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- W2955117458 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." @default.
- W2955117458 created "2019-07-12" @default.
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- W2955117458 date "2019-01-01" @default.
- W2955117458 modified "2023-09-26" @default.
- W2955117458 title "Privacy Beyond Confidentiality, Data Science Beyond Spying: From Movement Data and Data Privacy Towards a Wider Fundamental Rights Discourse" @default.
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- W2955117458 doi "https://doi.org/10.1007/978-3-030-21752-5_5" @default.
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