  Feature attribution is a fundamental task in both machine learning and data
analysis, which involves determining the contribution of individual features or
variables to a model's output. This process helps identify the most important
features for predicting an outcome. The history of feature attribution methods
can be traced back to General Additive Models (GAMs), which extend linear
regression models by incorporating non-linear relationships between dependent
and independent variables. In recent years, gradient-based methods and
surrogate models have been applied to unravel complex Artificial Intelligence
(AI) systems, but these methods have limitations. GAMs tend to achieve lower
accuracy, gradient-based methods can be difficult to interpret, and surrogate
models often suffer from stability and fidelity issues. Furthermore, most
existing methods do not consider users' contexts, which can significantly
influence their preferences. To address these limitations and advance the
current state-of-the-art, we define a novel feature attribution framework
called Context-Aware Feature Attribution Through Argumentation (CA-FATA). Our
framework harnesses the power of argumentation by treating each feature as an
argument that can either support, attack or neutralize a prediction.
Additionally, CA-FATA formulates feature attribution as an argumentation
procedure, and each computation has explicit semantics, which makes it
inherently interpretable. CA-FATA also easily integrates side information, such
as users' contexts, resulting in more accurate predictions.
