  The growing use of automated decision-making in critical applications, such
as crime prediction and college admission, has raised questions about fairness
in machine learning. How can we decide whether different treatments are
reasonable or discriminatory? In this paper, we investigate discrimination in
machine learning from a visual analytics perspective and propose an interactive
visualization tool, DiscriLens, to support a more comprehensive analysis. To
reveal detailed information on algorithmic discrimination, DiscriLens
identifies a collection of potentially discriminatory itemsets based on causal
modeling and classification rules mining. By combining an extended Euler
diagram with a matrix-based visualization, we develop a novel set visualization
to facilitate the exploration and interpretation of discriminatory itemsets. A
user study shows that users can interpret the visually encoded information in
DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens
provides informative guidance in understanding and reducing algorithmic
discrimination.
