Build confidence and acceptance of AI-based decision support systems - Explainable and liable AI

Nicodème, Claire

doi:10.1109/hsi49210.2020.9142668

Cited by 25 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This mode of presentation is therefore an explainable system versus the typical black-box decision of traditional AI models, which can improve confidence and thus acceptance in AI. 20 The results of this study did not support that the presentation of AI in its current form changed confidence levels. It might be possible that the experience level of the clinician affects confidence in AI despite an explainable system.…”

Section: Discussioncontrasting

confidence: 67%

Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians

et al. 2022

View full text Add to dashboard Cite

Objective Despite artificial intelligence (AI) being used increasingly in healthcare, implementation challenges exist leading to potential biases during the clinical decision process of the practitioner. The interaction of AI with novice clinicians was investigated through an identification task, an important component of diagnosis, in dental radiography. The study evaluated the performance, efficiency, and confidence level of dental students on radiographic identification of furcation involvement (FI), with and without AI assistance. Materials and Methods Twenty-two third- and 19 fourth-year dental students (DS3 and DS4, respectively) completed remotely administered surveys to identify FI lesions on a series of dental radiographs. The control group received radiographs without AI assistance while the test group received the same radiographs and AI-labeled radiographs. Data were appropriately analyzed using the Chi-square, Fischer’s exact, analysis of variance, or Kruskal–Wallis tests. Results Performance between groups with and without AI assistance was not statistically significant except for 1 question where tendency was to err with AI-generated answer (P < .05). The efficiency of task completion and confidence levels was not statistically significant between groups. However, both groups with and without AI assistance believed the use of AI would improve the clinical decision-making. Discussion Dental students detecting FI in radiographs with AI assistance had a tendency towards over-reliance on AI. Conclusion AI input impacts clinical decision-making, which might be particularly exaggerated in novice clinicians. As it is integrated into routine clinical practice, caution must be taken to prevent overreliance on AI-generated information.

show abstract

Section: Discussioncontrasting

confidence: 67%

Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Hence, to achieve better performances from their subjective perspective, participants may attribute less weight in their own responses compared to those given by the machine through a decrease in the confidence associated with their own responses, which may in turn enable them to adapt their post-decisional strategies based on their confidence evaluations. In line with this notion, recent studies showed that machines inspire overconfidence (Booth, 2017; 2020) or mistrust (Nicodeme, 2020; Lee & Rich, 2021; Seth et al, 2020) depending on the situation. Furthermore, it has been shown that prior beliefs about a task could induce under- and overconfidence (Van Marcke et al, 2022), and that confidence plays a role in shaping certain aspects of decision-making behavior such as the confirmation bias (Rollwage et al, 2020).…”

Section: Discussionmentioning

confidence: 91%

What confidence and the eyes can tell about interacting with a partner

Sanchez

Tomei

Mamassian

et al. 2023

Preprint

View full text Add to dashboard Cite

Every decision we make is accompanied by a sense of confidence. Confidence mechanisms allow evaluating the evidence that supports our decisions such as the quality of our perceptual representations. Confidence is thought to play a critical role in guiding decision-making, but only a few empirical studies have actually investigated the function of confidence. To address this, we designed a perceptual task in which participants provided a confidence judgment on the accuracy of their perceptual decision. Then, they viewed the response of a machine or human partner, and they were instructed to decide whether to keep or change their initial response. We observed that confidence predicted participants' decision to keep or change their initial responses more than task difficulty and perceptual accuracy. This suggests that confidence, as a subjective evaluation of uncertainty, enables us to weigh our decisions in order to adapt our decision-making behavior driving the interaction with a partner. Furthermore, confidence judgments could be predicted by eye pupil dynamics, suggesting that arousal changes are linked to confidence computations. This study contributes to our understanding of the function of confidence in decision-making and highlights the possibility of using pupil dynamics as a proxy of confidence.

show abstract

“…Before the current heyday of DL, a disinterest towards it existed for several years, mainly due to hardware limitations and lack of funding. However, these techniques were reassessed as soon as more powerful hardware became available, especially Graphics Processing Units (GPUs) [ 146 ]. These devices were initially designed to compute three-dimensional graphics in video games and proved to be good performers of parallel computing; therefore, such systems were promptly used for processing DL algorithms.…”

Section: Artificial Intelligence Applications In Plant Stress Sciencementioning

confidence: 99%

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Rico-Chávez

Franco²,

Fernandez‐Jaramillo³

et al. 2022

Plants

View full text Add to dashboard Cite

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.

show abstract

Build confidence and acceptance of AI-based decision support systems - Explainable and liable AI

Cited by 25 publications

References 12 publications

Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians

Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians

What confidence and the eyes can tell about interacting with a partner

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

Contact Info

Product

Resources

About