  In recent years, the games industry has made a major move towards data-driven
development, using data analytics and player modeling to inform design
decisions. Data-driven techniques are beneficial as they allow for the study of
player behavior at scale, making them very applicable to modern digital game
development. However, with this move towards data driven decision-making comes
a number of ethical concerns. Previous work in player modeling as well as work
in the fields of AI and machine learning have demonstrated several ways in
which algorithmic decision-making can be flawed due to data or algorithmic bias
or lack of data from specific groups. Further, black box algorithms create a
trust problem due to lack of interpretability and transparency of the results
or models developed based on the data, requiring blind faith in the results. In
this position paper, we discuss several factors affecting the use of game data
in the development cycle. In addition to issues raised by previous work, we
also raise issues with algorithms marginalizing certain player groups and flaws
in the resulting models due to their inability to reason about situational
factors affecting players' decisions. Further, we outline some work that seeks
to address these problems and identify some open problems concerning ethics and
game data science.
