Causal Structure Learning of Bias for Fair Affect Recognition

Cheong, Jiaee 
Kalkan, Sinan 

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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.

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Engineering and Physical Sciences Research Council (EP/R030782/1)
Alan Turing Institute (ATIPO000004438)
Alan Turing Institute (Unknown)
Alan Turing Institute