21 citations · 22 across the 3 of their papers we have counts for
5 papers
Explainable Data Imputation using Constraints
Sandeep Hans, Diptikalyan Saha, Aniya Aggarwal
Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analys…
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…
Automated Test Generation to Detect Individual Discrimination in AI Models
Aniya Agarwal, Pranay Lohia, Seema Nagar +2
Dependability on AI models is of utmost importance to ensure full acceptance of the AI systems. One of the key aspects of the dependable AI system is to ensure that all its decisio…