FAT* 2020 Invited Tutorial ‘Gender: What the GDPR does not tell us (But maybe you can?)’
Gloria González Fuster (Law, Science, Technology & Society (LSTS) Research Group, Vrije Universiteit Brussel (VUB))
Monday, January 27th 2020. Barceló Sants Hotel, Barcelona, Spain. Room MR8.
Gender is nowadays in a paradoxical state. On the one hand, some national laws, apps and online platforms appear to increasingly welcome the right of individuals to self-determine their own gender, which – it is more and more accepted – might even go beyond the traditional gender binary, potentially encompassing a multitude of diverse, non-confirming gender identities across a presumably fluid spectrum of possibilities. On the other hand, however, a variety of ‘AI solutions’ and related technologies are openly based on their presumed ability to ‘detect’ whether people are male or female: from facial recognition relying on ‘automated gender recognition’ to automated gender classification by social media based on the widespread monitoring of online ‘interests and behaviour’, the ways in which we are all being continuously gendered (and thus potentially re- and mis-gendered) are multiplying, almost always in an opaque manner.
While there is a clear interest in a variety of communities on gender-based discrimination and gender stereotypes, there seems to be comparatively less attention being given to the question of how biases can surface in the very moment of an automated attribution of gender. Equally, while legal scholars have been thinking on whether existing laws protect us effectively against discriminatory automated decision-making (e.g. decisions favouring a gender as opposed to another), there is limited knowledge on the extent to which we might all have a right to know when somebody classifies us on the basis of our presumed gender, a right to know how they reach such conclusions, a right to demand that they stop such classifications, or a right to contest them.
Nature of the Tutorial
This invited tutorial aims at being fundamentally interactive. The presenter’s main aspiration is to open a dialogue with the participants, in order to jointly investigate which could be the best ways to improve our scientific understanding (as researchers), but also in general the understanding by any individual (as a human being entitled to fair, transparent & accountable data processing) of the ways in which gender is attributed – and constructed- online.
Scope of the Tutorial
The tutorial’s focus is the potential of (and the limitations of the potential of) the provisions of the General Data Protection Regulation (GDPR) as a tool to better understand, and actively contest, or engage in, the attribution of gender online.
Key issues that will be addressed are:
- the pervasiveness of gender attribution online;
- the lack of information on gender attribution provided to the individuals which are being labelled (‘data subjects’);
- the absence of any explicit consideration of gender in the GDPR;
- the ways in which data subjects could, in principle, exercise their rights to get a better grasp of what is actually going on, as a first step to potentially do something about it;
- some of the obstacles encountered in the exercise of such rights;
- alternative or complementary paths through which to unmask, and make transparent and accountable, gendering data practices.
Who should participate?
The tutorial is open to everybody regardless of their level of knowledge about gender issues and/or the GDPR. There is no specific requirement beyond a willingness to engage in an open reflection, hopefully across disciplines, on how to interrogate and improve the transparency of ongoing gendering data practices.
About the Presenter
Prof. Dr. Gloria González Fuster is a Research Professor at the Faculty of Law and Criminology of the Vrije Universiteit Brussel (VUB), and co-director of the Law, Science, Technology & Society (LSTS) Research Group. Her research focuses on privacy and data protection law, and she is particularly interested in data subject rights and their connection with digital (mis-)representation.
Slides available here
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