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Found 6 papers, 4 papers with code
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown.
Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing
Instead of generating a response from scratch, P2-Net generates system responses by paraphrasing template-based responses.
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans.
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target, both based on Monte Carlo simulations.
Global Aggregations of Local Explanations for Black Box models
We present Global Aggregations of Local Explanations (GALE) with the objective to provide insights in a model's global decision making process.
Explaining Predictions from Tree-based Boosting Ensembles
Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community.
