  Companies and academic researchers may collect, process, and distribute large
quantities of personal data without the explicit knowledge or consent of the
individuals to whom the data pertains. Existing forms of consent often fail to
be appropriately readable and ethical oversight of data mining may not be
sufficient. This raises the question of whether existing consent instruments
are sufficient, logistically feasible, or even necessary, for data mining. In
this chapter, we review the data collection and mining landscape, including
commercial and academic activities, and the relevant data protection concerns,
to determine the types of consent instruments used. Using three case studies,
we use the new paradigm of human-data interaction to examine whether these
existing approaches are appropriate. We then introduce an approach to consent
that has been empirically demonstrated to improve on the state of the art and
deliver meaningful consent. Finally, we propose some best practices for data
collectors to ensure their data mining activities do not violate the
expectations of the people to whom the data relate.
