  Artificial Intelligence based systems increasingly use personalization to
provide users with relevant content, products, and solutions. Personalization
is intended to support users and address their respective needs and
preferences. However, users are becoming increasingly vulnerable to online
manipulation due to algorithmic advancements and lack of transparency. Such
manipulation decreases users' levels of trust, autonomy, and satisfaction
concerning the systems with which they interact. Increasing transparency is an
important goal for personalization based systems. Unfortunately, system
designers lack guidance in assessing and implementing transparency in their
developed systems.
  In this work we combine insights from technology ethics and computer science
to generate a list of transparency best practices for machine generated
personalization. Based on these best practices, we develop a checklist to be
used by designers wishing to evaluate and increase the transparency of their
algorithmic systems. Adopting a designer perspective, we apply the checklist to
prominent online services and discuss its advantages and shortcomings. We
encourage researchers to adopt the checklist in various environments and to
work towards a consensus-based tool for measuring transparency in the
personalization community.

Computers and Society;Human-Computer Interaction;Machine Learning