Information Quality: the cornerstone for AI-based Industry 4.0

Mattioli, Juliette; Robic, Pierre-Olivier; Jesson, Emeric

doi:10.1016/j.procs.2022.03.059

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Data diversity is the ratio between the number of available data sources, their size, and the dataset are finally used [24]. Other DQ KPIs could be find in [5,18,48,50].…”

Section: Data Qualitymentioning

confidence: 99%

An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering

Mattioli,

Sohier,

Delaborde

et al. 2024

AI Ethics

Self Cite

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When deployed, machine-learning (ML) adoption depends on its ability to actually deliver the expected service safely, and to meet user expectations in terms of quality and continuity of service. For instance, the users expect that the technology will not do something it is not supposed to do, e.g., performing actions without informing users. Thus, the use of Artificial Intelligence (AI) in safety-critical systems such as in avionics, mobility, defense, and healthcare requires proving their trustworthiness through out its overall lifecycle (from design to deployment). Based on surveys on quality measures, characteristics and sub-characteristics of AI systems, the Confiance.ai program (www.confiance.ai) aims to identify the relevant trustworthiness attributes and their associated Key Performance Indicators (KPI) or their associated methods for assessing the induced level of trust. Motivation for ML trustworthiness assessmentTrustworthiness is tightly related to accountability: accountability can be considered as a factor of trust or as an alternative to trust [57]. Then, in [4], dependability is used to represent the overall quality measure of a system based on four sub-attributes including security, safety, reliability, and maintainability. Thereafter, security and dependability became key attributes for computer-based system trust [8]. In 2019, the U.S. National Artificial Intelligence Research and Development Strategic Plan [54] emphasized that: "standard metrics are needed to define quantifiable measures in order to characterize AI technologies". More recently, [65] noted that "significant work is needed to establish what appropriate metrics should be to assess system performance across attributes for responsible AI and across profiles for particular applications/contexts.".The Assessment List for Trustworthy AI [1] considers 7 pillars of trustworthiness: 1) human agency and autonomy, 2) technical robustness and safety, 3) privacy and data governance, 4) transparency, 5) diversity, non discrimination and fairness, 6) societal and environmental well-being, 7) accountability. The European Commission has proposed a set of rules for AI, the AI Act [19], regulating the technology. Such proposals, which are still at the consultation stage, would apply to AI systems developed or deployed in the EU

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“…Data diversity is the ratio between the number of available data sources, their size, and the dataset are finally used [24]. Other DQ KPIs could be find in [5,18,48,50].…”

Section: Data Qualitymentioning

confidence: 99%

An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering

Mattioli,

Sohier,

Delaborde

et al. 2024

AI Ethics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mattioli et al [102] highlighted that data/information is a key ingredient for most data-driven AI applications. AI can help achieve a more holistic approach that would allow the implementation of a continuous approach addressing products/services but also processes or any other factors in an outcome-based approach in terms of information management.…”

Section: Data and Information Management Aspects In Iiotmentioning

confidence: 99%

A Survey on AI-Empowered Softwarized Industrial IoT Networks

Rojas,

Carrascal,

Lopez-Pajares

et al. 2024

Electronics

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The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas.

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Workshop digitalisation and its effect on manufacturing operations management: a new analysis method from a data quality perspective

Li,

Zheng

2024

Enterprise Information Systems

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Information Quality: the cornerstone for AI-based Industry 4.0

Cited by 4 publications

References 15 publications

An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering

An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering

A Survey on AI-Empowered Softwarized Industrial IoT Networks

Workshop digitalisation and its effect on manufacturing operations management: a new analysis method from a data quality perspective

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