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

arXiv: https://arxiv.org/abs/1903.07171 · HTML: https://mlsanity.com/paper/1903.07171/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)

## Abstract (arXiv)

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.