  The ethical decisions behind the acquisition and analysis of audio, video or
physiological human data, harnessed for (deep) machine learning algorithms, is
an increasing concern for the Artificial Intelligence (AI) community. In this
regard, herein we highlight the growing need for responsible, and
representative data collection and analysis, through a discussion of modality
diversification. Factors such as Auditability, Benchmarking, Confidence,
Data-reliance, and Explainability (ABCDE), have been touched upon within the
machine learning community, and here we lay out these ABCDE sub-categories in
relation to the acquisition and analysis of multimodal data, to weave through
the high priority ethical concerns currently under discussion for AI. To this
end, we propose how these five subcategories can be included in early planning
of such acquisition paradigms.
