Explainable Artificial Intelligence (XAI) is an emerging topic in Machine Learning (ML) that aims to give humans visibility into how AI systems make decisions. XAI is increasingly important in bringing transparency to fields such as medicine and criminal justice where AI informs high consequence decisions. While many XAI techniques have been proposed, few have been evaluated beyond anecdotal evidence. Our research offers a novel approach to assess how humans interpret AI explanations; we explore this by integrating XAI with Games with a Purpose (GWAP). XAI requires human evaluation at scale, and GWAP can be used for XAI tasks which are presented through rounds of play. This paper outlines the benefits of GWAP for XAI, and demonstrates application through our creation of a multi-player GWAP that focuses on explaining deep learning models trained for image recognition. Through our game, we seek to understand how humans select and interpret explanations used in image recognition systems, and bring empirical evidence on the validity of GWAP designs for XAI.