Continuous Quality Assurance and ML Pipelines under the AI Act
Wagner, Matthias LU
(2024)
3rd International Conference on AI Engineering, CAIN 2024, co-located with the 46th International Conference on Software Engineering, ICSE 2024
In Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024
p.247-249
- Abstract
More than ever, Machine Learning (ML) as a subfield of Artificial Intelligence (AI) is on the rise and is finding its way into safety-critical software applications. However, when it comes to quality assurance (QA) and trustworthiness, integrating ML models into software comes with challenges that may not be apparent at first glance. The European Union (EU) aims to tackle this problem with new regulatory requirements in the form of harmonized rules on AI (AI Act). It is a risk-based approach with extensive requirements for high-risk systems as well as for foundation models that can be used in various downstream AI systems. Reliable software engineering processes in the form of ML-enabled automated pipelines are likely to become a... (More)
More than ever, Machine Learning (ML) as a subfield of Artificial Intelligence (AI) is on the rise and is finding its way into safety-critical software applications. However, when it comes to quality assurance (QA) and trustworthiness, integrating ML models into software comes with challenges that may not be apparent at first glance. The European Union (EU) aims to tackle this problem with new regulatory requirements in the form of harmonized rules on AI (AI Act). It is a risk-based approach with extensive requirements for high-risk systems as well as for foundation models that can be used in various downstream AI systems. Reliable software engineering processes in the form of ML-enabled automated pipelines are likely to become a discerning factor for legally compliant ML systems. Our research project aims to contribute to the field by establishing an empirically grounded foundation on how to achieve trustworthy AI Act compliant ML systems. Both a literature review and an interview study are ongoing. At a later stage, concrete tools shall be developed, ideally in cooperation with an industry partner, possibly by utilizing the concept of regulatory sandboxes.
(Less)
- author
- Wagner, Matthias
LU
- organization
- publishing date
- 2024-04-14
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- AI act, quality assurance, software engineering
- host publication
- Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024
- series title
- Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024
- pages
- 3 pages
- publisher
- Association for Computing Machinery (ACM)
- conference name
- 3rd International Conference on AI Engineering, CAIN 2024, co-located with the 46th International Conference on Software Engineering, ICSE 2024
- conference location
- Lisbon, Portugal
- conference dates
- 2024-04-14 - 2024-04-15
- external identifiers
-
- scopus:85196480508
- ISBN
- 9798400705915
- DOI
- 10.1145/3644815.3644973
- language
- English
- LU publication?
- yes
- id
- d1b6d2a3-dee2-47b6-87db-76098dcb50e4
- date added to LUP
- 2024-08-30 14:09:15
- date last changed
- 2024-08-30 14:09:36
@inproceedings{d1b6d2a3-dee2-47b6-87db-76098dcb50e4,
abstract = {{<p>More than ever, Machine Learning (ML) as a subfield of Artificial Intelligence (AI) is on the rise and is finding its way into safety-critical software applications. However, when it comes to quality assurance (QA) and trustworthiness, integrating ML models into software comes with challenges that may not be apparent at first glance. The European Union (EU) aims to tackle this problem with new regulatory requirements in the form of harmonized rules on AI (AI Act). It is a risk-based approach with extensive requirements for high-risk systems as well as for foundation models that can be used in various downstream AI systems. Reliable software engineering processes in the form of ML-enabled automated pipelines are likely to become a discerning factor for legally compliant ML systems. Our research project aims to contribute to the field by establishing an empirically grounded foundation on how to achieve trustworthy AI Act compliant ML systems. Both a literature review and an interview study are ongoing. At a later stage, concrete tools shall be developed, ideally in cooperation with an industry partner, possibly by utilizing the concept of regulatory sandboxes.</p>}},
author = {{Wagner, Matthias}},
booktitle = {{Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024}},
isbn = {{9798400705915}},
keywords = {{AI act; quality assurance; software engineering}},
language = {{eng}},
month = {{04}},
pages = {{247--249}},
publisher = {{Association for Computing Machinery (ACM)}},
series = {{Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024}},
title = {{Continuous Quality Assurance and ML Pipelines under the AI Act}},
url = {{http://dx.doi.org/10.1145/3644815.3644973}},
doi = {{10.1145/3644815.3644973}},
year = {{2024}},
}