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In this study, we experimentally analyzed whether people can perceive the accuracy or smartness of AI judgments, and whether the judgment accuracy of AI and the level of confidence in those judgments affect people's decision-making. The results showed that people may perceive an AI's smartness even when it only presents information on the results of its judgments. The results also suggest that AI accuracy and confidence affect human decision-making, and that the magnitude of the effect of AI confidence varies with AI accuracy. We also found that when a person's ability to make a decision is less than or equal to the AI's ability to make a decision, the human performance in a binary decision task improves regardless of AI accuracy. The results obtained in this study are similar in some respects to relationships in which people make decisions while interacting, and the findings from research on human interactions may apply to the research and development of human–AI interactions. [ABSTRACT FROM AUTHOR]

Recently, work has been done to understand aspects of how CI processes with sound. Here, we use neural temporal correlation in the inferior colliculus for identifying and categorising the sound that was used as a stimulus. The success of the classification gradually deteriorates for shorter durations. We tried to improve these success values with deep learning methods for audio, on processing windows of 62.5 ms, 250 ms and 1000 ms. We demonstrate that 62.5 ms could be an integration time for temporal correlation. The neural data contains sound features that can be easily processed with artificial neural networks dedicated to audio signals. Network architectures dedicated to audio classification, such as Yamnet, Vggish, Openl3, used in transfer learning, give quite quickly neural data classification results with very high accuracy, compared to image classification networks. In the case of unshuffled correlation images, we have the best accuracy. With noiseless shuffled correlation images, we have the best accuracy, such as for 1000 ms: 100%, for 250 ms: 96.7%, for 62.5 ms: 93.8%, obtained with the OpenL3 network. To evaluate the importance of the contributions of the input features of a neural network to its outputs, we use Explainable Artificial Intelligence. We then used three different explicability methods, such as Grad-CAM, LIME and Occlusion Sensitivity to obtain three sensitive maps. Network uses different regions corresponding to a very high or very low correlation to make its prediction. [ABSTRACT FROM AUTHOR]

In this study, we experimentally analyzed whether people can perceive the accuracy or smartness of AI judgments, and whether the judgment accuracy of AI and the level of confidence in those judgments affect people’s decision-making. The results showed that people may perceive an AI’s smartness even when it only presents information on the results of its judgments. The results also suggest that AI accuracy and confidence affect human decision-making, and that the magnitude of the effect of AI confidence varies with AI accuracy. We also found that when a person’s ability to make a decision is less than or equal to the AI’s ability to make a decision, the human performance in a binary decision task improves regardless of AI accuracy. The results obtained in this study are similar in some respects to relationships in which people make decisions while interacting, and the findings from research on human interactions may apply to the research and development of human–AI interactions.

Deep learning has demonstrated remarkable accuracy analyzing images for cancer detection tasks in recent years. The accuracy that has been achieved rivals radiologists and is suitable for implementation as a clinical tool. However, a significant problem is that these models are black-box algorithms therefore they are intrinsically unexplainable. This creates a barrier for clinical implementation due to lack of trust and transparency that is a characteristic of black box algorithms. Additionally, recent regulations prevent the implementation of unexplainable models in clinical settings which further demonstrates a need for explainability. To mitigate these concerns, there have been recent studies that attempt to overcome these issues by modifying deep learning architectures or providing after-the-fact explanations. A review of the deep learning explanation literature focused on cancer detection using MR images is presented here. The gap between what clinicians deem explainable and what current methods provide is discussed and future suggestions to close this gap are provided. [ABSTRACT FROM AUTHOR]

Deep learning has demonstrated remarkable accuracy analyzing images for cancer detection tasks in recent years. The accuracy that has been achieved rivals radiologists and is suitable for implementation as a clinical tool. However, a significant problem is that these models are black-box algorithms therefore they are intrinsically unexplainable. This creates a barrier for clinical implementation due to lack of trust and transparency that is a characteristic of black box algorithms. Additionally, recent regulations prevent the implementation of unexplainable models in clinical settings which further demonstrates a need for explainability. To mitigate these concerns, there have been recent studies that attempt to overcome these issues by modifying deep learning architectures or providing after-the-fact explanations. A review of the deep learning explanation literature focused on cancer detection using MR images is presented here. The gap between what clinicians deem explainable and what current methods provide is discussed and future suggestions to close this gap are provided.

Purpose : Explainable AI aims to build systems that not only give high performance but also are able to provide insights that drive the decision making. However, deriving this explanation is often dependent on fully annotated (class label and local annotation) data, which are not readily available in the medical domain. Approach : This paper addresses the above-mentioned aspects and presents an innovative approach to classifying a lung nodule in a CT volume as malignant or benign, and generating a morphologically meaningful explanation for the decision in the form of attributes such as nodule margin, sphericity, and spiculation. A deep learning architecture that is trained using a multi-phase training regime is proposed. The nodule class label (benign/malignant) is learned with full supervision and is guided by semantic attributes that are learned in a weakly supervised manner. Results : Results of an extensive evaluation of the proposed system on the LIDC-IDRI dataset show good performance compared with state-of-the-art, fully supervised methods. The proposed model is able to label nodules (after full supervision) with an accuracy of 89.1% and an area under curve of 0.91 and to provide eight attributes scores as an explanation, which is learned from a much smaller training set. The proposed system's potential to be integrated with a sub-optimal nodule detection system was also tested, and our system handled 95% of false positive or random regions in the input well by labeling them as benign, which underscores its robustness. Conclusions : The proposed approach offers a way to address computer-aided diagnosis system design under the constraint of sparse availability of fully annotated images., (© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).)