In the era of Industry 4.0, the use of Artificial Intelligence (AI) is widespread in occupational settings. Since dealing with human safety, explainability and trustworthiness of AI are even more important than achieving high accuracy. eXplainable AI (XAI) is investigated in this paper to detect physical fatigue during manual material handling task simulation. Besides comparing global rule-based XAI models (LLM and DT) to black-box models (NN, SVM, XGBoost) in terms of performance, we also compare global models with local ones (LIME over XGBoost). Surprisingly, global and local approaches achieve similar conclusions, in terms of feature importance. Moreover, an expansion from local rules to global rules is designed for Anchors, by posing an appropriate optimization method (Anchors coverage is enlarged from an original low value, 11%, up to 43%). As far as trustworthiness is concerned, rule sensitivity analysis drives the identification of optimized regions in the feature space, where physical fatigue is predicted with zero statistical error. The discovery of such “non-fatigue regions” helps certifying the organizational and clinical decision making.

On the Intersection of Explainable and Reliable AI for physical fatigue prediction / Narteni, Sara; Orani, Vanessa; Cambiaso, Enrico; Rucco, Matteo; Mongelli, Maurizio. - In: IEEE ACCESS. - ISSN 2169-3536. - ELETTRONICO. - 10:(2022), pp. 76243-76260. [10.1109/ACCESS.2022.3191907]

On the Intersection of Explainable and Reliable AI for physical fatigue prediction

Sara Narteni;
2022

Abstract

In the era of Industry 4.0, the use of Artificial Intelligence (AI) is widespread in occupational settings. Since dealing with human safety, explainability and trustworthiness of AI are even more important than achieving high accuracy. eXplainable AI (XAI) is investigated in this paper to detect physical fatigue during manual material handling task simulation. Besides comparing global rule-based XAI models (LLM and DT) to black-box models (NN, SVM, XGBoost) in terms of performance, we also compare global models with local ones (LIME over XGBoost). Surprisingly, global and local approaches achieve similar conclusions, in terms of feature importance. Moreover, an expansion from local rules to global rules is designed for Anchors, by posing an appropriate optimization method (Anchors coverage is enlarged from an original low value, 11%, up to 43%). As far as trustworthiness is concerned, rule sensitivity analysis drives the identification of optimized regions in the feature space, where physical fatigue is predicted with zero statistical error. The discovery of such “non-fatigue regions” helps certifying the organizational and clinical decision making.
File in questo prodotto:
File Dimensione Formato  
xAI_for_fatigue_detection_final.pdf

accesso aperto

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Creative commons
Dimensione 3.45 MB
Formato Adobe PDF
3.45 MB Adobe PDF Visualizza/Apri
On_the_Intersection_of_Explainable_and_Reliable_AI_for_Physical_Fatigue_Prediction.pdf

accesso aperto

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Creative commons
Dimensione 1.67 MB
Formato Adobe PDF
1.67 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2970234