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The ethical decisions behind the acquisition and analysis of audio, video orphysiological human data, harnessed for (deep) machine learning algorithms, isan increasing concern for the Artificial Intelligence (AI) community. In thisregard, herein we highlight the growing need for responsible, andrepresentative data collection and analysis, through a discussion of modalitydiversification. Factors such as Auditability, Benchmarking, Confidence,Data-reliance, and Explainability (ABCDE), have been touched upon within themachine learning community, and here we lay out these ABCDE sub-categories inrelation to the acquisition and analysis of multimodal data, to weave throughthe high priority ethical concerns currently under discussion for AI. To thisend, we propose how these five subcategories can be included in early planningof such acquisition paradigms.

Responsible and Representative Multimodal Data Acquisition and Analysis: On Auditability, Benchmarking, Confidence, Data-Reliance & Explainability