Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policies
This addresses the need for fair privacy policies to ensure they are understandable and trustworthy for data subjects, though it is incremental as it builds on existing fairness research and applies known methods to a new domain.
The paper tackles the problem of assessing fairness in privacy policies by proposing an automated approach based on text statistics, linguistics, and AI, with initial experiments on German policies revealing issues across three fairness dimensions.
Privacy policies are expected to inform data subjects about their data protection rights and should explain the data controller's data management practices. Privacy policies only fulfill their purpose, if they are correctly interpreted, understood, and trusted by the data subject. This implies 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. We outline our approach to assessing fairness in privacy policies. 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. We conduct initial experiments with German privacy policies to provide evidence that our approach is applicable. Our experiments indicate that there are issues in all three dimensions of fairness. This is important, as future privacy policies may be used in a corpus for legal artificial intelligence models.