They Know More Than You Think: Exploring a Collective Informational Privacy Right in the Age of Algorithmic Predictive Profiling
(2026) JAMM07 20261Department of Law
Faculty of Law
- Abstract
- In the algorithmic age, the use of machine learning algorithms to profile individuals and groups is on the rise. European data protection regulation remains focused on an overly individualistic conception of informational privacy, requiring individuals to manage their own data protection. This has been proved unsustainable in practice due to the countless privacy notices that individuals encounter that go beyond their technical knowledge. A more damning challenge, however, is at the theoretical level. Focusing on the individual fails to account for the mass comparative task that algorithmic predictive profiling undertakes. Instead, each individual’s online informational privacy must be understood as codependent with everyone else’s online... (More)
- In the algorithmic age, the use of machine learning algorithms to profile individuals and groups is on the rise. European data protection regulation remains focused on an overly individualistic conception of informational privacy, requiring individuals to manage their own data protection. This has been proved unsustainable in practice due to the countless privacy notices that individuals encounter that go beyond their technical knowledge. A more damning challenge, however, is at the theoretical level. Focusing on the individual fails to account for the mass comparative task that algorithmic predictive profiling undertakes. Instead, each individual’s online informational privacy must be understood as codependent with everyone else’s online informational privacy.
It is argued that, from a collective conception of group rights, a moral collective informational privacy right can be identified that can introduce population-level privacy considerations and, therefore, can help to challenge power asymmetries created by the power of algorithmic profiling. This right functions as a gateway right that can be used to fully understand the privacy harms inherent in the mass comparative task involved in the use of machine learning algorithms by both states and private entities.
This collective informational privacy right, while being a moral right could be legally protected at the European Court of Human Rights. Two methods of protecting collective informational privacy indirectly through individual rights are explored – namely, applying existing data protect standards to predicted information and challenging the harms that result from predicted information. It will be concluded that these approaches offer useful perspectives on solving the current issues with European data protection approaches, but they do not account for the harms inherent in the comparative task of algorithmic profiling. Instead, it is argued that recent developments in climate change litigation at the Court provide an avenue for grounding non-environmental collective claims, including collective informational privacy. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9232888
- author
- Kaiser, Meera LU
- supervisor
- organization
- course
- JAMM07 20261
- year
- 2026
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Collective Rights, Privacy, Informational Privacy, Artificial Intelligence, Machine Learning, European Court of Human Rights, European Convention of Human Rights, Data Protection, Technology, Human Rights
- language
- English
- id
- 9232888
- date added to LUP
- 2026-06-15 09:13:34
- date last changed
- 2026-06-15 09:13:34
@misc{9232888,
abstract = {{In the algorithmic age, the use of machine learning algorithms to profile individuals and groups is on the rise. European data protection regulation remains focused on an overly individualistic conception of informational privacy, requiring individuals to manage their own data protection. This has been proved unsustainable in practice due to the countless privacy notices that individuals encounter that go beyond their technical knowledge. A more damning challenge, however, is at the theoretical level. Focusing on the individual fails to account for the mass comparative task that algorithmic predictive profiling undertakes. Instead, each individual’s online informational privacy must be understood as codependent with everyone else’s online informational privacy.
It is argued that, from a collective conception of group rights, a moral collective informational privacy right can be identified that can introduce population-level privacy considerations and, therefore, can help to challenge power asymmetries created by the power of algorithmic profiling. This right functions as a gateway right that can be used to fully understand the privacy harms inherent in the mass comparative task involved in the use of machine learning algorithms by both states and private entities.
This collective informational privacy right, while being a moral right could be legally protected at the European Court of Human Rights. Two methods of protecting collective informational privacy indirectly through individual rights are explored – namely, applying existing data protect standards to predicted information and challenging the harms that result from predicted information. It will be concluded that these approaches offer useful perspectives on solving the current issues with European data protection approaches, but they do not account for the harms inherent in the comparative task of algorithmic profiling. Instead, it is argued that recent developments in climate change litigation at the Court provide an avenue for grounding non-environmental collective claims, including collective informational privacy.}},
author = {{Kaiser, Meera}},
language = {{eng}},
note = {{Student Paper}},
title = {{They Know More Than You Think: Exploring a Collective Informational Privacy Right in the Age of Algorithmic Predictive Profiling}},
year = {{2026}},
}