Why Are People’s Decisions Sometimes Worse with Computer Support?

Alberdi, Eugenio; Strigini, Lorenzo; Povyakalo, Andrey; Ayton, Peter

doi:10.1007/978-3-642-04468-7_3

Cited by 18 publications

(25 citation statements)

References 36 publications

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“…Because the data were sparse (26) the distributions of model deviances are not Chisquared and the standard analysis of deviance is not applicable. Therefore, we applied the le Cessie-van Houwelingen global test (27) to the null hypothesis of no difference between the observed responses and those estimated by the regression models [1] and [2]. For all sub-populations of cancers from rows and columns of Table 1, the le Cessie-van Houwelingen global test did not reject the null hypothesis for either of these two regression models, corroborating the validity of estimates obtained by these analyses.…”

Section: "Impact" Of Cad: Logistic Regression Estimatesmentioning

confidence: 61%

How to Discriminate between Computer-Aided and Computer-Hindered Decisions

et al. 2013

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Background. Computer aids can affect decisions in complex ways, potentially even making them worse; common assessment methods may miss these effects. We developed a method for estimating the quality of decisions, as well as how computer aids affect it, and applied it to computer-aided detection (CAD) of cancer, reanalyzing data from a published study where 50 professionals (“readers”) interpreted 180 mammograms, both with and without computer support. Method. We used stepwise regression to estimate how CAD affected the probability of a reader making a correct screening decision on a patient with cancer (sensitivity), thereby taking into account the effects of the difficulty of the cancer (proportion of readers who missed it) and the reader’s discriminating ability (Youden’s determinant). Using regression estimates, we obtained thresholds for classifying a posteriori the cases (by difficulty) and the readers (by discriminating ability). Results. Use of CAD was associated with a 0.016 increase in sensitivity (95% confidence interval [CI], 0.003–0.028) for the 44 least discriminating radiologists for 45 relatively easy, mostly CAD-detected cancers. However, for the 6 most discriminating radiologists, with CAD, sensitivity decreased by 0.145 (95% CI, 0.034–0.257) for the 15 relatively difficult cancers. Conclusions. Our exploratory analysis method reveals unexpected effects. It indicates that, despite the original study detecting no significant average effect, CAD helped the less discriminating readers but hindered the more discriminating readers. Such differential effects, although subtle, may be clinically significant and important for improving both computer algorithms and protocols for their use. They should be assessed when evaluating CAD and similar warning systems.

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Section: "Impact" Of Cad: Logistic Regression Estimatesmentioning

confidence: 61%

How to Discriminate between Computer-Aided and Computer-Hindered Decisions

et al. 2013

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“…Thus, users may believe a system to be more reliable than it actually is and misuse it by agreeing with incorrect suggestions, a behavior known as over-reliance [3]. Previous studies have shown that over-reliance is widespread in CDSS use [2,4,14,25,26] and that it can be more pronounced in users with low confidence in their abilities or judgment [18]. This is a major concern when targeting a CDSS at clinicians who do not have specialized knowledge.…”

Section: A System Reliability and Usementioning

confidence: 96%

“…[17]. However, there is still a lack of knowledge about the impact of explanations on building trust and countering overreliance, as some research suggests that explanations lead to more correct decisions [9] whilst others suggest they can also lead to worse decision-making by the clinician [4].…”

Section: Introductionmentioning

confidence: 99%

The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems

Bussone

Stumpf

O’Sullivan

2015

2015 International Conference on Healthcare Informatics

251

209

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Abstract-Clinical decision support systems (CDSS) are increasingly used by healthcare professionals for evidence-based diagnosis and treatment support. However, research has suggested that users often over-rely on system suggestions -even if the suggestions are wrong. Providing explanations could potentially mitigate misplaced trust in the system and overreliance. In this paper, we explore how explanations are related to user trust and reliance, as well as what information users would find helpful to better understand the reliability of a system's decision-making. We investigated these questions through an exploratory user study in which healthcare professionals were observed using a CDSS prototype to diagnose hypothetic cases using fictional patients suffering from a balancerelated disorder. Our results show that the amount of system confidence had only a slight effect on trust and reliance. More importantly, giving a fuller explanation of the facts used in making a diagnosis had a positive effect on trust but also led to over-reliance issues, whereas less detailed explanations made participants question the system's reliability and led to selfreliance problems. To help them in their assessment of the reliability of the system's decisions, study participants wanted better explanations to help them interpret the system's confidence, to verify that the disorder fit the suggestion, to better understand the reasoning chain of the decision model, and to make differential diagnoses. Our work is a first step toward improved CDSS design that better supports clinicians in making correct diagnoses.

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“…177 This, coupled with our natural tendency toward automation bias, leads to the user's overconfidence and overreliance in the manufacturer's ability (or willingness) to ensure the security or reliability of their technologies. 178 Reasonably full disclosure on the part of the manufacturer, whether through regulatory, legislative, or private means, is a key factor in reducing these information gaps.…”

Section: Understand and Address The Technology Information Gapmentioning

confidence: 99%

Cybersecurity and Moral Hazard

Vagle

2017

SSRN Journal

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Our everyday lives are enmeshed, often invisibly, with connected technologies, making the security of those devices and the data they carry increasingly important. Yet our institutions have largely failed to address these technologies' cybersecurity risks. And that is in large part because they have failed to address-and have even exacerbated-the moral hazard inherent in making and selling connected technologies. Currently, technology manufacturers, sellers, and service providers are richly rewarded for innovations that bring security risks, while technology users bear the bulk of the costs associated with those risks, including the nearly inevitable exploitation of their data. Technology manufacturers furthermore are positioned to understand and reduce those risks in ways technology users are not. And so technology manufacturers face a moral hazard: They must decide whether to make (or later fail to support) devices having risks that would be costly or impossible to eliminate for users-when those users will likely pay the same to them regardless. Our institutions' support for technology innovation over product maintenance indulges rather than combats this moral hazard, especially in the low-margin business of connected devices and the Internet of Things (IoT). These failures are also due to our tendency toward technological ubiquity, the unclear-and often unhealthy-relationships between technology manufacturer and user, the inherent complexity of technology, and the network effects inherent to connected technologies.

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Why Are People’s Decisions Sometimes Worse with Computer Support?

Cited by 18 publications

References 36 publications

How to Discriminate between Computer-Aided and Computer-Hindered Decisions

How to Discriminate between Computer-Aided and Computer-Hindered Decisions

The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems

Cybersecurity and Moral Hazard

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