DAFT models attention as a dynamical system to make neural networks more interpretable.
problem Uninterpretable features learned by neural networks without human priors.
method DAFT models attention as a continuous dynamical system using neural ODEs.
result DAFT reduces the number of reasoning steps while maintaining similar performance.
The paper defines a mathematical framework for measuring model interpretability.
problem Improving trust and understanding in machine learning models for complex decisions.
method Constructing interpretable steps in a sequence for various models, generalizing to a family of consistent measures.
result A formal definition of interpretability allows quantifying the tradeoff with predictive accuracy.
Interpretability of ML models improves healthcare decisions.
problem Ensuring machine learning models are understandable for healthcare users.
method Classifying interpretability into local and global approaches, and model-specific vs. model-agnostic methods.
result Examples of practical interpretability in healthcare, including prediction and treatment optimization.
It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing pr…
Proposes a model to interpret complex ML algorithms.
problem Complex ML models are hard to interpret.
method Uses model-based regression trees and interpretable main-effects models.
result Surrogate model provides interpretable results with good predictive performance.
The workshop focuses on AI principles for structured data.
problem Using AI on structured data for decision-making.
method Addressing principles of privacy, accountability, interpretability, robustness, and reasoning.
result Designing approaches to use structured data for reliable decisions.
Survey on principles and challenges of interpretable machine learning.
problem Improving machine learning models' interpretability for high-stakes decisions.
method Identification and analysis of 10 technical challenges in interpretable machine learning.
result Identification of 10 technical challenges in interpretable machine learning.
The paper addresses interpretability issues in EBM models by improving feature selection and reducing spurious interactions.
problem Interpretability issues in EBM models, especially spurious interactions and single feature dominance.
method Alternate Cross-feature selection, ensemble features, and model configuration alteration techniques.
result Our approach improves interpretability and predictive performance of EBM models, reducing spurious interactions and single feature dominance.
Interpretable ML methods for better decision-making with explanations.
problem Lack of transparency in black-box ML models.
method Use of Formal Concept Analysis and cooperative game theory to assess attribute importance and reduce attribute count.
result Developed methods to assess attribute importance and reduce attribute count in ML models.
Geometric framework detects concept frustration between human concepts and machine representations.
problem Aligning human concepts with machine learning representations.
method Geometric framework and similarity measures for detecting concept frustration.
result Concept frustration affects machine learning model performance and reorganizes learned concept representations.
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying t…
Selective neural network improves credit risk prediction while maintaining interpretability.
problem Improving credit risk prediction accuracy while maintaining interpretability for financial regulators.
method Introducing a neural network with a selective option to distinguish between linear and non-linear datasets.
result For most datasets, logistic regression is sufficient and interpretable, while for specific data portions, a shallow neural network model provides better accuracy.
MonoNet enforces monotonicity to improve model interpretability.
problem Lack of interpretability in complex machine learning models.
method Enforces monotonicity between features and outputs in deep learning models.
result Enforcing monotonicity allows for better understanding of model predictions.
Machine learning basics: key principles and limitations.
problem Understanding machine learning principles and their limitations.
method Analysis of machine learning families, performance comparison, and model interpretation.
result Interpretable models are often sufficient and deep learning doesn't always outperform others.
Machine learning models have become more and more complex in order to better approximate complex functions. Although fruitful in many domains, the added complexity has come at the cost of model interpretability. The once popular k-nearest neighbors (kNN) approach, which finds and uses the most similar data for reasonin…
Improves deep learning interpretability through logical network construction.
problem Difficulty in understanding and explaining deep neural networks, especially convolution neural networks.
method Proposes a method to construct a logical network based on fully connected layers to improve interpretability.
result Improves the interpretability of deep learning processes and models.
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
problem Existing feature importance methods ignore or hide feature dependencies.
method Shapley Flow considers the entire causal graph and assigns credit to edges.
result Shapley Flow provides a deeper, graph-based view of feature importance.
The paper offers a checklist for comparing human and machine visual perception.
problem Comparing human and machine visual perception.
method Designing, conducting, and interpreting experiments to investigate mechanisms.
result Feedback mechanisms may not be necessary for visual reasoning tasks.
Neural networks currently dominate the machine learning community and they do so for good reasons. Their accuracy on complex tasks such as image classification is unrivaled at the moment and with recent improvements they are reasonably easy to train. Nevertheless, neural networks are lacking robustness and interpretabi…
Study evaluates local explanation methods for time series forecasting.
problem Lack of local interpretability methods for multivariate time series forecasting.
method Proposed two novel evaluation metrics: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold.
result Comprehensive comparison of local explanation models on two datasets.
Machine learning models predict the behavior of negatively buoyant jets from wastewater.
problem Minimizing harmful effects of negatively buoyant jets during wastewater discharge.
method Training machine learning models (ANN, XGBoost, CatBoost, LightGBM) on OpenFOAM simulations and experimental data.
result Artificial Neural Network provided the best prediction with R2 0.98 and RMSE 0.28.
Defines explainability as reasoning under background knowledge.
problem Lack of agreed definitions in explainable AI.
method Reviews philosophical and social foundations, translates to tech realm.
result Defines explainability as logical reasoning under background knowledge.
Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning. In support of these goals, researchers evaluate model explanation performance using humans and real world applications. This alone presents a challenge in many areas of artificial intelligence. In this …
Calls to arms to build interpretable models express a well-founded discomfort with machine learning. Should a software agent that does not even know what a loan is decide who qualifies for one? Indeed, we ought to be cautious about injecting machine learning (or anything else, for that matter) into applications where t…
Study on trade-offs between accuracy and interpretability in machine learning.
problem Lack of formal study on statistical cost of interpretability.
method Modeling interpretability as a constraint in empirical risk minimization for binary classification.
result Explains conditions under which accuracy trade-off occurs with interpretability constraints.
Study proposes a data-driven CBR system for improved bankruptcy prediction.
problem Lack of interpretability in machine learning models for bankruptcy prediction.
method Data-driven explainable case-based reasoning (CBR) system.
result Proposed CBR system outperforms existing CBR and machine learning models.
VTA combines verbal and latent reasoning for accurate stock time-series forecasts.
problem Challenges in combining textual analysis with time-series data for financial forecasting.
method Converts stock price data into textual annotations, optimizes reasoning trace using inverse MSE, conditions time-series model outputs on reasoning attributes.
result VTA achieves state-of-the-art forecasting accuracy and interpretable reasoning traces.
Interpretability has arisen as a key desideratum of machine learning models alongside performance. Approaches so far have been primarily concerned with fixed dimensional inputs emphasizing feature relevance or selection. In contrast, we focus on temporal modeling and the problem of tailoring the predictor, functionally…
Improves interpretability of anomaly scores in GBRBM-based detection.
problem Difficulty in setting a proper threshold for anomaly scores.
method Proposes a measure based on cumulative distribution and uses simulated annealing for evaluation.
result Established a guideline for setting the threshold using the interpretable measure.
This paper explores how to interpret machine learning models in business process analytics.
problem The lack of interpretability in machine learning models used for predictive process analytics.
method Derives explanations using interpretable machine learning techniques to compare and contrast predictive models.
result Highlights scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data.
This work identifies and mitigates reasoning shortcuts in Neuro-Symbolic models.
problem Neuro-Symbolic models can achieve high accuracy by using unintended concepts.
method Characterized reasoning shortcuts as unintended optima of the learning objective and identified four key conditions.
result Reasoning shortcuts are difficult to mitigate, casting doubt on NeSy solutions' trustworthiness and interpretability.
A new method combines DNN with simpler models for better interpretability and accuracy.
problem Combining interpretability and accuracy in machine learning models.
method A mixture of linear models guided by deep neural networks.
result Improved interpretability without sacrificing accuracy.
DCR improves interpretability of concept-based models by using neural networks to build rule structures.
problem Inability of concept-based models to provide transparent decision processes.
method DCR uses neural networks to build syntactic rule structures using concept embeddings and executes these rules on concept truth degrees.
result DCR improves interpretability by up to 25% on challenging benchmarks and discovers meaningful logic rules.
This paper explores explaining GBDT2NN predictions by its teacher model, improving distillation performance.
problem Explaining GBDT2NN predictions when the models have different structures.
method Empirical study on new approach to explain GBDT2NN predictions and use it as an auxiliary learning task.
result Proposed methods achieve better performance on both explanations and predictions.
Machine Learning benefits from prior information and computational power for better performance and understanding.
problem Improper use of Machine Learning methods leads to lack of understanding and performance issues.
method Employing prior information and computational power to solve learning problems, emphasizing interpretability and performance.
result Combining prior information and computational power can lead to better understanding and performance in Machine Learning.
MAC Net is a compositional attention network designed for Visual Question Answering. We propose a modified MAC net architecture for Natural Language Question Answering. Question Answering typically requires Language Understanding and multi-step Reasoning. MAC net's unique architecture - the separation between memory an…
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
problem Improving accuracy of default probabilities and loan portfolio loss distributions in mortgage credit risk.
method Combines tree-boosting with a latent spatio-temporal Gaussian process model.
result Predictive models outperform conventional methods due to non-linear and spatio-temporal effects.
Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and i…
A new explainable CBR system predicts financial risks with interpretability and good performance.
problem Predicting financial risks with interpretability and good performance.
method A novel explainable case-based reasoning (CBR) approach.
result The CBR system provides a good prediction performance and interpretability.
Study examines explainable machine learning for monotonic models, finding Integrated gradients better for strong monotonicity.
problem Applying explainable machine learning to science-informed models.
method Proposed axioms for monotonicity, tested Shapley value and Integrated gradients methods.
result Integrated gradients provides better explanations for strong monotonicity.
Proposes NLRL for enhancing neural networks' interpretability.
problem Deep neural networks lack interpretability for humans.
method Introduces neural logic rule layers (NLRL) to represent arbitrary logic rules.
result NLRL-enhanced neural networks can learn complex logic and arithmetic.
Bayesian attention modules improve model interpretability and performance.
problem Deterministic attention modules limit model interpretability and optimization.
method Proposes a scalable stochastic attention module using simplex-constrained distributions and Bayesian learning.
result Consistent improvements over baselines in various attention-based models.
BRACE generates efficient counterfactual explanations by integrating causal reasoning.
problem Challenges in traditional counterfactual explanations, especially neglecting causal relationships.
method Backtracking counterfactuals with causal reasoning.
result Our method provides deeper insights into model outputs and is computationally efficient.
Combining feature importance estimates improves reliability of machine learning predictions.
problem Lack of consensus on feature importance quantification makes explanations unreliable.
method Proposes a feature importance fusion framework combining multiple quantifiers.
result Feature importance ensembles reduce prediction error by 15%.
Predicts academic risk in college students using interpretable machine learning.
problem Predicting academic risk from high-dimensional, unbalanced student data.
method Binary classification task using LightGBM model and Shapley value.
result 8 predictors for academic risk identified, including quality of academic partners and dormitory study atmosphere.
ECSEL learns signomial equations for explainable classification.
problem Creating interpretable models for classification.
method ECSEL constructs signomial equations directly for classification and explanation.
result ECSEL outperforms state-of-the-art methods in interpretability and efficiency.
Proposes a copula-based method to interpret neural networks.
problem Neural networks lack interpretability due to ambiguity in decision-making.
method Uses pair copula correlation coefficients to reveal feature importance and decision paths.
result Consistent results with Random Forest's feature importance values.
Machine learning is usually defined in behaviourist terms, where external validation is the primary mechanism of learning. In this paper, I argue for a more holistic interpretation in which finding more probable, efficient and abstract representations is as central to learning as performance. In other words, machine le…