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

## TL;DR

This paper emphasizes the importance of responsible, ethical, and representative multimodal data collection and analysis in AI, focusing on auditability, benchmarking, confidence, data-reliance, and explainability.

## Contribution

It introduces the ABCDE framework for integrating ethical considerations into multimodal data acquisition and analysis planning in AI.

## Key findings

- Highlights ethical concerns in multimodal data collection.
- Proposes the ABCDE framework for responsible AI data practices.
- Suggests integrating ABCDE into early data acquisition planning.

## Abstract

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.