Ensuring trustworthy AI for sensitive infrastructure using Knowledge Representation
dc.contributor.author | Mejri, Oumayma | |
dc.contributor.author | Waedt, Karl | |
dc.contributor.author | Yatagha, Romarick | |
dc.contributor.author | Edeh, Natasha | |
dc.contributor.author | Sebastiao, Claudia Lemos | |
dc.contributor.editor | Klein, Maike | |
dc.contributor.editor | Krupka, Daniel | |
dc.contributor.editor | Winter, Cornelia | |
dc.contributor.editor | Gergeleit, Martin | |
dc.contributor.editor | Martin, Ludger | |
dc.date.accessioned | 2024-10-21T18:24:17Z | |
dc.date.available | 2024-10-21T18:24:17Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Artificial intelligence (AI) has become increasingly integrated into various aspects of society, from healthcare and finance to law enforcement and hiring processes. More recently, sensitive infrastructure such as nuclear plants is engaging AI in aspects of safety. However, these systems are not immune to biases and ethical concerns. This paper explores the role of knowledge representation in addressing ethics and fairness in AI, examining how biased or incomplete representations can lead to unfair outcomes and unreliable decision-making. It proposes strategies to mitigate these risks. | en |
dc.identifier.doi | 10.18420/inf2024_167 | |
dc.identifier.eissn | 2944-7682 | |
dc.identifier.isbn | 978-3-88579-746-3 | |
dc.identifier.issn | 2944-7682 | |
dc.identifier.pissn | 1617-5468 | |
dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/45144 | |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik e.V. | |
dc.relation.ispartof | INFORMATIK 2024 | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-352 | |
dc.subject | Artificial Intelligence | |
dc.subject | Bias in AI | |
dc.subject | Knowledge Representation | |
dc.subject | Trustworthy AI | |
dc.subject | Sensitive Infrastructure | |
dc.subject | Data Bias | |
dc.subject | Explainable AI | |
dc.subject | Algorithmic Fairness | |
dc.title | Ensuring trustworthy AI for sensitive infrastructure using Knowledge Representation | en |
dc.type | Text/Conference Paper | |
gi.citation.endPage | 1938 | |
gi.citation.publisherPlace | Bonn | |
gi.citation.startPage | 1929 | |
gi.conference.date | 24.-26. September 2024 | |
gi.conference.location | Wiesbaden | |
gi.conference.sessiontitle | 9th IACS WS'24 |
Dateien
Originalbündel
1 - 1 von 1
Lade...
- Name:
- Mejri_et_al_Ensuring_trustworthy_AI.pdf
- Größe:
- 393.25 KB
- Format:
- Adobe Portable Document Format