  In this position paper, we present five key principles, namely
interpretability, inherent capability to explain, independent data, interactive
learning, and inquisitiveness, for the development of conversational AI that,
unlike the currently popular black box approaches, is transparent and
accountable. At present, there is a growing concern with the use of black box
statistical language models: While displaying impressive average performance,
such systems are also prone to occasional spectacular failures, for which there
is no clear remedy. In an effort to initiate a discussion on possible
alternatives, we outline and exemplify how our five principles enable the
development of conversational AI systems that are transparent and thus safer
for use. We also present some of the challenges inherent in the implementation
of those principles.
