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Your search keyword '"Natali, C."' showing total 12 results
Start Over Author "Natali, C." Publisher elsevier ltd
12 results on '"Natali, C."'

4. Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI

Author

Cabitza, F, Campagner, A, Malgieri, G, Natali, C, Schneeberger, D, Stoeger, K, Holzinger, A, Cabitza F., Campagner A., Malgieri G., Natali C., Schneeberger D., Stoeger K., Holzinger A., Cabitza, F, Campagner, A, Malgieri, G, Natali, C, Schneeberger, D, Stoeger, K, Holzinger, A, Cabitza F., Campagner A., Malgieri G., Natali C., Schneeberger D., Stoeger K., and Holzinger A.

Abstract

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods.

Published
2023

11. Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI

Author

Andreas HOLZINGER, Karl Stöger, David Schneeberger, ANDREA CAMPAGNER, Chiara Natali, Federico Cabitza, Gianclaudio Malgieri, Cabitza, F, Campagner, A, Malgieri, G, Natali, C, Schneeberger, D, Stoeger, K, and Holzinger, A

Subjects
Artificial intelligence, XAI, Explanation, Explainable AI, Machine learning, General Engineering, Computer Science Applications, Taxonomy
Abstract

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods.

Published
2023

12. Field assessment of active BSE: Trends over test days of subjective indicators and self-reported fatigue for railway construction workers.

Author

Sposito M, Fanti V, Poliero T, Caldwell DG, and Di Natali C

Abstract

The research community has conducted several controlled "in -lab" assessments on the effectiveness of industrial exoskeletons, paving the way for their adoption. However, field testing, focusing on ergonomics and the user experience, could serve to enhance both end-users' awareness and address open doubts concerning true effectiveness of industrial exoskeletons. This study presents an analysis of qualitative data regarding the use of back-support exoskeletons during field trials in harsh civil engineering environments. This work evaluates the StreamEXO's (an active back-support exoskeleton) efficacy in reducing fatigue and the evolution of its perceived usefulness. This is achieved using qualitative data collection tools, during real scenarios testing over multiple-day trials. Collected data shows a positive correlation between self-reported fatigue, measured on a four verbal anchors-based Borg CR10 scale, and the use of the exoskeleton during physically demanding movements. Moreover, the evolution of scores throughout the testing sessions (90 minutes of exoskeleton use for three nonconsecutive days) suggests a trend due to the adaptation and learning curve of workers during the exoskeleton experience. The analysis of the open-ended answers highlights that the adaptation to physical interaction has a negative oscillation on day two to rise back during the third day, possibly correlated to a change in muscle pattern. The main critical factors affecting comfort during the exoskeleton experience are weight balance, body pressure, and thermal comfort, which can strongly affect device acceptance., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work was supported by the STREAM project funded by Shift2Rail Joint Undertaking, established under the European Unions 10.13039/100010661Horizon 2020 Framework Programme for research and innovation, under grant agreement No. 101015418., (© 2024 The Author(s).)

Published
2024
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