I can do better than your AI

Schaffer, James; O’Donovan, John; Michaelis, James; Raglin, Adrienne; Höllerer, Tobias

doi:10.1145/3301275.3302308

Cited by 71 publications

(24 citation statements)

References 36 publications

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“…As we discussed, calibrating trust for individual predictions is especially important in AI-assisted decision making scenarios. We note several recent studies employed similar AI-assisted decisionmaking setups and studied how various model related information impacts trust and decision outcome [17,27,29,30,32]. Multiple studies examined the effect of accuracy information [17,30,32], and found people to increase their trust in the model when high accuracy indicators are displayed, reflected both in subjective reporting and more consistent choices with the model's recommendations.…”

Section: Related Workmentioning

confidence: 99%

“…Another challenge is their inherent uncertainty, since a ML system can make mistakes in its prediction based on learned patterns, and such uncertainty often cannot be fully captured before deployment using testing methods. While many emphasized the requisite of transparency for trusting AI [9,28], several recent empirical studies found little evidence that the level of transparency has significant impact on people's willingness to trust a ML system, whether by using a directly interpretable model, allowing user to inspect the model behavior, showing explanation or reducing the number of features presented [5,16,27,29]. Many reasons could have contributed to this lack of effect.…”

Section: Related Workmentioning

confidence: 99%

“…According to theories of trust [6,12,15], trusting behaviors such as adopting suggestions are not only driven by a more positive perception of the trustee but also other factors such as one's disposition to trust and situation awareness. In fact, several studies suggest that overloading users with information about the system could potentially harm people's situation awareness and lead to worse performance or decision-making outcome [6,27,29].…”

Section: Related Workmentioning

confidence: 99%

“…Trust. Prior work suggests that subjective self-reported trust may not be a reliable indicator for trusting behaviors [16,29], which are what ultimately matter in AI-assisted decision tasks. Therefore, following recent studies [17,27,30], we measured participants' trust in the AI by two behavioral indicators:…”

Section: 21mentioning

confidence: 99%

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Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making

Zhang¹,

Liao²,

Bellamy³

2020

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

465

326

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Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios. In these scenarios, full automation is often undesirable, not only due to the significance of the outcome, but also because human experts can draw on their domain knowledge complementary to the model's to ensure task success. We refer to these scenarios as AI-assisted decision making, where the individual strengths of the human and the AI come together to optimize the joint decision outcome. A key to their success is to appropriately calibrate human trust in the AI on a case-by-case basis; knowing when to trust or distrust the AI allows the human expert to appropriately apply their knowledge, improving decision outcomes in cases where the model is likely to perform poorly. This research conducts a case study of AI-assisted decision making in which humans and AI have comparable performance alone, and explores whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. Specifically, we study the effect of showing confidence score and local explanation for a particular prediction. Through two human experiments, we show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making, which may also depend on whether the human can bring in enough unique knowledge to complement the AI's errors. We also highlight the problems in using local explanation for AI-assisted decision making scenarios and invite the research community to explore new approaches to explainability for calibrating human trust in AI.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: 21mentioning

confidence: 99%

See 2 more Smart Citations

Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making

Zhang¹,

Liao²,

Bellamy³

2020

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

465

326

View full text Add to dashboard Cite

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“…People rate their behaviour as better than the norm, though it is mathematically impossible for most people to have better-than-median abilities. 44 The BTAE exists across humans [44][45][46][47] and health professionals are no exception.…”

Section: Discussionmentioning

confidence: 99%

Conformity, obedience, and the Better than Average Effect in health professional students

2021

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Background: Compliance, through conformity and obedience to authority, can produce negative outcomes for patient safety, as well as education. To date, educational interventions for dealing with situations of compliance or positive deviance have shown variable results. Part of the challenge for education on compliance may result from disparities between learners' expectations about their potential for engaging in positive deviance and the actual likelihood of engaging in positive deviance. More specifically, students may demonstrate a Better Than Average Effect (BTAE), the tendency for people to believe they are comparatively better than the average across a wide range of behaviours and skills. Methods: Four vignettes were designed and piloted using cognitive interviews, to investigate the BTAE. Conformity and obedience to authority were each addressed with two vignettes. The vignettes were included in a survey distributed to Canadian health professional students across multiple programs at several different institutions during the Winter 2019 semester. Self-evaluation of behaviour was investigated using a one-sample proportion test. Demographic data were investigated using logistic regression to identify predictors of the BTAE. Results: Participants demonstrated the BTAE for expected behaviour compared to peers for situations of conformity and obedience to authority. Age, sex, and program year were identified as potential predictors for exhibiting the BTAE. Conclusions: This study demonstrated that health professional students expect that they will behave better than average in compliance scenarios. Health professional students are not exempt from this cognitive bias in self-assessment. The results have implications for education on compliance, positive deviance, and patient safety.

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Sonographer interaction with artificial intelligence: collaboration or conflict?

Matthew

Budd

Hajnal

et al. 2023

Ultrasound in Obstet & Gyne

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I can do better than your AI

Cited by 71 publications

References 36 publications

Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making

Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making

Conformity, obedience, and the Better than Average Effect in health professional students

Sonographer interaction with artificial intelligence: collaboration or conflict?

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