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Found 13 papers, 6 papers with code
Diagnosing AI Explanation Methods with Folk Concepts of Behavior
When explaining AI behavior to humans, how is the communicated information being comprehended by the human explainee, and does it match what the explanation attempted to communicate?
Human Interpretation of Saliency-based Explanation Over Text
In this work, we focus on this question through a study of saliency-based explanations over textual data.
Contrastive Explanations for Model Interpretability
Our method is based on projecting model representation to a latent space that captures only the features that are useful (to the model) to differentiate two potential decisions.
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i. e., trust between people).
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data
The process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior.
Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals
In this work, we point out the inability to infer behavioral conclusions from probing results and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded.
Aligning Faithful Interpretations with their Social Attribution
We find that the requirement of model interpretations to be faithful is vague and incomplete.
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems.
Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning
We consider the situation in which a user has collected a small set of documents on a cohesive topic, and they want to retrieve additional documents on this topic from a large collection.
Learning and Understanding Different Categories of Sexism Using Convolutional Neural Network's Filters
Sexism is very common in social media and makes the boundaries of free speech tighter for female users.
Neural network gradient-based learning of black-box function interfaces
We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions.
Understanding Convolutional Neural Networks for Text Classification
We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text.
Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application.
