Paper Info
Title
EU Data Protection Law: An Ally for Scientific Reproducibility?
Abstract
This keynote will introduce some of the key concepts of European data protection law, and clarify how and why this is not equivalent with privacy law. Next, I will explain why and how EU data protection law could enhance the methodological integrity of machine learning applications, also in the domain of multimedia. The question is, first, how the General Data Protection Regulation (GDPR) applies to inferences captured from multimedia data. This raises a number of questions. Does it matter whether such data has been made public by the person it relates to? Does processing personal data always require consent? What counts as valid consent? What if the inferences are mere statistics? What does the prohibition of processing 'sensitive data' (ethnicity, health) mean for multimedia analytics? This keynote will provide a crash course in the underlying 'logic' of the GDPR [3], with a focus on what is relevant for inferences based on multimedia content and metadata. I will uncover the purpose limitation principle as the guiding rationale of EU data protection law, protecting individuals against incorrect, unfair or unwarranted targeting. In the second part of the keynote I will explain how the purpose limitation principle relates to machine learning research design, requiring keen attention to specific aspects of methodological integrity [2]. These may concern p-hacking, data dredging, or cherry picking performance metrics, and connect with the reproducibility crisis in machine learning that is on the verge of destroying the reliability of ML applications [1].
Year
DOI
Venue
2019
10.1145/3343031.3355511
Proceedings of the 27th ACM International Conference on Multimedia
Keywords
Field
DocType
general data protection regulation, machine learning
Computer science,Nice,Data Protection Directive,Law
Conference
ISBN
Citations 
PageRank 
978-1-4503-6889-6
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Mireille Hildebrandt1132.76