Understanding the Role of Explanations in Computer Vision Applications

Recent advancements in AI show great performance over a range of applications, but its operations are hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. Against this background, this thesis reports on four user studies designed to investigate the role of explanations in helping end-users build a better functional understanding of computer vision processes. In addition, we seek to understand what features lay users attend to in order to build such functional understanding, and whether different techniques provide different gains. In particular, we begin by examining the utility of "keypoint markers"; coloured dot visualisations that correspond to patterns of interest identified by an underlying algorithm and can be seen in many computer vision applications. We then investigate the utility of saliency maps; a popular group of explanations for the operation of Convolutional Neural Networks (CNNs). The findings indicate that keypoint markers can be helpful if they are presented in line with users' expectations. They also indicate that saliency maps can improve participants' ability to predict the outcome of a CNN, but only moderately. Overall, this thesis contributes by evaluating these explanation techniques through user studies. It also provides a number of key findings that provide helpful guidelines for practitioners on how and when to use these explanations, as well as which types of users to target. Furthermore, it proposes and evaluates two novel explanation techniques as well as a number of helpful tools that help researchers and practitioners when designing user studies around the evaluation of explanations. Finally, this thesis highlights a number of implications for the design of explanation techniques and further research in that area

Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study

Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. In this paper, we report on an online between-group user study designed to evaluate the performance of "saliency maps" - a popular explanation algorithm for image classification applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some specific image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the network's output for new images. Drawing on our findings, we highlight implications for design and further research on explainable AI. In particular, we argue the HCI and AI communities should look beyond instance-level explanations.Comment: 10 pages, 6 figures, accepted long paper for ACM IUI 202

Evaluating the Effect of Feedback from Different Computer Vision Processing Stages: A Comparative Lab Study

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applicationstargeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our fndings indicate that the stage of processing from which feedback is derived plays an important role in users’ ability to develop coherent and correct understandings of a system’s operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms

Embedded Real-Time Video Surveillance System based on Multi-Sensor and Visual Tracking

This Paper describes the design and implementation of an embedded remote video surveillance system for general purposes security application. The proposed system is able to detect and report vandalism, tampering, and theft activities before they take place via SMS, Email or by a phone call. The system has been enriched with a vast range of sensors to increase its sensing capability of different types of attacks.Moreover, the proposed system has been enhanced by adding a visual verification technique to overcome false alarms generated by sensors, where a video camera is integrated within the system software to capture video footage, verify, and track the abnormal events taking into consideration bandwidth consumption and real-time processing. Finally, the system was implemented using SBC (Raspberry Pi) as a working platform supported by OpenCV and Python as a programming language. The results proved that the proposed system can achieve monitoring and reporting in real-time. Where the average processing time specified to complete all the required tasks for each frame (starting from video source to broadcasting stage) does not exceed 64%. Moreover, the proposed system achieved a reduction in the utilized data size as a result of using image processing algorithms, reaching an average of 91%, which decreased the amount of transferred data to an average of 13.4 Mbit/sec and increased the bandwidth efficiency to an average of 92%. Finally, this system is characterized by being flexible, portable, easy to install, expandable and cost-effective. Therefore, it can be considered as an efficient technology for different monitoring purposes

Evaluating the effect of feedback from different computer vision processing stages: a comparative lab study

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applications targeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our findings indicate that the stage of processing from which feedback is derived plays an important role in users' ability to develop coherent and correct understandings of a system's operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms

Evaluating the effect of feedback from different computer vision processing stages: a comparative lab study

Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applications targeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our findings indicate that the stage of processing from which feedback is derived plays an important role in users' ability to develop coherent and correct understandings of a system's operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms

Dataset: Evaluating the Effect of Feedback from Different Computer Vision Processing Stages

Dataset supports: J. Kittley-Davies, A. Alqaraawi, R. Yang, E. Costanza, A. Rogers, and S. Stein. 2019. Evaluating the Effect of Feedback from Different Computer Vision Processing Stages: A Comparative Lab Study. In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4&ndash;9, 2019, Glasgow, Scotland UK. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3290605.3300273</span

New-onset diabetes after kidney transplantation: Incidence, risk factors, and outcomes

Many patients develop new-onset diabetes after kidney transplantation (NODAT). Its incidence and epidemiology are unknown in the Saudi population. We aimed to study the incidence, epidemiology, and outcomes of kidney transplant recipients who developed NODAT. This is a retrospective study of all adults who received kidney transplant between January 2003 and December 2009. NODAT was defined according to the criteria outlined in the 2003 International Consensus guidelines. A total of 500 patients were included in this study, 54% were male patients. One hundred thirty-six patients (27%) developed diabetes (NODAT group). In the univariate analysis, patients were older in the NODAT group (P <0.001), were of higher weight (P = 0.006), and had positive family history of diabetes (P = 0.002). Similarly, more patients in this group had impaired glucose tolerance before transplant (P = 0.01) and history of hepatitis C infection (P = 0.005). In the multivariate analysis, older age [odds ratio (OR) 1.06], family history of diabetes (OR 1.09), hepatitis C infection (OR 1.92), and impaired fasting glucose (OR 1.79) were significant risk factors for the development of NODAT. Mortality was 6% in the NODAT group and 0.5% in the non-diabetic group had died (P <0.001). Graft survival was not different between the groups (P = 0.35). In conclusion, there is a significant risk of developing diabetes after renal transplantation. Patients are at higher risk if they are older, have a family history of diabetes, pre-transplant impaired fasting/random glucose, and hepatitis C virus infection