Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems
(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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- author
- Borg, Markus LU ; Bronson, Joshua ; Christensson, Linus ; Olsson, Fredrik ; Lennartsson, Olof ; Sonnsjo, Elias ; Ebabi, Hamid and Karsberg, Martin
- publishing date
- 2021
- 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}}, }