Towards Better Trust in Human-Machine Teaming through Explainable Dependability
The human-machine teaming paradigm is increasingly widespread in critical domains, such as healthcare and domestic assistance. The paradigm goes beyond human-on-the-loop and human-in-the-loop systems by promoting tight teamwork between humans and autonomous machines that collaborate in the same phys...
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| Published in: | International Conference on Software Architecture Companion (Online) pp. 86 - 90 |
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| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
01.03.2023
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| Subjects: | |
| ISSN: | 2768-4288 |
| Online Access: | Get full text |
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| Summary: | The human-machine teaming paradigm is increasingly widespread in critical domains, such as healthcare and domestic assistance. The paradigm goes beyond human-on-the-loop and human-in-the-loop systems by promoting tight teamwork between humans and autonomous machines that collaborate in the same physical space. These systems are expected to build a certain level of trust by enforcing dependability and exhibiting interpretable behavior. We present emerging results in this direction, with a novel framework aiming at achieving better trust in human-machine teaming leveraging formal analysis, as well as eXplainable AI. We illustrate our approach and the emerging results with an example from the healthcare domain. |
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| ISSN: | 2768-4288 |
| DOI: | 10.1109/ICSA-C57050.2023.00029 |