  For a social networking service to acquire and retain users, it must find
ways to keep them engaged. By accurately gauging their preferences, it is able
to serve them with the subset of available content that maximises revenue for
the site. Without the constraints of an appropriate regulatory framework, we
argue that a sufficiently sophisticated curator algorithm tasked with
performing this process may choose to explore curation strategies that are
detrimental to users. In particular, we suggest that such an algorithm is
capable of learning to manipulate its users, for several qualitative reasons:
1. Access to vast quantities of user data combined with ongoing breakthroughs
in the field of machine learning are leading to powerful but uninterpretable
strategies for decision making at scale. 2. The availability of an effective
feedback mechanism for assessing the short and long term user responses to
curation strategies. 3. Techniques from reinforcement learning have allowed
machines to learn automated and highly successful strategies at an abstract
level, often resulting in non-intuitive yet nonetheless highly appropriate
action selection. In this work, we consider the form that these strategies for
user manipulation might take and scrutinise the role that regulation should
play in the design of such systems.
