---
_id: '51372'
abstract:
- lang: eng
  text: Machine learning is frequently used in affective computing, but presents challenges
    due the opacity of state-of-the-art machine learning methods. Because of the impact
    affective machine learning systems may have on an individual's life, it is important
    that models be made transparent to detect and mitigate biased decision making.
    In this regard, affective machine learning could benefit from the recent advancements
    in explainable artificial intelligence (XAI) research. We perform a structured
    literature review to examine the use of interpretability in the context of affective
    machine learning. We focus on studies using audio, visual, or audiovisual data
    for model training and identified 29 research articles. Our findings show an emergence
    of the use of interpretability methods in the last five years. However, their
    use is currently limited regarding the range of methods used, the depth of evaluations,
    and the consideration of use-cases. We outline the main gaps in the research and
    provide recommendations for researchers that aim to implement interpretable methods
    for affective machine learning.
author:
- first_name: 'David '
  full_name: 'Johnson, David '
  last_name: Johnson
- first_name: Olya
  full_name: Hakobyan, Olya
  last_name: Hakobyan
- first_name: Hanna
  full_name: Drimalla, Hanna
  last_name: Drimalla
citation:
  ama: 'Johnson D, Hakobyan O, Drimalla H. Towards Interpretability in Audio and Visual
    Affective Machine Learning: A Review. Published online 2023.'
  apa: 'Johnson, D., Hakobyan, O., &#38; Drimalla, H. (2023). <i>Towards Interpretability
    in Audio and Visual Affective Machine Learning: A Review</i>.'
  bibtex: '@article{Johnson_Hakobyan_Drimalla_2023, title={Towards Interpretability
    in Audio and Visual Affective Machine Learning: A Review}, author={Johnson, David  and
    Hakobyan, Olya and Drimalla, Hanna}, year={2023} }'
  chicago: 'Johnson, David , Olya Hakobyan, and Hanna Drimalla. “Towards Interpretability
    in Audio and Visual Affective Machine Learning: A Review,” 2023.'
  ieee: 'D. Johnson, O. Hakobyan, and H. Drimalla, “Towards Interpretability in Audio
    and Visual Affective Machine Learning: A Review.” 2023.'
  mla: 'Johnson, David, et al. <i>Towards Interpretability in Audio and Visual Affective
    Machine Learning: A Review</i>. 2023.'
  short: D. Johnson, O. Hakobyan, H. Drimalla, (2023).
date_created: 2024-02-18T10:52:36Z
date_updated: 2024-02-26T08:43:01Z
department:
- _id: '660'
language:
- iso: eng
project:
- _id: '110'
  name: 'TRR 318 - A: TRR 318 - Project Area A'
status: public
title: 'Towards Interpretability in Audio and Visual Affective Machine Learning: A
  Review'
type: preprint
user_id: '54779'
year: '2023'
...