Local surrogate explainers vary in objectives, leading to incomparable explanations.
problem Variability in objectives among local surrogate explainers.
method Review of multiple local surrogate explainers, focusing on extracted information.
result Diverse explanations from similar methods due to differing objectives.
Proposes MOC method for better counterfactual explanations in ML models.
problem Difficulties in balancing multiple objectives for counterfactual explanations.
method Translates counterfactual search into a multi-objective optimization problem.
result Returns diverse counterfactuals with different trade-offs and maintains feature diversity.
ADS explains object differences by quantifying and removing underlying properties.
problem Explaining differences between two object images.
method Align-Deform-Subtract (ADS) framework that uses semantic alignments and iterative quantification/removal of differences.
result ADS provides disentangled error measures explaining object differences in terms of underlying properties.
Multi-SpaCE generates valid counterfactual explanations for multivariate time series data.
problem Lack of transparency in deep learning models for multivariate time series data.
method Multi-objective counterfactual explanation method using NSGA-II for multivariate time series data.
result Ensures perfect validity and superior performance compared to existing methods.
Attack reveals model details from counterfactual explanations.
problem Extracting model details from counterfactual explanations.
method Adversary uses counterfactual explanations to build high-fidelity model.
result High-fidelity and high-accuracy model extraction possible.
Proposes minimal interventions over counterfactual explanations for algorithmic recourse.
problem Lack of actionable recommendations for algorithmic recourse.
method Causal reasoning to shift focus from explanations to recommendations.
result Minimal interventions provide more actionable recommendations for recourse.
Develops causal explanations for black-box classifiers.
problem Creating understandable explanations for complex models.
method Generative model with information-theoretic measures of causal influence.
result Learned latent factors influence classifier outputs.
This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges. The core challenge is that people usually cannot obtain ground-truth explanations of the neural network. To …
G-SHAP generates multiple types of explanations for machine learning models.
problem Understanding model predictions and their differences across groups.
method Generalization of SHAP method to produce additional types of explanations.
result G-SHAP produces explanations for classification, intergroup differences, and model failure.
Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based e…
We consider objective evaluation measures of saliency explanations for complex black-box machine learning models. We propose simple robust variants of two notions that have been considered in recent literature: (in)fidelity, and sensitivity. We analyze optimal explanations with respect to both these measures, and while…
Proposes counterfactual explanations for deep two-sample tests on high-dimensional data.
problem Limited interpretability of deep two-sample tests on high-dimensional data.
method Combines diffusion autoencoder and pretrained deep two-sample test model to generate counterfactuals.
result Counterfactual transformations increase p-values, indicating closer distribution similarity.
GRANITE unifies feature-based explanation methods to reduce disagreement.
problem Disagreement among feature-based explanation methods.
method GRANITE partitions feature space into regions minimizing interaction and distribution influences.
result Unified and consistent feature explanations.
Paper compares two local explanation methods for machine learning models.
problem Comparing two local explanation methods for machine learning models.
method Integrated Gradients and Baseline Shapley methods.
result Additional insights on comparative behavior for tabular data and neural networks.
Proposes an efficient method for ordered counterfactual explanations.
problem Insufficient explanation of perturbation vectors for executing actions.
method Mixed-Integer Linear Optimization (MILP) approach for evaluating and extracting optimal pairs of actions and orders.
result Demonstrated effectiveness of the proposed method on real datasets.
New method identifies differences between groups in low-dimensional data representations.
problem Identifying meaningful differences between groups in low-dimensional data representations.
method Introduce Global Counterfactual Explanation (GCE) and Transitive Global Translations (TGT) for computing GCEs.
result TGT identifies sparse, accurate explanations that match real data patterns.
AXE evaluates explanations to avoid misleading Rashomon set model selection.
problem Evaluating explanations for Rashomon set models to avoid false selection.
method Proposed AXE method to evaluate explanation quality.
result AXE detects adversarial fairwashing with 100% success rate.
The paper introduces metrics to objectively evaluate interpretability methods.
problem Lack of objective evaluation metrics for interpretability methods.
method Proposes a set of metrics to evaluate interpretability methods along simplicity and broadness.
result Validated metrics on different benchmark tasks and showed their utility in method selection.
In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as anomalies the theory of rational finance cannot explain: Predictability of asset returns, The Equity Premium, (The Volatility Puzzle. We offer resolutions of those objections within the rational finance…
For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective. Too often, the litany of proposed explainable deep learning methods stop at the first step, providing practitioners with insight into a model, but…
Recent work in model-agnostic explanations of black-box machine learning has demonstrated that interpretability of complex models does not have to come at the cost of accuracy or model flexibility. However, it is not clear what kind of explanations, such as linear models, decision trees, and rule lists, are the appropr…
FeAT improves OOD generalization by learning richer features.
problem Improving feature learning for out-of-distribution (OOD) generalization.
method Feature Augmented Training (FeAT) iteratively augments and retains features from different subsets of training data.
result FeAT effectively learns richer features, boosting OOD performance.
COMRECGC finds common recourse for global counterfactual explanations in GNNs.
problem Finding common recourse for global counterfactual explanations in GNNs.
method Formalized the common recourse explanation problem and designed COMRECGC algorithm.
result COMRECGC outperforms strong baselines on four real-world graph datasets.
New method evaluates visual explanations of deep models using adversarial perturbations.
problem Lack of objective evaluation of visual explanations of deep models.
method Proposes an adversarial perturbation approach to evaluate visual explanations of deep models.
result Demonstrates the effectiveness of the proposed approach through comparisons with existing methods.
New research challenges the idea that counterfactual explanations should be sparse.
problem Predictive multiplicity leads to multiple models giving almost equal solutions.
method Derive a general upper bound for counterfactual costs under multiplicity and compare sparse vs. data support approaches.
result Data support methods are more robust to multiplicity but have higher counterfactual costs.
Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models. An instance of explanations are counterfactual explanations which provide an intuitive and useful explanations of machine learning models. In this su…
Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis. In these problems, the ability to explain how the attributes affect the prediction is critical to users. However, most, if not all, existing ordi…
Proposes a model for generating survival trajectories and data.
problem Generating survival trajectories and data for new feature vectors.
method Autoencoder with specific structure, incorporating weighting scheme for robustness.
result Generates a prototype time-dependent trajectory for objects.
RAW-Explainer generates interpretable subgraph explanations for link predictions in knowledge graphs.
problem Interpreting GNN predictions for link prediction in heterogeneous settings is challenging.
method RAW-Explainer uses random walk objective and neural network to generate connected, concise subgraph explanations.
result RAW-Explainer strikes a balance between explanation quality and computational efficiency.
Managing large-scale transportation infrastructure projects is difficult due to frequent misinformation about the costs which results in large cost overruns that often threaten the overall project viability. This paper investigates the explanations for cost overruns that are given in the literature. Overall, four categ…
Paper explores a consumer-friendly approach to explain machine learning decisions.
problem Challenges in providing understandable explanations for machine learning predictions.
method Consumer-driven approach called TED that asks for explanations in training data.
result TED is robust to increasing numbers of explanations, noisy explanations, and missing explanations.
Interpretability is an elusive but highly sought-after characteristic of modern machine learning methods. Recent work has focused on interpretability via explanations, which justify individual model predictions. In this work, we take a step towards reconciling machine explanations with those that humans prod…
Introduces PCG for better counterfactual explanations in vision models.
problem Ambiguity in latent-space optimization methods for counterfactual explanations.
method Constructs counterfactuals by tracing geodesics under a perceptually Riemannian metric.
result PCG outperforms baselines and reveals hidden failure modes.
Unified framework for feature-based explanations using ANOVA and game theory.
problem Differences between feature-based explanations methods limit their applicability.
method Introduces a unified framework combining fANOVA and cooperative game theory.
result Uncovered similarities and differences between various explanation techniques.
Interpretation and explanation of deep models is critical towards wide adoption of systems that rely on them. In this paper, we propose a novel scheme for both interpretation as well as explanation in which, given a pretrained model, we automatically identify internal features relevant for the set of classes considered…
Develops transparent global models consistent with local explanations.
problem Creating globally interpretable models that align with local explanations from black-box models.
method Custom boolean features from sparse local contrastive explanations are used to train a globally transparent model.
result Custom transparent models have higher local consistency compared to other strategies.
Explearn learns to explain predictions using Gaussian Processes.
problem Learning to explain predictions effectively.
method Gaussian Processes-based contextual bandits.
result Guaranteed convergence with high probability.
R package for counterfactual explanation methods.
problem Lack of unified interfaces for counterfactual explanation methods.
method Developed a modular R6-based interface for three existing counterfactual methods and proposed extensions.
result Comparison of implemented methods' quality and runtime behavior.
This research examines how model explanations change under distribution shifts in tabular data.
problem Detecting distribution shifts in tabular data affecting model performance and explanations.
method Investigates the relationship between model performance and explanation characteristics under distribution shifts.
result Explanation shifts are a better indicator for detecting predictive performance changes than traditional distribution shift techniques.
In the past years, many new explanation methods have been proposed to achieve interpretability of machine learning predictions. However, the utility of these methods in practical applications has not been researched extensively. In this paper we present the results of a human-grounded evaluation of SHAP, an explanation…
Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are significantly preferred to help people better understand how machine learning systems work and further enhance their trust towards systems. Howeve…
GraphOpt learns the formation mechanism of graphs from observed structures.
problem Learning formation mechanisms from observed graphs with complex structural properties.
method GraphOpt uses maximum entropy inverse reinforcement learning to solve the link formation problem in a sequential decision-making process.
result GraphOpt discovers a latent objective function that can explain and transfer across different graphs.
Hybrid Deep Embedding for aspect-level explanations in recommendations.
problem Challenges in personalization, dynamic explanations, and aspect-level granularity in recommendation systems.
method Proposes Hybrid Deep Embedding (HDE) to learn dynamic embeddings for user and item preferences, and aspect-level quality vectors.
result Demonstrates improved recommending performance and dynamic aspect-level explanations.
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE. CLE gives an faithful and interpretable explanation to the prediction, by approximating the model locally using an interpretable model. We demonstrate the flexibility of CLE by explaining different models…
OrphicX generates causal explanations for GNNs by isolating latent causal factors.
problem Generating interpretable causal explanations for complex graph neural networks.
method Develops a generative model and objective function to isolate latent causal factors, maximizing information flow.
result OrphicX effectively identifies causal semantics, significantly outperforming alternatives.
The paper tackles strategic behavior in decision-making with counterfactual explanations.
problem Finding optimal counterfactual explanations and policies in a strategic setting.
method NP-hard problem, greedy algorithm, submodularity, randomized algorithm, matroid constraint.
result Optimal counterfactual explanations and policies increase utility.
Interactive explanations improve machine learning transparency.
problem Transparency of machine learning predictions for diverse stakeholders.
method Personalized counterfactual explanations and follow-up questions.
result Improved understanding of black-box systems through interactive explanations.
Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques …