Audit, Don’t Explain – Recommendations Based on a Socio-Technical Understanding of ML-Based Systems
| dc.contributor.author | Heuer, Hendrik | |
| dc.contributor.editor | Wienrich, Carolin | |
| dc.contributor.editor | Wintersberger, Philipp | |
| dc.contributor.editor | Weyers, Benjamin | |
| dc.date.accessioned | 2021-09-05T18:56:35Z | |
| dc.date.available | 2021-09-05T18:56:35Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | In this position paper, I provide a socio-technical perspective on machine learning-based systems. I also explain why systematic audits may be preferable to explainable AI systems. I make concrete recommendations for how institutions governed by public law akin to the German TÜV and Stiftung Wartentest can ensure that ML systems operate in the interest of the public. | en |
| dc.identifier.doi | 10.18420/muc2021-mci-ws02-232 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/37371 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Mensch und Computer 2021 - Workshopband | |
| dc.relation.ispartofseries | Mensch und Computer | |
| dc.subject | Algorithmic Bias | |
| dc.subject | Algorithmic Experience | |
| dc.subject | Algorithmic Transparency | |
| dc.subject | Human-Centered Machine Learning | |
| dc.subject | Recommender Systems | |
| dc.subject | Social Media | |
| dc.subject | User Beliefs | |
| dc.title | Audit, Don’t Explain – Recommendations Based on a Socio-Technical Understanding of ML-Based Systems | en |
| dc.type | Text/Workshop Paper | |
| gi.citation.publisherPlace | Bonn | |
| gi.conference.date | 5.-8. September 2021 | |
| gi.conference.location | Ingolstadt | |
| gi.conference.sessiontitle | MCI-WS02: UCAI 2021: Workshop on User-Centered Artificial Intelligence | |
| gi.document.quality | digidoc |
Dateien
Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
- Name:
- Contribution_232__a.pdf
- Größe:
- 379.82 KB
- Format:
- Adobe Portable Document Format