  The debate about the ethical implications of Artificial Intelligence dates
from the 1960s. However, in recent years symbolic AI has been complemented and
sometimes replaced by Neural Networks and Machine Learning techniques. This has
vastly increased its potential utility and impact on society, with the
consequence that the ethical debate has gone mainstream. Such debate has
primarily focused on principles - the what of AI ethics - rather than on
practices, the how. Awareness of the potential issues is increasing at a fast
rate, but the AI community's ability to take action to mitigate the associated
risks is still at its infancy. Therefore, our intention in presenting this
research is to contribute to closing the gap between principles and practices
by constructing a typology that may help practically-minded developers apply
ethics at each stage of the pipeline, and to signal to researchers where
further work is needed. The focus is exclusively on Machine Learning, but it is
hoped that the results of this research may be easily applicable to other
branches of AI. The article outlines the research method for creating this
typology, the initial findings, and provides a summary of future research
needs.
