The Value of Interaction in Data Intelligence
This work addresses the undervalued benefit of HCI to computers in data intelligence, though it appears incremental as it formalizes existing concepts rather than introducing a new paradigm.
The paper tackles the problem of quantifying the knowledge computers receive from users via human-computer interaction (HCI) by developing an information-theoretic framework, and it confirms the significant role of HCI in data intelligence workflows through theoretical reasoning.
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.