  Artificial Intelligence has the potential to exacerbate societal bias and set
back decades of advances in equal rights and civil liberty. Data used to train
machine learning algorithms may capture social injustices, inequality or
discriminatory attitudes that may be learned and perpetuated in society.
Attempts to address this issue are rapidly emerging from different perspectives
involving technical solutions, social justice and data governance measures.
While each of these approaches are essential to the development of a
comprehensive solution, often discourse associated with each seems disparate.
This paper reviews ongoing work to ensure data justice, fairness and bias
mitigation in AI systems from different domains exploring the interrelated
dynamics of each and examining whether the inevitability of bias in AI training
data may in fact be used for social good. We highlight the complexity
associated with defining policies for dealing with bias. We also consider
technical challenges in addressing issues of societal bias.
