Legally Binding but Unfair? Towards Assessing Fairness of Privacy
  Policies
Vincent Freiberger Erik Buchmann
Privacy policies are expected to inform data subjects about their data
protection rights. They should explain the data controller's data management
practices, and make facts such as retention periods or data transfers to third
parties transparent. Privacy policies only fulfill their purpose, if they are
correctly perceived, interpreted, understood, and trusted by the data subject.
Amongst others, this requires that a privacy policy is written in a fair way,
e.g., it does not use polarizing terms, does not require a certain education,
or does not assume a particular social background. In this work-in-progress
paper, we outline our approach to assessing fairness in privacy policies. To
this end, we identify from fundamental legal sources and fairness research, how
the dimensions informational fairness, representational fairness and
ethics/morality are related to privacy policies. We propose options to
automatically assess policies in these fairness dimensions, based on text
statistics, linguistic methods and artificial intelligence. Finally, we conduct
initial experiments with German privacy policies to provide evidence that our
approach is applicable. Our experiments indicate that there are indeed issues
in all three dimensions of fairness. For example, our approach finds out if a
policy discriminates against individuals with impaired reading skills or
certain demographics, and identifies questionable ethics. This is important, as
future privacy policies may be used in a corpus for legal artificial
intelligence models.