With the increasing deployment of AI systems in high-stakes decision-making domains such as healthcare, finance and law, the technology's explainability has become an issue of public concern. Explainable AI (XAI) is critical for earning the trust of end-users with regard to the outputs generated by machine learning (ML) algorithms, and the research community in recent years has strived to bring more transparency to the inner workings of AI systems by addressing this "black box" problem. The question of just who is going to open the box has however remained relatively underexplored. In a new paper, a team from Georgia Institute of Technology, Cornell University and IBM Research conducts a mixed-methods study on how people with and without expert knowledge of AI perceive different types of AI explanations. The researchers first conducted a systematic review of related work regarding trust, acceptance, and engagement of autonomous or AI systems, followed by informal interviews with six experts spanning human-computer interactions (HCI), AI, and human-robot interactions (HRI).