  In human computer interaction (HCI), it is common to evaluate the value of
HCI designs, techniques, devices, and systems in terms of their benefit to
users. It is less common to discuss the benefit of HCI to computers. Every HCI
task allows a computer to receive some data from the user. In many situations,
the data received by the computer embodies human knowledge and intelligence in
handling complex problems, and/or some critical information without which the
computer cannot proceed. In this paper, we present an information-theoretic
framework for quantifying the knowledge received by the computer from its users
via HCI. We apply information-theoretic measures to some common HCI tasks as
well as HCI tasks in complex data intelligence processes. We formalize the
methods for estimating such quantities analytically and measuring them
empirically. Using theoretical reasoning, we can confirm the significant but
often undervalued role of HCI in data intelligence workflows.

Human-Computer Interaction