Context-aware applications can facilitate people as they carry out their daily tasks. These applications can use a suite of sensors to detect what is happening in the environment and with the user. They can then infer the user intention. This way, they try to understand the contexts of the situation, and consequently act to provide services. For example, a smart phone can recognize that you are in a conversation, and suppress any incoming messages during this period. To minimize obtrusiveness and allow users to focus primarily on their tasks, context-aware applications perform sensing implicitly without explicitly informing users. Furthermore, to better understand the contexts of users in their physical and social environments, context-aware applications are using increasingly complex mechanisms to infer these contexts (e.g., by using machine learning algorithms). This implicit sensing and complex inference can remain invisible when the applications work well and as expected, but become a mystery when the applications behave inappropriately or unexpectedly. In such cases, the lack of understanding of these applications can lead users to mistrust, misuse it, or abandon them altogether. To counter this, context-aware applications should be intelligible, capable of generating explanations of their behavior.

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