Causal Structure Learning of Bias for Fair Affect Recognition

Creative Commons License

Cheong J., KALKAN S., Gunes H.

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023, Hawaii, United States Of America, 3 - 07 January 2023, pp.340-349 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/wacvw58289.2023.00038
  • City: Hawaii
  • Country: United States Of America
  • Page Numbers: pp.340-349
  • Middle East Technical University Affiliated: Yes


The problem of bias in facial affect recognition tools can lead to severe consequences and issues. It has been posited that causality is able to address the gaps induced by the associational nature of traditional machine learning, and one such gap is that of fairness. However, given the nascency of the field, there is still no clear mapping between tools in causality and applications in fair machine learning for the specific task of affect recognition. To address this gap, we provide the first causal structure formalisation of the different biases that can arise in affect recognition. We conducted a proof of concept on utilising causal structure learning for the post-hoc understanding and analysing bias.