Bias mitigation techniques in image classification: fair machine learning in human heritage collections
This work addresses fairness issues in automated classification for cultural heritage collections, which is an incremental improvement in applying existing methods to a specific domain.
The study tackled bias in gender classification for human heritage collections by evaluating three bias mitigation techniques with Xception and EfficientNet neural networks, finding that transfer learning combined with threshold change, re-weighting, and image augmentation achieved the fairest classifier.
A major problem with using automated classification systems is that if they are not engineered correctly and with fairness considerations, they could be detrimental to certain populations. Furthermore, while engineers have developed cutting-edge technologies for image classification, there is still a gap in the application of these models in human heritage collections, where data sets usually consist of low-quality pictures of people with diverse ethnicity, gender, and age. In this work, we evaluate three bias mitigation techniques using two state-of-the-art neural networks, Xception and EfficientNet, for gender classification. Moreover, we explore the use of transfer learning using a fair data set to overcome the training data scarcity. We evaluated the effectiveness of the bias mitigation pipeline on a cultural heritage collection of photographs from the 19th and 20th centuries, and we used the FairFace data set for the transfer learning experiments. After the evaluation, we found that transfer learning is a good technique that allows better performance when working with a small data set. Moreover, the fairest classifier was found to be accomplished using transfer learning, threshold change, re-weighting and image augmentation as bias mitigation methods.