  The interactive machine learning (IML) community aims to augment humans'
ability to learn and make decisions over time through the development of
automated decision-making systems. This interaction represents a collaboration
between multiple intelligent systems---humans and machines. A lack of
appropriate consideration for the humans involved can lead to problematic
system behaviour, and issues of fairness, accountability, and transparency.
This work presents a human-centred thinking approach to applying IML methods.
This guide is intended to be used by AI practitioners who incorporate human
factors in their work. These practitioners are responsible for the health,
safety, and well-being of interacting humans. An obligation of responsibility
for public interaction means acting with integrity, honesty, fairness, and
abiding by applicable legal statutes. With these values and principles in mind,
we as a research community can better achieve the collective goal of augmenting
human ability. This practical guide aims to support many of the responsible
decisions necessary throughout iterative design, development, and dissemination
of IML systems.
