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Found 15 papers, 6 papers with code
Post Hoc Explanations of Language Models Can Improve Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks.
TABLET: Learning From Instructions For Tabular Data
Acquiring high-quality data is often a significant challenge in training machine learning (ML) models for tabular prediction, particularly in privacy-sensitive and costly domains like medicine and finance.
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
In real-world evaluations with humans, 73% of healthcare workers (e. g., doctors and nurses) agreed they would use TalkToModel over baseline point-and-click systems for explainability in a disease prediction task, and 85% of ML professionals agreed TalkToModel was easier to use for computing explanations.
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition
However, we identify these techniques are not well equipped for safe policy learning because they ignore negative experiences(e. g., unsafe or unsuccessful), focusing only on positive experiences, which harms their ability to generalize to new tasks safely.
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
Overall, we hope our work serves as a starting place for researchers and engineers to design interactive explainability systems.
Feature Attributions and Counterfactual Explanations Can Be Manipulated
As machine learning models are increasingly used in critical decision-making settings (e. g., healthcare, finance), there has been a growing emphasis on developing methods to explain model predictions.
On the Lack of Robust Interpretability of Neural Text Classifiers
With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models.
Counterfactual Explanations Can Be Manipulated
In this work, we introduce the first framework that describes the vulnerabilities of counterfactual explanations and shows how they can be manipulated.
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy
Each region contains a specific type of model bug; for instance, a misclassification region for an MNIST classifier contains a style of skinny 6 that the model mistakes as a 1.
Differentially Private Language Models Benefit from Public Pre-training
Language modeling is a keystone task in natural language processing.
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
In this paper, we address the aforementioned challenges by developing a novel Bayesian framework for generating local explanations along with their associated uncertainty.
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous.
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Then, we illustrate the usefulness of both algorithms as a combined method for training models from a few data points on new tasks while using Fairness Warnings as interpretable boundary conditions under which the newly trained model may not be fair.
Assessing the Local Interpretability of Machine Learning Models
Through a user study with 1, 000 participants, we test whether humans perform well on tasks that mimic the definitions of simulatability and "what if" local explainability on models that are typically considered locally interpretable.
