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Communication Dans Un Congrès Année : 2023

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

Juliette Mattioli
Henri Sohier
  • Fonction : Auteur
  • PersonId : 1054948
Kahina Amokrane
Afef Awadid
  • Fonction : Auteur
  • PersonId : 1048939
Souhaiel Khalfaoui
  • Fonction : Auteur
  • PersonId : 768857
  • IdRef : 169958590

Résumé

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.
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Dates et versions

hal-04264027 , version 1 (29-10-2023)

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  • HAL Id : hal-04264027 , version 1

Citer

Juliette Mattioli, Henri Sohier, Agnès Delaborde, Kahina Amokrane, Afef Awadid, et al.. An overview of key trustworthiness attributes and KPIs for trusted ML-based systems engineering. Workshop AITA AI Trustworthiness Assessment - AAAI Spring Symposium, Mar 2023, Palo Alto (Californie), United States. ⟨hal-04264027⟩
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