Safety Assurance of Artificial Intelligence-Based Systems: A Systematic Literature Review on the State of the Art and Guidelines for Future Work

Neto, Antonio V. Silva; Camargo, João Batista; Almeida, Jorge Rady de; Cugnasca, Paulo Sérgio

doi:10.1109/access.2022.3229233

Cited by 11 publications

(9 citation statements)

References 145 publications

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“…These technologies enable seamless integration of data from diverse sources, including sensors, actuators, and operational databases, facilitating comprehensive condition monitoring and predictive analytics. Recent research in industrial machinery maintenance has delved into the utilization of Industry 4.0 technologies [4,5], particularly concerning predictive maintenance approaches ( [6][7][8]). Leveraging cyber-physical systems as the technological backbone, predictive approaches based on text mining [9] and machine learning [10] have been explored to enhance the performance of the maintenance process.…”

Section: Maintenancementioning

confidence: 99%

Predictive Maintenance with Linguistic Text Mining

Postiglione,

Monteleone

2024

Mathematics

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The escalating intricacy of industrial systems necessitates strategies for augmenting the reliability and efficiency of industrial machinery to curtail downtime. In such a context, predictive maintenance (PdM) has surfaced as a pivotal strategy. The amalgamation of cyber-physical systems, IoT devices, and real-time data analytics, emblematic of Industry 4.0, proffers novel avenues to refine maintenance of production equipment from both technical and managerial standpoints, serving as a supportive technology to enhance the precision and efficacy of predictive maintenance. This paper presents an innovative approach that melds text mining techniques with the cyber-physical infrastructure of a manufacturing sector. The aim is to improve the precision and promptness of predictive maintenance within industrial settings. The text mining framework is designed to sift through extensive log files containing data on the status of operational parameters. These datasets encompass information generated by sensors or computed by the control system throughout the production process execution. The algorithm aids in forecasting potential equipment failures, thereby curtailing maintenance costs and fortifying overall system resilience. Furthermore, we substantiate the efficacy of our approach through a case study involving a real-world industrial machine. This research contributes to the progression of predictive maintenance strategies by leveraging the wealth of textual information available within industrial environments, ultimately bolstering equipment reliability and operational efficiency.

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Section: Maintenancementioning

confidence: 99%

Predictive Maintenance with Linguistic Text Mining

Postiglione,

Monteleone

2024

Mathematics

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“…The recent effervescence surrounding Artificial Intelligence (AI) has led to a significant increase, especially from the mid-2010s onwards, in research on its usage within the core of safety-critical systems [1]. One of the most relevant concerns on applying AI in safetycritical applications is the safety assurance of these systems, which comprises "(.…”

Section: Introductionmentioning

confidence: 99%

“….) the set of activities, means, and methods that shall be considered, throughout the lifecycle of a system, to produce results towards building arguments that confidently support the safety requirements/targets of such a system have been met" [1][2][3][4][5][6][7]. This is taken into account not only at the design time but also throughout operation, notably when online learning is at play [1,[8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the safety assurance of AI-based systems is a relevant theme for research, especially because means and methods within pre-existing standards and research papers feature gaps in relevant themes for their practical usage, as evidenced by a systematic literature review covering 5090 papers published up to 2022 [1]. These include, but are not limited to, the lack of detailed guidelines covering the dos and don'ts of safety-critical AI for safety practitioners [34,35], constraining safety practices to a subset of AI variants [36][37][38][39][40][41], and crafting safety practices only for specific application domains [8,9,37,40].…”

Section: Introductionmentioning

confidence: 99%

“…This is justified by two aspects: (i.) the scarcity of research with this specific objective [1] and (ii.) specific safety constraints that are applicable to safety-critical systems with FPGAs.…”

Section: Introductionmentioning

confidence: 99%

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Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System

Silva Neto,

Silva,

Camargo

et al. 2023

Electronics

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With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems meet their safety requirements is of paramount importance for their revenue service. Based on this context, this paper has two main objectives. The first of them is to present the Safety ArtISt method, developed by the authors to guide the lifecycle of AI-based safety-critical systems, and emphasize its FPGA-oriented tasks and recommended practice towards safety assurance. The second one is to illustrate the application of Safety ArtISt with an FPGA-based braking control system for autonomous vehicles relying on explainable AI generated with High-Level Synthesis. The results indicate that Safety ArtISt played four main roles in the safety lifecycle of AI-based systems for FPGAs. Firstly, it provided guidance in identifying the safety-critical role of activities such as sensitivity analyses for numeric representation and FPGA dimensioning to achieve safety. Furthermore, it allowed building qualitative and quantitative safety arguments from analyses and physical experimentation with actual FPGAs. It also allowed the early detection of safety issues—thus reducing project costs—and, ultimately, it uncovered relevant challenges not discussed in detail when designing safety-critical, explainable AI for FPGAs.

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Managing workplace AI risks and the future of work

Howard,

Schulte

2024

American J Industrial Med

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Artificial intelligence (AI)—the field of computer science that designs machines to perform tasks that typically require human intelligence—has seen rapid advances in the development of foundation systems such as large language models. In the workplace, the adoption of AI technologies can result in a broad range of hazards and risks to workers, as illustrated by the recent growth in industrial robotics and algorithmic management. Sources of risk from deployment of AI technologies across society and in the workplace have led to numerous government and private sector guidelines that propose principles governing the design and use of trustworthy and ethical AI. As AI capabilities become integrated in devices, machines, and systems across industry sectors, employers, workers, and occupational safety and health practitioners will be challenged to manage AI risks to worker health, safety, and well‐being. Five risk management options are presented as ways to assure that only trustworthy and ethical AI enables workplace devices, machinery, and processes. AI technologies will play a significant role in the future of work. The occupational safety and health practice and research communities need to ensure that the promise of these new AI technologies results in benefit, not harm, to workers.

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Safety Assurance of Artificial Intelligence-Based Systems: A Systematic Literature Review on the State of the Art and Guidelines for Future Work

Cited by 11 publications

References 145 publications

Predictive Maintenance with Linguistic Text Mining

Predictive Maintenance with Linguistic Text Mining

Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System

Managing workplace AI risks and the future of work

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