The Dos and Don'ts of Affect Analysis

As an inseparable and crucial component of communication affects play a substantial role in human-device and human-human interaction. They convey information about a person's specific traits and states [1, 4, 5], how one feels about the aims of a conversation, the trustworthiness of one's verbal communication [3], and the degree of adaptation in interpersonal speech [2]. This multifaceted nature of human affects poses a great challenge when it comes to applying machine learning systems for their automatic recognition and understanding. Contemporary self-supervised learning architectures such as Transformers, which define state-of-the-art (SOTA) in this area, have shown noticeable deficits in terms of explainability, while more conventional, non-deep machine learning methods, which provide more transparency, often fall (far) behind SOTA systems. So, is it possible to get the best of these two 'worlds'? And more importantly, at what price? In this talk, I provide a set of Dos and Don'ts guidelines for addressing affective computing tasks w. r. t. (i) preserving privacy for affective data and individuals/groups, (ii) being efficient in computing such data in a transparent way, (iii) ensuring reproducibility of the results, (iv) knowing the differences between causation and correlation, and (v) properly applying social and ethical protocols.