  Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due
to skewed datasets problematise the application of prediction models in
practice. Representation bias is a prevalent form of bias found in the majority
of datasets. This bias arises when training data inadequately represents
certain segments of the data space, resulting in poor generalisation of
prediction models. Despite AI practitioners employing various methods to
mitigate representation bias, their effectiveness is often limited due to a
lack of thorough domain knowledge. To address this limitation, this paper
introduces human-in-the-loop interaction approaches for representation
debiasing of generated data involving domain experts. Our work advocates for a
controlled data generation process involving domain experts to effectively
mitigate the effects of representation bias. We argue that domain experts can
leverage their expertise to assess how representation bias affects prediction
models. Moreover, our interaction approaches can facilitate domain experts in
steering data augmentation algorithms to produce debiased augmented data and
validate or refine the generated samples to reduce representation bias. We also
discuss how these approaches can be leveraged for designing and developing
user-centred AI systems to mitigate the impact of representation bias through
effective collaboration between domain experts and AI.
