  A pressing issue in the adoption of AI models is the increasing demand for
more human-centric explanations of their predictions. To advance towards more
human-centric explanations, understanding how humans produce and select
explanations has been beneficial. In this work, inspired by insights of human
cognition we propose and test the incorporation of two novel biases to enhance
the search for effective counterfactual explanations. Central to our
methodology is the application of diffusion distance, which emphasizes data
connectivity and actionability in the search for feasible counterfactual
explanations. In particular, diffusion distance effectively weights more those
points that are more interconnected by numerous short-length paths. This
approach brings closely connected points nearer to each other, identifying a
feasible path between them. We also introduce a directional coherence term that
allows the expression of a preference for the alignment between the joint and
marginal directional changes in feature space to reach a counterfactual. This
term enables the generation of counterfactual explanations that align with a
set of marginal predictions based on expectations of how the outcome of the
model varies by changing one feature at a time. We evaluate our method, named
Coherent Directional Counterfactual Explainer (CoDiCE), and the impact of the
two novel biases against existing methods such as DiCE, FACE, Prototypes, and
Growing Spheres. Through a series of ablation experiments on both synthetic and
real datasets with continuous and mixed-type features, we demonstrate the
effectiveness of our method.
