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Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
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Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine learning based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations, a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.
Forward citations
Cited by 14 Pith papers
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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations
BTTF optimizes the initial noise of an image-to-video diffusion model using the target classifier's gradients to produce minimal counterfactual videos that explain video classifiers.
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DCFO: Density-Based Counterfactuals for Outliers -- Additional Material
DCFO partitions the feature space by nearest-neighbour structure to make LOF scores differentiable, then uses gradient-based search to find the closest change that turns an outlier into an inlier.
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Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
A plug-in contrastive loss that enforces monotonicity between numerical features and recommender outputs, via counterfactual sample synthesis, improves AUC, GAUC, and monotonicity.
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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation
LLM-generated counterfactual code pairs with flipped vulnerability labels, used to train a GNN, sharply improve CWE-20 detection and attribution on the released CWE-20-CFA benchmark.
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Are machine learning interpretations reliable? A stability study on global interpretations
Popular machine learning interpretation methods are frequently unstable under small data perturbations, and interpretation stability does not track prediction accuracy.
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AULLM++: Structured-Token-Conditioned Large Language Models for Micro-Expression Action Unit Detection
AULLM++ fuses multi-granularity visual tokens with FACS-prior AU graph instructions into an LLM prompt and uses counterfactual consistency training to improve micro-expression AU detection and cross-domain Macro-F1.
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Negotiating Risk Boundaries in AI for Policing Through Mixed-Stakeholder Deliberation
A mixed-stakeholder UK workshop rated 13 AI policing use cases, rejecting recidivism risk assessment outright while accepting most others conditionally, and found that a racial-equity focus broadened, not narrowed, th...
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Inferring Effects of Major Events through Discontinuity Forecasting of Population Anxiety
Discontinuity forecasting predicts a county's anxiety jump and slope change after a major event from pre-event trends, reaching out-of-sample correlations of about .76 and .87.
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XplainAct: Visualization for Personalized Intervention Insights
XplainAct combines choropleth maps, LIME/SHAP local explanations, and nearest-neighbor subgrouping to simulate and interpret personalized interventions at the county level.
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Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Explainable AI research should prioritize definitions, properties, evaluations, and actionability over new ad-hoc methods, on evidence from 617 papers and 34 practitioners.
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Model-Free Counterfactual Subset Selection at Scale
A one-pass streaming algorithm selects a diverse, relevant subset of real examples as counterfactual explanations, with a claimed 1/5.585 approximation guarantee and O(log k) update time.
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Pareto Optimal Algorithmic Recourse in Multi-cost Function
A Bellman-Ford-style dynamic program over an actionability graph returns all Pareto-optimal recourse paths for multiple non-differentiable metric costs, with an epsilon-net sampling scheme proposed for scalability.
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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability
A survey of robustness and explainability methods for digital health AI, proposing a taxonomy and illustrating known XAI tools, without new empirical or theoretical results.
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CoFE: A Framework Generating Counterfactual ECG for Explainable Cardiac AI-Diagnostics
CoFE edits ECG signals in a StyleGAN2 latent space so that an AI model changes its prediction, and the resulting feature changes match clinical signs for AF and hyperkalemia.
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