Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.ORCID iD: 0000-0001-7879-4371
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
Lund University, Sweden.
Lund University, Sweden.
Show others and affiliations
2021 (English)In: 2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice (SEthics), 2021, p. 5-12Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2021. p. 5-12
Keywords [en]
Ethics, Conferences, Redundancy, Europe, Organizations, Machine learning, Artificial intelligence, functional safety, automotive software, trustworthy AI
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-55980DOI: 10.1109/SEthics52569.2021.00009OAI: oai:DiVA.org:ri-55980DiVA, id: diva2:1588406
Conference
2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice (SEthics)
Available from: 2021-08-27 Created: 2021-08-27 Last updated: 2021-08-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Borg, Markus

Search in DiVA

By author/editor
Borg, Markus
By organisation
Mobility and Systems
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 56 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf