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Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems

Borg, Markus LU ; Bronson, Joshua ; Christensson, Linus ; Olsson, Fredrik ; Lennartsson, Olof ; Sonnsjo, Elias ; Ebabi, Hamid and Karsberg, Martin (2021) 2nd IEEE/ACM International Workshop on Ethics in Software Engineering Research and Practice, SEthics 2021 In Proceedings - 2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice, SEthics 2021 p.5-12
Abstract

Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI- HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related... (More)

Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI- HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related to human agency and transparency can be disregarded. Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation. We present how we plan to develop the ADAS to ensure ALTAI-compliance. Finally, we provide three recommendations for the next revision of ALTAI, i.e., life-cycle variants, domainspecific adaptations, and removed redundancy.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
automotive software, ethics, functional safety, machine learning, trustworthy AI
host publication
Proceedings - 2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice, SEthics 2021
series title
Proceedings - 2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice, SEthics 2021
article number
9474814
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2nd IEEE/ACM International Workshop on Ethics in Software Engineering Research and Practice, SEthics 2021
conference location
Virtual, Online
conference dates
2021-06-04
external identifiers
  • scopus:85113233819
ISBN
9781665445559
DOI
10.1109/SEthics52569.2021.00009
language
English
LU publication?
no
id
406a3915-ebf6-443e-8f21-e00ac3a652a6
date added to LUP
2021-09-06 15:57:25
date last changed
2022-04-27 03:42:24
@inproceedings{406a3915-ebf6-443e-8f21-e00ac3a652a6,
  abstract     = {{<p>Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI- HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related to human agency and transparency can be disregarded. Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation. We present how we plan to develop the ADAS to ensure ALTAI-compliance. Finally, we provide three recommendations for the next revision of ALTAI, i.e., life-cycle variants, domainspecific adaptations, and removed redundancy. </p>}},
  author       = {{Borg, Markus and Bronson, Joshua and Christensson, Linus and Olsson, Fredrik and Lennartsson, Olof and Sonnsjo, Elias and Ebabi, Hamid and Karsberg, Martin}},
  booktitle    = {{Proceedings - 2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice, SEthics 2021}},
  isbn         = {{9781665445559}},
  keywords     = {{automotive software; ethics; functional safety; machine learning; trustworthy AI}},
  language     = {{eng}},
  pages        = {{5--12}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings - 2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice, SEthics 2021}},
  title        = {{Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems}},
  url          = {{http://dx.doi.org/10.1109/SEthics52569.2021.00009}},
  doi          = {{10.1109/SEthics52569.2021.00009}},
  year         = {{2021}},
}