  With the rise of AI, algorithms have become better at learning underlying
patterns from the training data including ingrained social biases based on
gender, race, etc. Deployment of such algorithms to domains such as hiring,
healthcare, law enforcement, etc. has raised serious concerns about fairness,
accountability, trust and interpretability in machine learning algorithms. To
alleviate this problem, we propose D-BIAS, a visual interactive tool that
embodies human-in-the-loop AI approach for auditing and mitigating social
biases from tabular datasets. It uses a graphical causal model to represent
causal relationships among different features in the dataset and as a medium to
inject domain knowledge. A user can detect the presence of bias against a
group, say females, or a subgroup, say black females, by identifying unfair
causal relationships in the causal network and using an array of fairness
metrics. Thereafter, the user can mitigate bias by acting on the unfair causal
edges. For each interaction, say weakening/deleting a biased causal edge, the
system uses a novel method to simulate a new (debiased) dataset based on the
current causal model. Users can visually assess the impact of their
interactions on different fairness metrics, utility metrics, data distortion,
and the underlying data distribution. Once satisfied, they can download the
debiased dataset and use it for any downstream application for fairer
predictions. We evaluate D-BIAS by conducting experiments on 3 datasets and
also a formal user study. We found that D-BIAS helps reduce bias significantly
compared to the baseline debiasing approach across different fairness metrics
while incurring little data distortion and a small loss in utility. Moreover,
our human-in-the-loop based approach significantly outperforms an automated
approach on trust, interpretability and accountability.
