265 citations · 369 across the 19 of their papers we have counts for
8 papers · 1 filter
Interpretable Differencing of Machine Learning Models
Swagatam Haldar, Diptikalyan Saha, Dennis Wei +2
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…
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language
Nishtha Madaan, Adithya Manjunatha, Hrithik Nambiar +4
Machine learning and deep learning-based decision making has become part of today's software. The goal of this work is to ensure that machine learning and deep learning-based syste…
FROTE: Feedback Rule-Driven Oversampling for Editing Models
Öznur Alkan, Dennis Wei, Massimiliano Mattetti +3
Machine learning models may involve decision boundaries that change over time due to updates to rules and regulations, such as in loan approvals or claims management. However, in s…
Data Synthesis for Testing Black-Box Machine Learning Models
Diptikalyan Saha, Aniya Aggarwal, Sandeep Hans
The increasing usage of machine learning models raises the question of the reliability of these models. The current practice of testing with limited data is often insufficient. In…
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets
Nitin Gupta, Hima Patel, Shazia Afzal +10
The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Various tools and techniques are available that assess data qua…
Testing Framework for Black-box AI Models
Aniya Aggarwal, Samiulla Shaikh, Sandeep Hans +3
With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-to-end gen…
Verifying Individual Fairness in Machine Learning Models
Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha
We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or un…
Bias Mitigation Post-processing for Individual and Group Fairness
Pranay K. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide +3
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 in…