Detection and Evaluation of bias-inducing Features in Machine learning
Moses Openja Gabriel Laberge Foutse Khomh
The cause-to-effect analysis can help us decompose all the likely causes of a
problem, such as an undesirable business situation or unintended harm to the
individual(s). This implies that we can identify how the problems are
inherited, rank the causes to help prioritize fixes, simplify a complex problem
and visualize them. In the context of machine learning (ML), one can use
cause-to-effect analysis to understand the reason for the biased behavior of
the system. For example, we can examine the root causes of biases by checking
each feature for a potential cause of bias in the model. To approach this, one
can apply small changes to a given feature or a pair of features in the data,
following some guidelines and observing how it impacts the decision made by the
model (i.e., model prediction). Therefore, we can use cause-to-effect analysis
to identify the potential bias-inducing features, even when these features are
originally are unknown. This is important since most current methods require a
pre-identification of sensitive features for bias assessment and can actually
miss other relevant bias-inducing features, which is why systematic
identification of such features is necessary. Moreover, it often occurs that to
achieve an equitable outcome, one has to take into account sensitive features
in the model decision. Therefore, it should be up to the domain experts to
decide based on their knowledge of the context of a decision whether bias
induced by specific features is acceptable or not. In this study, we propose an
approach for systematically identifying all bias-inducing features of a model
to help support the decision-making of domain experts. We evaluated our
technique using four well-known datasets to showcase how our contribution can
help spearhead the standard procedure when developing, testing, maintaining,
and deploying fair/equitable machine learning systems.