  Real-world datasets often encode stereotypes and societal biases. Such biases
can be implicitly captured by trained models, leading to biased predictions and
exacerbating existing societal preconceptions. Existing debiasing methods, such
as adversarial training and removing protected information from
representations, have been shown to reduce bias. However, a disconnect between
fairness criteria and training objectives makes it difficult to reason
theoretically about the effectiveness of different techniques. In this work, we
propose two novel training objectives which directly optimise for the
widely-used criterion of {\it equal opportunity}, and show that they are
effective in reducing bias while maintaining high performance over two
classification tasks.
