Abstract
Although privacy and AI/data science are multi-faceted concepts, there is an increasing trend to focus on only a subset of their meaning: privacy as data privacy, with a focus on confidentiality, and AI/data science as a threat to autonomy and privacy, through data collection, unwanted inferences, and profiling. However, confidentiality and “invisibility” are not always constitutive of privacy as “the freedom from unreasonable constraints on the construction of one’s own identity” – in some cases, visibility can be more important, and data collection, presentation, and inferences can help and extend a desired visibility. In this position paper, I will focus on a specific application around these phenomena: the analysis of vehicle/human trajectory data. I will discuss two recent examples of the analysis of such data: the New York City taxi rides dataset, and the use of data from the maritime Automatic Information System (AIS) for mapping refugee movements on the Mediterranean Sea. The goal is to encourage a discussion as to whether and how such wider fundamental-rights questions and their implications for privacy, data protection, and technology can and should be investigated in the scope of APF.
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Notes
- 1.
Of course, the GDPR also contains and elaborates on many other principles, including requirements on data processing related to IT security (integrity and availability in addition to confidentiality, Article 32 and Article 5(1)(f)), accountability, weighing of interests, and others.
- 2.
A field situated in the intersection of machine learning (as a part of AI) and substantive expertise [7].
- 3.
This movement started as the encouragement of victims of sexual harassment (especially in, but not limited to, the workplace) to tweet about their experiences and give people a sense of the magnitude of the problem. The Twitter hashtag #metoo simplifies the retrievability of these reports, such that specific incidents as well as patterns of sexual harassment become a public and visible phenomenon, rather than remain “private” singular experiences. As in the case of calling out domestic violence, the hope is that “this ‘mainstreaming’ of feminist activism is laying the foundation for a collective shift towards a more just society” [24, p. 239].
- 4.
- 5.
It is debatable where/when the violation occurs. Opinions differ as to whether the existence of knowledge about individuals per se represents a privacy violation, whether this only occurs when this knowledge is acted upon, or whether the publication of data as an enabler of such consequences already forms a privacy violation [4].
- 6.
This information was given to me under conditions of confidentiality, as was the assessment that university ethics boards tend to be conservative in their interpretation of the GDPR. The publicly available university documents that I have seen on what is and what is not allowed regarding the re-use of public datasets, do not address specific questions such as “is it allowed to re-use public datasets”, rather, they refer to the general principle that GDPR compliance always also depends on the whole context of research – which is of course a correct rendering of a law that requires interpretation in context. Even if I therefore cannot provide a reference for my claim, I consider it worthwhile to mention it, for example to encourage discussion among researchers about their respective institutions’ GDPR handling.
- 7.
- 8.
The authors enrich the data with broadcast warning data produced by WWNWS, a global service managed by the UN Maritime Organization IMO (data that appear to cover only a small fraction of vessels in distress, p. 37), and other data such as the tweets issued by NGO vessels.
- 9.
How likely they are will depend on the existence of background knowledge and the existence of and incentives for “attackers” (paparazzi, celebrity fans, law enforcement, criminals, ...).
- 10.
I thank Konstantinos Tserpes for mentioning this example and making available a visualization of the trajectory.
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Acknowledgements
I thank the members of the EU H2020 Marie-Skłodowska-Curie programme project MASTER for the inspiration to deal with the privacy aspects of AIS data and migrants, and for many valuable discussions of previous versions of this article.
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Berendt, B. (2019). Privacy Beyond Confidentiality, Data Science Beyond Spying: From Movement Data and Data Privacy Towards a Wider Fundamental Rights Discourse. In: Naldi, M., Italiano, G., Rannenberg, K., Medina, M., Bourka, A. (eds) Privacy Technologies and Policy. APF 2019. Lecture Notes in Computer Science(), vol 11498. Springer, Cham. https://doi.org/10.1007/978-3-030-21752-5_5
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