzwald, B., Feuerriegel, S., & Netland, T. H. (2024) · Scientific Reports · doi:10.1038/s41598-024-82501-9 · Cited by 157
Wang, J., Zheng, Y., Ma, J., Li, X., Wang, C., Gee, J. C., Wang, H., & Huang, W. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102687 · Cited by 105
Deploying artificial intelligence in services to AID vulnerable consumers
Hermann, E., Williams, G. Y., & Puntoni, S. (2023) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-023-00986-8 · Cited by 123
“The opacity and black-box nature of AI (e.g., Barredo Arri-eta et al., 2020; Cadario et al., 2021; Rai, 2020) present challenges in understanding how AI models function and their underlying data processing algorithms (i.e., intelligibility ; Floridi et al., 2018).”
Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence
Nußberger, A.-M., Luo, L., Celis, L. E., & Crockett, M. J. (2022) · Nature Communications · doi:10.1038/s41467-022-33417-3 · Cited by 105
“…fi ndingsdescribed in the present workdependon stakeholder perspectives (e.g., policy-makers versus users) and how they evolve over time, to explore how attitudes towards interpretability translate into manifest choices, and how they relate to attitudes towards explanations of human decisions 46 .”
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“Consequently, increasing an algorithm's transparency has been shown to increase its acceptance, provided this information is comprehensible (Cadario et al., 2021; Castelo et al., 2019; Glikson & Woolley, 2020).”
BackgroundMethodInfluentialChallenges of human—machine collaboration in risky decision-making
Xiong, W., Fan, H., Ma, L., & Wang, C. (2022) · Frontiers of Engineering Management · doi:10.1007/s42524-021-0182-0 · Cited by 95
“, 2020), explainability (Gunning, 2016; Degani et al., 2017; DARPA, 2018; Amann et al., 2020; Cadario et al., 2021), task allocation (van Maanen and van Dongen, 2005; Roth et al.”
BackgroundInfluentialPreference for human, not algorithm aversion
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“2021; Timan and Mann 2021) AI3 Reluctance to adopt new technologies (Lins et al. 2021; Cadario et al. 2021; Dwivedi et al. 2021; Suseno et al. 2022) AI4 Risk and cost associated with construction projects (Regona et al.”
Humans as teammates: The signal of human–AI teaming enhances consumer acceptance of chatbots
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Review of applications of deep learning in veterinary diagnostics and animal health
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Unveiling the Mind of the Machine
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Thinking about God increases acceptance of artificial intelligence in decision-making
Karataş, M., & Cutright, K. M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218961120 · Cited by 55
“As most AI systems operate as a “black box” (52, 53) and consumers are not fully certain how AI systems make decisions, it is possible that consumers perceive AI decisions as being similar to the decision-making of God, which is also unknown to them, and that this perceived similarity heightens the…”
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Physician Adoption of AI Assistant
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How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program
Choi, S., Kang, H., Kim, N., & Kim, J. (2021) · Strategic Management Journal · doi:10.1002/smj.3694 · Cited by 95
“Professionals thus perceive AI-powered tools as generally riskier to adopt or utilize when making important decisions, with a tradeoff between performance and risk (Cadario et al., 2021; Lebovitz et al., 2021).”
How AI can learn from the law: putting humans in the loop only on appeal
Cohen, I. G., Babic, B., Gerke, S., Xia, Q., Evgeniou, T., & Wertenbroch, K. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00906-8 · Cited by 51
BOHM, R. M., Jörling, M., Reiter, L., & Fuchs, C. (2023) · Communications Psychology · doi:10.1038/s44271-023-00032-x · Cited by 67
“Studies have demonstrated that individuals are (more) averse to following AI/algorithmic support in moral decision contexts 12,13 , in medical decision contexts 14,15 , and in hedonic contexts 16 .”
Consumers and service robots: Power relationships amid COVID-19 pandemic
Merdin‐Uygur, E., & Öztürkcan, S. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103174 · Cited by 48
Frank, D., & Otterbring, T. (2024) · British Journal of Management · doi:10.1111/1467-8551.12780 · Cited by 49
Querci, I., Barbarossa, C., Romani, S., & Ricotta, F. (2022) · Psychology & Marketing · doi:10.1002/mar.21705 · Cited by 42
Algorithm Aversion: Evidence from Ridesharing Drivers
Liu, M., Tang, X., Xia, S., Zhang, S., Zhu, Y., & Meng, Q. (2023) · Management Science · doi:10.1287/mnsc.2022.02475 · Cited by 64
A Naturalistic Investigation of Trust, AI, and Intelligence Work
Dorton, S. L., & Harper, S. B. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221103718 · Cited by 55
“This is evidenced by the calls for XAI in numerous high-stakes fi elds such as medicine, autonomous driving systems, and air traf fi c control (Cadario, et al., 2021; Lorente, et al., 2021; Xie, et al., 2021).”
Qin, H., Zhu, Y., Jiang, Y., Luo, S., & Huang, C. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102726 · Cited by 55
He, X., Hong, Y., Xi, Z., & Zhang, Y. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2095093 · Cited by 49
“Algorithmic aversion in healthcare utilization, i.e., patients are more reluctant to exploit healthcare provided by AI compared to similar human providers, is a phenomenon that has been observed by predecessors (Cadario et al., 2021; Saragih & Morrison, 2021; Yokoi et al., 2021).”
BackgroundSmadi, B. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107921 · Cited by 50
The role of recommendation sources and attribute framing in online product recommendations
Yang, Y., Zheng, J.-H., Yu, Y.-N., Qiu, Y., & Wang, L. (2024) · Journal of business research · doi:10.1016/j.jbusres.2024.114498 · Cited by 36
Transparency of medical artificial intelligence systems
Kim, C., Gadgil, S. U., & Lee, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00363-w · Cited by 60
Human-AI cooperation: Modes and their effects on attitudes
Li, J., Huang, J., Liu, J., & Zheng, T. (2022) · Telematics and Informatics · doi:10.1016/j.tele.2022.101862 · Cited by 47
Schlund, R., & Zitek, E. M. (2024) · Communications Psychology · doi:10.1038/s44271-024-00102-8 · Cited by 52
“” 24 That is, people tend to believe they understand the processes behind human decision-making (e.g., how and why other humans make certain decisions) to a greater extent than they actually do.”
BackgroundAcceptability lies in the eye of the beholder: Self-other biases in GenAI collaborations
Celiktutan, B., Klesse, A.-K., & Tuk, M. A. (2024) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2024.05.006 · Cited by 41
Schweitzer, S., & Cremer, D. D. (2023) · Academy of Management Discoveries · doi:10.5465/amd.2022.0115 · Cited by 45
Perceived corruption reduces algorithm aversion
Castelo, N. (2023) · Journal of Consumer Psychology · doi:10.1002/jcpy.1373 · Cited by 38
Zehnle, M., Hildebrand, C., & Valenzuela, A. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2025.02.005 · Cited by 51
Kumar, N., Patel, M., Nguyen, T. T., Bhatnagar, P., & Kim, J. (2021) · Materials Today Chemistry · doi:10.1016/j.mtchem.2021.100681 · Cited by 33
The Roles of Social Perception and AI Anxiety in Individuals’ Attitudes Toward ChatGPT in Education
Wang, C., Li, X., Liang, Z., Sheng, Y., Zhao, Q., & Chen, S. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2365453 · Cited by 38
“First, the objective characteristics of AI (Cadario et al., 2021; Castelo et al., 2019; Koo et al., 2015; Mori et al., 2012).”
Pan, S., Qin, Z., & Zhang, Y. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19040157 · Cited by 42
“A large number of studies on prediction [42], medical diagnosis [63,64], and chat-bots [65] have shown that algorithmic aversion reflects various types of biases harbored by consumers toward algorithm-based AI products [66].”
Factors influencing trust in algorithmic decision-making: an indirect scenario-based experiment
Marmolejo‐Ramos, F., Marrone, R. L., Korolkiewicz, M. W., Gabriel, F. C., Siemens, G., Joksimovíc, S., Yamada, Y., Mori, Y., Rahwan, T., Sahakyan, M., et al. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1465605 · Cited by 45
Student (Mis)Use of Generative AI Tools for University-Related Tasks
Reiter, L., Jörling, M., Fuchs, C., Working group ‘Artificial Intelligence in Higher Education’, & Böhm, R. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2462083 · Cited by 53
“Developers of AI tools should consider offering explanations for how the outputs are generated (explainable AI; Cadario et al., 2021).”
von Schenk, A., Klockmann, V., & Köbis, N. (2025) · Perspectives on Psychological Science · doi:10.1177/17456916231194949 · Cited by 30
Frank, D., Elbæk, C. T., Børsting, C. K., Mitkidis, P., Otterbring, T., & Borau, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259928 · Cited by 36
“While the prospects of medical AI to fundamentally improve access to and delivery of health services during the pandemic are promising, research on AI adoption also highlights challenges and barriers that could prevent widespread adoption [11, 12].”
BackgroundHow Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program
Choi, S., Kim, N., Kim, J., & Kang, H. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3893835 · Cited by 30
“Although the domains where AI outperforms humans have broadened to include different organizations such as hospitals (Cadario et al. 2021), law firms (Kahn 2020) and sports teams (Zarley 2021), 21 the application of our findings to different contexts requires careful consideration.”
BackgroundEnhancing human-AI collaboration: The case of colonoscopy
Introzzi, L., Zonca, J., Cabitza, F., Cherubini, P., & Reverberi, C. (2024) · Digestive and Liver Disease · doi:10.1016/j.dld.2023.10.018 · Cited by 28
Lin, C. S., Tsai, D., Wang, C., Chao, Y., Huang, J.-W., Lin, C.-S., & Fang, W. (2024) · Radiology · doi:10.1148/radiol.231937 · Cited by 28
Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies
Zhang, X., Zhang, W., Zhang, H., & Liao, X. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1546474 · Cited by 30
Leveraging AI for Content Generation: A Customer Equity Perspective
Schweidel, D. A., Reisenbichler, M., Reutterer, T., & Zhang, K. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1548-643520230000020006 · Cited by 31
Eye tracking insights into physician behaviour with safe and unsafe explainable AI recommendations
Nagendran, M., Festor, P., Komorowski, M., Gordon, A. C., & Faisal, A. A. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01200-x · Cited by 33
Kareemi, H., Yadav, K., Price, C. M., Bobrovitz, N., Meehan, A., Li, H., Goel, G., Masood, S., Grant, L., Ben‐Yakov, M., et al. (2025) · Academic Emergency Medicine · doi:10.1111/acem.15099 · Cited by 28
“12,13 A reporting pathway for AI-CDS tools has recently been proposed by the authors of the DECIDE-AI guidelines, which mirrors those of pharmaceutical and surgical innovations.”
Chen, C., & Cui, Z. (2025) · Journal of Medical Internet Research · doi:10.2196/66083 · Cited by 38
“First, we extended previous literature on medical AI aversion [4,39,40] and found that aversion widely persists even if AI is used as an assisting tool rather than a sole decision maker in the era of generative AI.”
Acceptance of Medical Treatment Regimens Provided by AI vs. Human
Wu, J., Xu, L., Yu, F., & Peng, K. (2021) · Applied Sciences · doi:10.3390/app12010110 · Cited by 27
Artificial intelligence in peritoneal dialysis: general overview
Bai, Q., & Tang, W. (2022) · Renal Failure · doi:10.1080/0886022x.2022.2064304 · Cited by 27
“Thus, people have difficulty of understanding it, which make people reluctant to use it [38].”
BackgroundCai, Z., He, H., Huo, W., & Xu, X. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2291616 · Cited by 26
“Most studies have found that the public can be reluctant to accept medical AI, especially when they have a choice between a human medical provider and AI (Cadario et al., 2021; Esmaeilzadeh et al., 2021; Frank et al., 2021; Longoni et al., 2019; Naneva et al., 2020; Yokoi et al., 2021).”
The 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. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/6fd2y · Cited by 47
Self-citationLonely human and dominant robot: Similarity versus complementary attraction
Yu, L., & Fan, X. (2024) · Psychology and Marketing · doi:10.1002/mar.21975 · Cited by 26
After opening the black box: Meta-dehumanization matters in algorithm recommendation aversion
Chen, G., Dang, J., & Liu, L. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108411 · Cited by 23
Hai, L., Tang, X., Ye, Y., & Huiqin, Z. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02600-w · Cited by 25
Khare, S. K., Blanes‐Vidal, V., Booth, B. B., Petersen, L. K., & Nadimi, E. S. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1550 · Cited by 25
“Therefore, clinical experts and stakeholders may show resistance and lack of trust in the outcome yielded by the current AI-assisted decision support system due to a lack of transparency (Cadario et al., 2021; Longoni et al., 2019).”
Reducing prejudice with counter‐stereotypical AI
Hermann, E., De Freitas, J., & Puntoni, S. (2025) · Consumer Psychology Review · doi:10.1002/arcp.1102 · Cited by 21
Yang, H., Wang, J., Wang, W., Shi, S., Liu, L., Yao, Y., Tian, G., Wang, P., & Yang, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf209 · Cited by 25
“These models often output only risk predictions without showing how decisions are made,leading experts to question their reliance on relevant prognostic factors [27].”
People see more of their biases in algorithms
Celiktutan, B., Cadario, R., & Morewedge, C. K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317602121 · Cited by 21
Self-citationBalakrishnan, M., Ferreira, K., & Tong, J. D. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4298669 · Cited by 21
“Commonly, algorithm transparency is provided in an attempt to increase end-user trust, which can be subjectively measured (e.g., Likert scale as in Cadario et al. 2021) or observed (e.g., algorithm use rate as in Yin et al. 2019).”
MethodAcceptance of Medical Artificial Intelligence in Skin Cancer Screening: Choice-Based Conjoint Survey
Jagemann, I., Wensing, O., Stegemann, M., & Hirschfeld, G. H. F. (2023) · JMIR Formative Research · doi:10.2196/46402 · Cited by 24
“Research into the technical quality of AI-based skin cancer screening technologies has shown that these systems achieve detection rates that are on par or better than those of highly trained clinicians [9-13].”
BackgroundSmoła, P., Młoźniak, I., Wojcieszko, M., Zwierczyk, U., Kobryn, M., Rzepecka, E., & Duplaga, M. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1458685 · Cited by 32
The Effect of Medical Artificial Intelligence Innovation Locus on Consumer Adoption of New Products
Huang, X., Wu, X., Cao, X., & Wu, J. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122902 · Cited by 22
Yue, Z., Tuk, M. A., & Klesse, A.-K. (2024) · Journal of the Association for Consumer Research · doi:10.1086/730710 · Cited by 22
“Spe-ci fi cally, whereas people often construe algorithms as a black box and believe that they cannot understand how algorithms work, people hold the illusion that they can better understand the decision-making processes of humans (Cadario et al. 2021; Bonezzi et al. 2022).”
BackgroundMethodResultInfluentialTo err is human: Bias salience can help overcome resistance to medical AI
Isaac, M. S., Wang, R. J.-H., Napper, L. E., & Marsh, J. K. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108402 · Cited by 22
AI in medical diagnosis: A contextualised study of patient motivations and concerns
Hajiheydari, N., Delgosha, M. S., & Saheb, T. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.117850 · Cited by 21
“Does Black Box AI In Medicine Compromise Informed Consent?”
Director, S. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00860-1 · Cited by 25
“…adoption of AI hinges not only on adoption by healthcare systems and providers but also on patient utilization, and patients are reluctant to utilize medical AI” (Cadario, Longoni, and Morewedge 2021, 1636). of their treatment would be entitled to treatment via human doctors or explainable AI.”
Balakrishnan, M., Ferreira, K., & Tong, J. D. (2025) · Management Science · doi:10.1287/mnsc.2022.03850 · Cited by 45
AI assessment changes human behavior
Görgen, J., de Bellis, E., & Klesse, A.-K. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2425439122 · Cited by 23
Algorithm Aversion: Evidence from Ridesharing DriversPreprint
Liu, M., Tang, X., Xia, S., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4194660 · Cited by 18
Averse to what: Consumer aversion to algorithmic labels, but not their outputs?
Mariadassou, S., Klesse, A.-K., & Boegershausen, J. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101839 · Cited by 19
Safety of human-AI cooperative decision-making within intensive care: A physical simulation study
Festor, P., Nagendran, M., Gordon, A. C., Faisal, A. A., & Komorowski, M. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000726 · Cited by 24
“Furthermore, the post-hoc rationalisations of human decision-makers can sometimes be “black-box” in nature, just as AI decisions can be [11].”
AI and omics technologies in biobanking: Applications and challenges for public health
Venturini, P., de Faria, P. L., & Cordeiro, J. V. (2025) · Public Health · doi:10.1016/j.puhe.2025.105726 · Cited by 19
Perceived opacity leads to algorithm aversion in the workplace
Yijun, Z. H. A. O., Xu, L., Yu, F., & Wanglong, J. I. N. (2024) · Acta Psychologica Sinica · doi:10.3724/sp.j.1041.2024.00497 · Cited by 19
Mei, Y., & Duan, Y. (2024) · Applied Sciences · doi:10.3390/app14103994 · Cited by 18
“To overcome this limitation, when dealing with subjective and objective semantic issues [54] in interdisciplinary and cross-domain DIKWP interaction and fusion under uncertainty, Gao et al. proposed a method for the dynamic reconfiguration of service workflows in mobile e-commerce environments…”
BackgroundEberhardt, S. T., Vehlen, A., Schaffrath, J., Schwartz, B., Baur, T., Schiller, D., Hallmen, T., André, E., & Lutz, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-14923-y · Cited by 23
“Unlike machine learning methods that extract technical features (e.g., audio signals or video frames) that can capture subtle cues, but are hard to interpret 47 , LLM rating scales ensure interpretability by using conceptually clear items directly linked to the constructs of interest.”
Hu, C., Gao, C., Li, T., Liu, C., & Peng, Z. (2024) · Postgraduate Medical Journal · doi:10.1093/postmj/qgad144 · Cited by 21
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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