Explainability via Responsibility
  Procedural Content Generation via Machine Learning (PCGML) refers to a group
of methods for creating game content (e.g. platformer levels, game maps, etc.)
using machine learning models. PCGML approaches rely on black box models, which
can be difficult to understand and debug by human designers who do not have
expert knowledge about machine learning. This can be even more tricky in
co-creative systems where human designers must interact with AI agents to
generate game content. In this paper we present an approach to explainable
artificial intelligence in which certain training instances are offered to
human users as an explanation for the AI agent's actions during a co-creation
process. We evaluate this approach by approximating its ability to provide
human users with the explanations of AI agent's actions and helping them to
more efficiently cooperate with the AI agent.
