  As algorithmic decision-making systems become more prevalent in society,
ensuring the fairness of these systems is becoming increasingly important.
Whilst there has been substantial research in building fair algorithmic
decision-making systems, the majority of these methods require access to the
training data, including personal characteristics, and are not transparent
regarding which individuals are classified unfairly. In this paper, we propose
a novel model-agnostic argumentation-based method to determine why an
individual is classified differently in comparison to similar individuals. Our
method uses a quantitative argumentation framework to represent attribute-value
pairs of an individual and of those similar to them, and uses a well-known
semantics to identify the attribute-value pairs in the individual contributing
most to their different classification. We evaluate our method on two datasets
commonly used in the fairness literature and illustrate its effectiveness in
the identification of bias.
