  The evaluation of interactive machine learning systems remains a difficult
task. These systems learn from and adapt to the human, but at the same time,
the human receives feedback and adapts to the system. Getting a clear
understanding of these subtle mechanisms of co-operation and co-adaptation is
challenging. In this chapter, we report on our experience in designing and
evaluating various interactive machine learning applications from different
domains. We argue for coupling two types of validation: algorithm-centered
analysis, to study the computational behaviour of the system; and
human-centered evaluation, to observe the utility and effectiveness of the
application for end-users. We use a visual analytics application for guided
search, built using an interactive evolutionary approach, as an exemplar of our
work. Our observation is that human-centered design and evaluation complement
algorithmic analysis, and can play an important role in addressing the
"black-box" effect of machine learning. Finally, we discuss research
opportunities that require human-computer interaction methodologies, in order
to support both the visible and hidden roles that humans play in interactive
machine learning.
