Artificial intelligence (AI) has a growing influence on every aspect of life. These systems need to be transparent, accountable, and explainable to be reliable and safe. Explainability helps to reduce the opacity of such systems and aids in gaining end-user trust in the system. Therefore, explainability can be seen as an emerging requirement for AI-based systems. A number of studies emphasize the transparency and explainability of AI-based systems. However, studies on identifying stakeholders to these requirements and how to elicit and specify explainability requirements are still rare and at an early stage. This Ph.D. research aims to establish a comprehensive reference process model that can serve as a guide for practitioners to address explainability requirements concerning AI-based systems.