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.

提出并测试了两种新的偏差，以增强对有效逆因对照解释的搜寻，方法为应用扩散距离和引入定向一致性项；通过在合成和实际数据集上进行一系列消融实验，证明了该方法的有效性。

用扩散距离和方向连贯性增强反事实解释搜索

Enhancing Counterfactual Explanation Search with Diffusion Distance and  Directional Coherence

This paper introduces a novel, non-deterministic method for embedding data in
low-dimensional Euclidean space based on computing realizations of a Gaussian
process depending on the geometry of the data. This type of embedding first
appeared in (Adler et al, 2018) as a theoretical model for a generic manifold
in high dimensions.
In particular, we take the covariance function of the Gaussian process to be
the heat kernel, and computing the embedding amounts to sketching a matrix
representing the heat kernel. The Karhunen-Lo\`eve expansion reveals that the
straight-line distances in the embedding approximate the diffusion distance in
a probabilistic sense, avoiding the need for sharp cutoffs and maintaining some
of the smaller-scale structure.
Our method demonstrates further advantage in its robustness to outliers. We
justify the approach with both theory and experiments.

引入一种新的非确定性方法，将数据嵌入低维欧几里德空间，该方法基于依赖于数据几何的高斯过程的实现，通过计算高斯过程的实现来计算嵌入，其中高斯过程的协方差函数被取为热核函数，嵌入的直线距离以概率意义上逼近扩散距离，避免了对距离进行尖锐截断并保留了一定的较小尺度结构，此方法还具有对异常值的鲁棒性，并通过理论和实验证明了该方法的优势。