Trustworthy Artificial Intelligence: A Review

Kaur, Davinder; Uslu, Süleyman; Rittichier, Kaley J.; Durresi, Arjan

doi:10.1145/3491209

Cited by 286 publications

(163 citation statements)

References 148 publications

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“…In this modern world, most day-to-day tasks are assisted by machines and algorithms. Several factors, such as fairness, explainability, accountability, reliability, and acceptance, are considered using reliable machine–algorithm-coordinated outcomes [ 3 ]. AI can be interpreted as the ability of the computer or robot to reproduce human intelligence in the form of software and algorithms.…”

Section: The Role Of Ai In Establishing a Smart Sensor Networkmentioning

confidence: 99%

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

et al. 2022

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Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

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Section: The Role Of Ai In Establishing a Smart Sensor Networkmentioning

confidence: 99%

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

et al. 2022

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“…This section reviews the related literature on trustworthy AI. Davinder et al [10] defined the needs for a trustworthy AI system, reviewed and compared existing trustworthy AI approaches based on their trustworthy requirements (i.e., Fairness, Explainability, Accountability, Privacy, Acceptance). They also considered human involvement for AI trustworthy (i.e., human before the loop, human in the loop, and human over the loop) and techniques to verify/validate the trustworthiness of AI without compromising its performance such as metamorphic testing, expert panels, benchmarking, and field trials.…”

Section: Related Workmentioning

confidence: 99%

Never trust, always verify : a roadmap for Trustworthy AI?

Tidjon¹,

Khomh²

2022

Preprint

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Artificial Intelligence (AI) is becoming the corner stone of many systems used in our daily lives such as autonomous vehicles, healthcare systems, and unmanned aircraft systems. Machine Learning is a field of AI that enables systems to learn from data and make decisions on new data based on models to achieve a given goal. The stochastic nature of AI models makes verification and validation tasks challenging. Moreover, there are intrinsic biaises in AI models such as reproductibility bias, selection bias (e.g., races, genders, color), and reporting bias (i.e., results that do not reflect the reality). Increasingly, there is also a particular attention to the ethical, legal, and societal impacts of AI. AI systems are difficult to audit and certify because of their black-box nature. They also appear to be vulnerable to threats; AI systems can misbehave when untrusted data are given, making them insecure and unsafe. Governments, national and international organizations have proposed several principles to overcome these challenges but their applications in practice are limited and there are different interpretations in the principles that can bias implementations. In this paper, we examine trust in the context of AI-based systems to understand what it means for an AI system to be trustworthy and identify actions that need to be undertaken to ensure that AI systems are trustworthy. To achieve this goal, we first review existing approaches proposed for ensuring the trustworthiness of AI systems, in order to identify potential conceptual gaps in understanding what trustworthy AI is. Then, we suggest a trust (resp. zero-trust) model for AI and suggest a set of properties that should be satisfied to ensure the trustworthiness of AI systems.

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“…Moreover, readers and journalists may perceive results from JKPs as less transparent and difficult to understand [92] as they are driven by AI. To improve their perception of trustworthiness and transparency, research on JKPs should consider explainable AI methods [93].…”

Section: Concernsmentioning

confidence: 99%

Supporting Newsrooms with Journalistic Knowledge Graph Platforms: Current State and Future Directions

Ocaña

Opdahl

2022

Technologies

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Increasing competition and loss of revenues force newsrooms to explore new digital solutions. The new solutions employ artificial intelligence and big data techniques such as machine learning and knowledge graphs to manage and support the knowledge work needed in all stages of news production. The result is an emerging type of intelligent information system we have called the Journalistic Knowledge Platform (JKP). In this paper, we analyse for the first time knowledge graph-based JKPs in research and practice. We focus on their current state, challenges, opportunities and future directions. Our analysis is based on 14 platforms reported in research carried out in collaboration with news organisations and industry partners and our experiences with developing knowledge graph-based JKPs along with an industry partner. We found that: (a) the most central contribution of JKPs so far is to automate metadata annotation and monitoring tasks; (b) they also increasingly contribute to improving background information and content analysis, speeding-up newsroom workflows and providing newsworthy insights; (c) future JKPs need better mechanisms to extract information from textual and multimedia news items; (d) JKPs can provide a digitalisation path towards reduced production costs and improved information quality while adapting the current workflows of newsrooms to new forms of journalism and readers’ demands.

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Trustworthy Artificial Intelligence: A Review

Cited by 286 publications

References 148 publications

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Never trust, always verify : a roadmap for Trustworthy AI?

Supporting Newsrooms with Journalistic Knowledge Graph Platforms: Current State and Future Directions

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