Halal products sales in non-Muslim countries like Europe grew significantly in 2010.
problem Understanding the growth and motives behind halal products in non-Muslim economies.
method Analyzing sales data and market trends.
result Halal products sales in non-Muslim countries have been growing rapidly since 2010.
Religious adherence reduces corporate greenwashing behavior.
problem Greenwashing behavior by corporations.
method Analysis of a large US firm sample (2005-2019), focusing on selective disclosure.
result Religious adherence correlates with lower greenwashing behavior.
This paper evaluates debiasing methods on word embeddings to reduce religious bias.
problem Social biases persist in word embeddings, potentially amplifying them in AI applications.
method Investigates and evaluates three multiclass debiasing techniques on three word embeddings.
result ConceptorDebiasing is the most effective method, reducing religious bias by 82-96%.
In the framework of applying econophysics ideas in religious topics, the finances of the Antoinist religious movement organized in Belgium between 1920 and 2000 are studied. The interest of investigating financial aspects of such a, sometimes called, sect stems in finding characteristics of conditions and mechanisms un…
Population growth (or decay) in a country can be due to various f socio-economic constraints, as demonstrated in this paper. For example, sexual intercourse is banned in various religions, during Nativity and Lent fasting periods. Data consisting of registered daily birth records for very long (35,429 points) time seri…
Model predicts personality traits from Facebook Likes.
problem Predicting personality traits from social media data.
method Mapped Facebook Likes to page categories, used machine learning.
result 83% accuracy distinguishing religious vs non-religious.
System identifies related passages for easier text comprehension.
problem Expensive and limited cross-reference resources for complex texts.
method Fine-grained topic modeling to find topically related verse pairs.
result System can produce cost-effective cross-references.
Research designs first model for Amharic handwritten character recognition.
problem No existing model for Amharic handwritten character recognition.
method Used a convolutional neural network and applied data augmentation and multi-task learning.
result Promising results observed from the enhanced model.
This study examines gender bias in Dutch newspapers from 1950-1990 using word embeddings.
problem Examining gender bias in historical newspapers.
method Word embeddings to measure bias changes over time.
result Clear differences in gender bias and changes within newspapers over time.
Among econophysics investigations, studies of religious groups have been of interest. On one hand, the present paper concerns the Antoinist community financial reports, - a community which appeared at the end of the 19-th century in Belgium. Several growth-decay regimes have been previously found over different time sp…
What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, g…
Study analyzes global public sentiment on DeFi from 2012-2022.
problem Global public sentiment on DeFi is understudied.
method Sentiment analysis, spatial econometrics, clustering, topic modeling.
result Economic development significantly influences DeFi engagement, especially after 2015.
Research analyzes public opinions on Twitter about diabetes, diet, exercise, and obesity.
problem Understanding public health opinions on social media.
method Developed a multi-component semantic and linguistic framework to collect and analyze Twitter data.
result Strongest correlation found between exercise and obesity; other notable correlations between diabetes and obesity, diet and obesity.
New definition of interpretability makes model design more actionable.
problem Current definitions of interpretability are not actionable and inform users poorly.
method Proposes a new definition of interpretability that is general, simple, and actionable.
result New definition reveals necessary properties for designing interpretable models.
New method improves neural network interpretability against adversarial attacks.
problem Adversarial attacks can hide from neural network interpretability methods.
method Develops an interpretability-aware defensive scheme promoting robust interpretation.
result Achieves both robust classification and robust interpretation.
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.
VALC provides concept-level interpretations of FLMs, overcoming word-level limitations.
problem Lack of higher-level structure interpretation in FLMs' attention weights.
method Formal definition of conceptual interpretation, variational Bayesian framework (VALC).
result VALC finds optimal language concepts for FLM predictions, providing concept-level interpretations.
The paper refines the discourse on interpretability of machine learning models.
problem The ambiguity and diversity of motivations for interpretability in machine learning models.
method Examining motivations, model properties, and techniques thought to confer interpretability.
result Identifies transparency and post-hoc explanations as competing notions of interpretability.
Interpreting machine learning models helps understand adversarial attacks and defenses.
problem Understanding model vulnerability to adversarial attacks.
method Model interpretation techniques to explore adversarial attacks and defenses.
result Interpretation methods can be applied to adversarial attacks and defenses.
New framework improves interpretability of trainable prompts.
problem Improving task-specific LLM performance with soft prompts remains a black-box method.
method Developed a theoretical framework for evaluating interpretability of trainable prompts, inspired new objective functions.
result Found a fundamental trade-off between interpretability and task performance in trainable prompts.
Proposes a decision-theoretic approach for enhancing model interpretability in Bayesian frameworks.
problem Challenges the traditional approach of restricting model structure for interpretability in Bayesian frameworks.
method Introduces an interpretability utility function and a two-step method involving a reference model and a proxy model.
result Demonstrates that the proposed method generates more accurate models with the same level of interpretability.
Defines interpretability in machine learning and calls for rigorous evaluation.
problem Lack of consensus on what interpretability is and how to measure it.
method Defines interpretability, suggests a taxonomy for rigorous evaluation, and identifies open questions.
result Calls for a more rigorous science of interpretable machine learning.
Optimizes model interpretability by involving humans in the loop.
problem Optimizing for interpretability while maintaining model accuracy.
method Develops an algorithm that minimizes user studies to find interpretable models.
result Human subjects results show varying preferences for interpretability proxies across different datasets.
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.
Model-agnostic interpretation methods can mislead if not used carefully.
problem Misinterpretation of machine learning models due to improper use of techniques.
method General pitfalls of model-agnostic interpretation methods.
result Many pitfalls exist when using global interpretation techniques for machine learning models.
New framework learns interpretable rule ensembles without sacrificing accuracy.
problem Trade-off between accuracy and interpretability in rule ensembles.
method Introduces local interpretability and a regularizer to promote it, using coordinate descent with local search.
result Learns rule ensembles with fewer rules to explain individual predictions, maintaining comparable accuracy.
Study finds machine learning interpretations are often unstable and unreliable.
problem Reliability of machine learning interpretations in high-stakes domains.
method Stability study on global interpretations using tabular data.
result Popular interpretation methods are frequently unstable, less stable than predictions, and not associated with prediction accuracy.
Regularizes black-box models to improve interpretability.
problem Improving interpretability of black-box models without sacrificing accuracy.
method Regularizes a black-box model at training time to connect model explainability, explanation system, and quality metrics.
result Substantial improvement in explanation fidelity and stability across various datasets and explanation systems, with slight accuracy trade-off.
InterpretML simplifies machine learning interpretability for users and researchers.
problem Making machine learning models understandable to non-experts.
method Unified Python package exposing interpretability algorithms and visualization.
result First implementation of Explainable Boosting Machine, a powerful, interpretable model.
Interprets deep CNN models via meta-learning.
problem Non-interpretable deep CNN models in machine learning.
method Meta-learning approach using clustering and Random Forest.
result Achieves global interpretation without sacrificing accuracy.
Meta-learning approach to learn interpretable models from human feedback.
problem Tackling the challenge of making machine learning models interpretable.
method A meta-learning approach where a model of non-trivial proxies of human interpretability is learned from human feedback, then incorporated into the ML training process to optimize for interpretability.
result The approach leads to formulas that are either significantly more or equally accurate while being more interpretable.
This paper optimizes clustering interpretability by balancing value and user-defined features.
problem Generating interpretable clusters in graph data.
method Proposes a β-interpretable clustering algorithm that ensures at least β fraction of nodes share the same feature value.
result Empirical demonstration of the benefits of the proposed approaches in generating interpretable clusters.
Boosted trees improve reinforcement learning solutions that are easy to understand.
problem Creating accurate reinforcement learning solutions that are also easy to understand.
method Using boosted regression trees to combine multiple regression trees.
result Boosted regression trees produce solutions that are as accurate as other methods but are also easy to understand.
We create interpretable word embeddings through sparse coding.
problem Difficult to interpret word embeddings in natural language processing.
method Transform pretrained dense word embeddings into sparse embeddings through sparse coding.
result Sparse embeddings are more interpretable and achieve good performance.
Certifiable defense method improves robustness of deep learning interpretation.
problem Vulnerability of gradient-based saliency maps to adversarial attacks.
method Sparsified SmoothGrad method, extending certifiably robust smooth classifier bounds.
result Sparsified SmoothGrad method is certifiably robust against adversarial perturbations.
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.
Paper defines interpretability for brain decoding models.
problem Lack of formal definition and quantitative measure for interpretability in brain decoding.
method Proposes a simple definition and combines interpretability with performance into a new criterion for model selection.
result Optimizing hyper-parameters based on the new criterion yields more informative linear models.
Simplifies complex regression model interpretation.
problem Interpreting complex machine learning models.
method A method for grounding interpretations in actual learning examples.
result Validated on academic and industrial regression tasks.
New method learns to encode predictions within interpretations, improving evaluation.
problem Need for interpretable machine learning, but existing methods are slow or lack fidelity.
method Amortized explanation methods that learn a global selector model optimizing fidelity of interpretations.
result Predictions can be encoded within interpretations, detected by EVAL-X.
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.
LIMIS improves locally interpretable models by selecting and distilling key instances.
problem Low fidelity of locally interpretable models.
method LIMIS uses instance-wise subsampling guided by policy gradient and reward to improve fidelity.
result LIMIS near-matches black-box model accuracy while significantly improving fidelity.
Visualization aids but distorts intuitive understanding of machine learning models.
problem The intuitive notion of interpretability in machine learning is flawed.
method Analysis of visualization techniques and their impact on interpretability.
result Visualization can both facilitate and hinder intuitive understanding of machine learning models.
GMT improves interpretability of XGNNs by approximating SubMT.
problem Limited understanding of existing interpretable subgraph learning methods.
method Formulated subgraph multilinear extension (SubMT) and designed GMT architecture.
result GMT outperforms state-of-the-art in both interpretability and generalizability.
Paper proposes a method to improve interpretability in kernel learning models.
problem Improving interpretability in flexible machine learning models.
method Proposes a quantitative index for interpretability and a universal learning framework to balance interpretability and generalization performance.
result Demonstrates a method to achieve global optimal solution in balancing interpretability and generalization performance.
Automated feature recommendation for signal analytics with interpretability.
problem Signal data analytics problems requiring feature interpretation.
method Wide Learning architecture for feature recommendation with interpretability.
result Effective feature recommendation and interpretation techniques for signal analytics.
A theory for interpreting black-box models in medical diagnostics.
problem Lack of computational formulation for interpreting black-box models in medical diagnostics.
method Defining interpretation as a finite communication between a known model and a black-box model, deriving an algorithm for diagnostic interpretability.
result Demonstrated the feasibility of interpreting black-box models in synthetic supervised classification scenarios.
XDeep interprets deep neural networks for practitioners and researchers.
problem Understanding and interpreting deep neural networks.
method Post-hoc interpretation algorithms integrated into XDeep.
result XDeep provides local and global explanations for deep models.
The paper simplifies complex tree ensembles for better interpretability.
problem Limited interpretability of tree ensembles like random forest and boosted trees.
method A post-processing method that approximates complex tree ensembles with a simpler, interpretable model using the EM algorithm.
result Complex tree ensembles can be approximated reasonably by simpler, interpretable models.