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"Why Should I Trust You?": Explaining the Predictions of Any Classifier
LIME is proposed, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning aninterpretable model locally varound the prediction.
Anchors: High-Precision Model-Agnostic Explanations
We introduce a novel model-agnostic system that explains the behavior of complex models with high-precision rules called anchors, representing local, "sufficient" conditions for predictions. We…
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
- Marco Tulio Ribeiro, Tongshuang Sherry Wu, Carlos Guestrin, Sameer Singh
- Computer ScienceACL
- 8 May 2020
Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models…
Model-Agnostic Interpretability of Machine Learning
This paper argues for explaining machine learning predictions using model-agnostic approaches, treating the machine learning models as black-box functions, which provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models.
Semantically Equivalent Adversarial Rules for Debugging NLP models
This work presents semantically equivalent adversaries (SEAs) – semantic-preserving perturbations that induce changes in the model’s predictions that induce adversaries on many instances that are extremely similar semantically.
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
This work conducts mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task (explaining itself in some conditions), and observes complementary improvements from AI augmentation that were not increased by explanations.
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
This work proposes anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear and is compared to linear LIME with simulated experiments, and demonstrates the flexibility of aLIME with qualitative examples from a variety of domains and tasks.
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
- Tongshuang Sherry Wu, Marco Tulio Ribeiro, Jeffrey Heer, Daniel S. Weld
- Computer ScienceACL
- 2 January 2021
Polyjuice is presented, a general-purpose counterfactual generator that allows for control over perturbation types and locations, trained by finetuning GPT-2 on multiple datasets of paired sentences.
Pareto-efficient hybridization for multi-objective recommender systems
- Marco Tulio Ribeiro, A. Lacerda, Adriano Veloso, N. Ziviani
- Computer ScienceRecSys '12
- 9 September 2012
A hybrid recommendation approach that combines existing algorithms which differ in their level of accuracy, novelty and diversity, and allows for adjusting the compromise between accuracy, diversity and novelty, so that the recommendation emphasis can be adjusted dynamically according to the needs of different users.