A historical perspective of explainable Artificial Intelligence

Confalonieri, Roberto; Çoba, Ludovik; Wagner, Benedikt; Besold, Tarek R.

doi:10.1002/widm.1391

Cited by 186 publications

(103 citation statements)

References 76 publications

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“…On the other hand, AI-based applications are increasingly being adopted by different sectors, including retail. Therefore, business stakeholders and users of AI systems should be able to understand their systems to trust outputs and manage these applications to their needs without getting help from AI experts or engineers [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores

Yilmazer

Birant

2021

Sensors

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Providing high on-shelf availability (OSA) is a key factor to increase profits in grocery stores. Recently, there has been growing interest in computer vision approaches to monitor OSA. However, the largest and well-known computer vision datasets do not provide annotation for store products, and therefore, a huge effort is needed to manually label products on images. To tackle the annotation problem, this paper proposes a new method that combines two concepts “semi-supervised learning” and “on-shelf availability” (SOSA) for the first time. Moreover, it is the first time that “You Only Look Once” (YOLOv4) deep learning architecture is used to monitor OSA. Furthermore, this paper provides the first demonstration of explainable artificial intelligence (XAI) on OSA. It presents a new software application, called SOSA XAI, with its capabilities and advantages. In the experimental studies, the effectiveness of the proposed SOSA method was verified on image datasets, with different ratios of labeled samples varying from 20% to 80%. The experimental results show that the proposed approach outperforms the existing approaches (RetinaNet and YOLOv3) in terms of accuracy.

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Section: Methodsmentioning

confidence: 99%

Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores

Yilmazer

Birant

2021

Sensors

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“…To this direction, a set of principles for trustworthy and secure use of ML techniques in the digital society have been drawn to augment innovation while protecting fundamental human rights [92] . Although ML techniques could extract complex patterns and correlations from large datasets, there is a severe lack of understanding considering the causal relationships and the explicit rules [93] .…”

Section: Literature Reviewmentioning

confidence: 99%

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

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Exarchos

Papaloukas

et al. 2021

Computational and Structural Biotechnology Journal

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Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.

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“…The closely related study of explainability has become popular in recent years with the rise of Artificial Intelligence (AI) and AI-based systems ( Adadi and Berrada, 2018 ; Baum et al, 2018 ; Gunning et al, 2019 ). This has led to the new field of explainable AI (XAI) ( Barredo Arrieta et al, 2020 ; Confalonieri et al, 2021 ), which is concerned with the ability to provide explanations about the mechanisms and decisions of AI systems ( Doshi-Velez and Kim, 2017 ; Lipton, 2018 ).…”

Section: Related Workmentioning

confidence: 99%

IEEE P7001: A Proposed Standard on Transparency

et al. 2021

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This paper describes IEEE P7001, a new draft standard on transparency of autonomous systems1. In the paper, we outline the development and structure of the draft standard. We present the rationale for transparency as a measurable, testable property. We outline five stakeholder groups: users, the general public and bystanders, safety certification agencies, incident/accident investigators and lawyers/expert witnesses, and explain the thinking behind the normative definitions of “levels” of transparency for each stakeholder group in P7001. The paper illustrates the application of P7001 through worked examples of both specification and assessment of fictional autonomous systems.

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A historical perspective of explainable Artificial Intelligence

Cited by 186 publications

References 76 publications

Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores

Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

IEEE P7001: A Proposed Standard on Transparency

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