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Causal Structure Learning of Bias for Fair Affect Recognition
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Date
2023-01-01
Author
Cheong, Jiaee
Kalkan, Sinan
Gunes, Hatice
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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.
URI
https://hdl.handle.net/11511/102760
DOI
https://doi.org/10.1109/wacvw58289.2023.00038
Conference Name
2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023
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Department of Computer Engineering, Conference / Seminar
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J. Cheong, S. Kalkan, and H. Gunes, “Causal Structure Learning of Bias for Fair Affect Recognition,” presented at the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023, Hawaii, Amerika Birleşik Devletleri, 2023, Accessed: 00, 2023. [Online]. Available: https://hdl.handle.net/11511/102760.