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Found 15 papers, 2 papers with code
Interpretable Differencing of Machine Learning Models
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model with new training data.
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language
The goal of this work is to ensure that machine learning and deep learning-based systems are as trusted as traditional software.
Plug and Play Counterfactual Text Generation for Model Robustness
We also show that the generated counterfactuals from CASPer can be used for augmenting the training data and thereby fixing and making the test model more robust.
Explainable Data Imputation using Constraints
Data values in a dataset can be missing or anomalous due to mishandling or human error.
FROTE: Feedback Rule-Driven Oversampling for Editing Models
However, in such scenarios, it may take time for sufficient training data to accumulate in order to retrain the model to reflect the new decision boundaries.
Data Synthesis for Testing Black-Box Machine Learning Models
The increasing usage of machine learning models raises the question of the reliability of these models.
Automated Testing of AI Models
The last decade has seen tremendous progress in AI technology and applications.
Towards API Testing Across Cloud and Edge
API economy is driving the digital transformation of business applications across the hybrid Cloud and edge environments.
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets
We attempt to re-look at the data quality issues in the context of building a machine learning pipeline and build a tool that can detect, explain and remediate issues in the data, and systematically and automatically capture all the changes applied to the data.
Testing Framework for Black-box AI Models
With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge.
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text
Aligned with this, we propose a framework GYC, to generate a set of counterfactual text samples, which are crucial for testing these ML systems.
Verifying Individual Fairness in Machine Learning Models
Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem.
Bias Mitigation Post-processing for Individual and Group Fairness
Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Automated Test Generation to Detect Individual Discrimination in AI Models
In this paper, we present an automated technique to generate test inputs, which is geared towards finding individual discrimination.
