Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk | Paper Citation Record

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This paper cites European Union regulations on algorith- mic decision-making and a ‘right to explanation’,.

Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk European Union regulations on algorith- mic decision-making and a ‘right to explanation’,

Reference 1

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This paper cites Benchmark- ing state-of-the-art classification algorithms for credit scoring: An update of research,.

Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Benchmark- ing state-of-the-art classification algorithms for credit scoring: An update of research,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Statistical and machine learning models in credit scoring: A systematic literature survey,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk A unified approach to interpreting model predictions,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk ‘Why should I trust you?’ Ex- plaining the predictions of any classifier,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Explainable machine learning in credit risk management,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Can formal argumentative reasoning enhance LLMs performances?

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk XAI for All: Can Large Language Models Simplify Explainable AI?

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk In-Context Explainers: Harnessing LLMs for Explaining Black Box Models

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk From XAI to stories: A factorial study of LLM-generated explanation quality,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk A Two-Stage LLM Framework for Accessible and Verified XAI Explanations

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Interpreting LLMs as credit risk classifiers: Do their feature explana- tions align with classical ML?,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Survey of hallucination in natural language generation,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk On the Robustness of Interpretability Methods

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Why do tree-based models still outperform deep learning on typical tabular data?,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Tabular data: Deep learning is not all you need,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk XGBoost: A scalable tree boosting system,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk LightGBM: A highly efficient gradient boosting decision tree,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk CatBoost: Unbiased boosting with categorical features,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Random forests,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Stacked generalization,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Deep residual learning for image recognition,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk TabNet: Attentive interpretable tabular learning,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Deep & cross network for ad click predictions,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Focal loss for dense object detection,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Adam: A method for stochastic optimization,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Batch normalization: Accelerating deep network training by reducing internal covariate shift,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Dropout: A simple way to prevent neural networks from overfit- ting,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Searching for Activation Functions

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk A value forn-person games,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Explaining prediction models and individual predictions with feature contributions,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk A survey of methods for explaining black box models,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Explainable Artificial Intelligence (XAI): Con- cepts, taxonomies, opportunities and challenges toward responsible AI,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Counterfactual explanations without opening the black box: Automated decisions and the GDPR,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk On calibration of modern neural networks,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Predicting good probabilities with supervised learning,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Equality of opportunity in super- vised learning,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk The meaning and use of the area under a receiver operating characteristic (ROC) curve,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Probable inference, the law of succession, and statistical inference,

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Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk Phi-2: The surprising power of small language models,

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