Certifying artificial intelligence-2.pdf
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The document outlines Vlad Stirbu's professional background and focuses on the regulatory landscape for artificial intelligence, particularly in medical products. It discusses AI development phases, technical debt in machine learning, and the importance of quality management systems in AI alignment. The text also contrasts regulatory approaches between the USA and the EU, emphasizing the need for adaptable design controls and compliance with standards.
![Copyright © 2023 Vlad Stirbu
[1] IEEE Position Statement Artificial Intelligence,
https://globalpolicy.ieee.org/wp-content/uploads/2019/06/IEEE18029.pdf
[2] FDA - Center for Devices and Radiological Health: Design Control Guidance for Medical Device Manufacturers,
1997
[3] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F.,
Dennison, D.: Hidden technical debt in machine learning systems. In: Proceedings of the 28th International
Conference on Neural Information Processing Systems. NIPS’15, 2015
[4] FDA - Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial
Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions, 2023
[5] Mahima Pushkarna, Andrew Zaldivar, and Oddur Kjartansson. 2022. Data Cards: Purposeful and Transparent
Dataset Documentation for Responsible AI. In 2022 ACM Conference on Fairness, Accountability, and
Transparency (FAccT '22)
[6] Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena
Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model Cards for Model Reporting. In Proceedings of
the Conference on Fairness, Accountability, and Transparency (FAT* '19)
[7] Stirbu, V., Granlund, T. & Mikkonen, T. Continuous design control for machine learning in certified medical
systems. Software Qual J (2022).
Bibliography](https://image.slidesharecdn.com/certifyingartificialintelligence-2-230702111729-6659d918/85/Certifying-artificial-intelligence-2-pdf-15-320.jpg)
Certifying artificial intelligence-2.pdf
- 1. Copyright © 2023 Vlad Stirbu Certifying artificial intelligence Vlad Știrbu ESHRE 2023 Copenhagen This work is licensed under a Creative Commons Attribution 4.0 International License.
- 2. Copyright © 2023 Vlad Stirbu Who am I? Current ● University of Jyväskylä, PostDoc Researcher ● University of Helsinki, PostDoc Researcher ● CompliancePal, Founder and lead developer, Expert witness ● RegOps Days, Organizer Past ● Nokia Technologies, Principal Software Engineer ● Nokia Research Center, Researcher
- 3. Copyright © 2023 Vlad Stirbu No conflict of interest
- 4. Copyright © 2023 Vlad Stirbu Learning objectives ● Regulatory landscape awareness ● Tools and techniques ● Avoid regulatory pitfalls
- 5. Copyright © 2023 Vlad Stirbu AI regulations in media vs reality ● General product safety (2001) ● Medical Device Directive (2007) ● Medical Device Regulation (2017) ● AI Act (2023)
- 6. Copyright © 2023 Vlad Stirbu Definitions Artificial intelligence: the theory and development of computer systems that are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, learning, decision-making, and natural language processing (IEEE Position Statement) Medical software: medical device functionality that is implemented in software
- 7. Copyright © 2023 Vlad Stirbu Design Control Safety Effectiveness Adapted from FDA 1997
- 8. Copyright © 2023 Vlad Stirbu AI Development Phases Deployment Software engineers ● Integrate model into the larger software system ● Deploy and operate ✓ Application Data preparation Data engineers ● Data sources ● Label data ✓ Training data Training Data scientists ● Develop algorithms ● Perform experiments with model candidates ✓ Models
- 9. Copyright © 2023 Vlad Stirbu Technical debt in machine learning systems Sculley et al. 2015
- 10. Copyright © 2023 Vlad Stirbu AI development in medical products Deployment Capability to detect data and concept drifts Continuous quality assurance Model redeployment (see FDA 2023) Automation: DevOps, RegOps (Stirbu et al 2022) Data preparation Data assessments Data management Data Cards (Pushkarna et al. 2022) Training Monitoring metrics Assessment of actual medical benefits Model Cards (Mitchell et al 2019) Regulatory lock
- 11. Copyright © 2023 Vlad Stirbu Cybersecurity, Privacy, Information Security ● Connected systems ● Regulations ○ MDR/FDA Guidelines ○ GDPR ○ HIPAA ● Standards ○ ISO 27001
- 12. Copyright © 2023 Vlad Stirbu Regulatory approache differences USA ● Risk-benefit centered ● Recognized Third Parties UE ● Conformance centered ● Notified Bodies
- 13. Copyright © 2023 Vlad Stirbu Design Control Revisited Adapted from FDA 1997 AI alignment Traceability Explainability Interpretability
- 14. Copyright © 2023 Vlad Stirbu Conclusions Establish QMS provisions covering AI development Documented proof of AI alignment
- 15. Copyright © 2023 Vlad Stirbu [1] IEEE Position Statement Artificial Intelligence, https://globalpolicy.ieee.org/wp-content/uploads/2019/06/IEEE18029.pdf [2] FDA - Center for Devices and Radiological Health: Design Control Guidance for Medical Device Manufacturers, 1997 [3] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., Dennison, D.: Hidden technical debt in machine learning systems. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. NIPS’15, 2015 [4] FDA - Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions, 2023 [5] Mahima Pushkarna, Andrew Zaldivar, and Oddur Kjartansson. 2022. Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22) [6] Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19) [7] Stirbu, V., Granlund, T. & Mikkonen, T. Continuous design control for machine learning in certified medical systems. Software Qual J (2022). Bibliography