10 citations · 30 across the 5 of their papers we have counts for
6 papers
Pitfalls of Explainable ML: An Industry Perspective
Sahil Verma, Aditya Lahiri, John P. Dickerson +1
As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountability is of utmost…
Counterfactual Explanations for Machine Learning: Challenges Revisited
Sahil Verma, John Dickerson, Keegan Hines
Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models. They provide ``what if'' feedback of the form `…
Removing biased data to improve fairness and accuracy
Sahil Verma, Michael Ernst, Rene Just
Machine learning systems are often trained using data collected from historical decisions. If past decisions were biased, then automated systems that learn from historical data wil…
ShapeFlow: Dynamic Shape Interpreter for TensorFlow
Sahil Verma, Zhendong Su
We present ShapeFlow, a dynamic abstract interpreter for TensorFlow which quickly catches tensor shape incompatibility errors, one of the most common bugs in deep learning code. Sh…
Facets of Fairness in Search and Recommendation
Sahil Verma, Ruoyuan Gao, Chirag Shah
Several recent works have highlighted how search and recommender systems exhibit bias along different dimensions. Counteracting this bias and bringing a certain amount of fairness…
Benchmarking Symbolic Execution Using Constraint Problems -- Initial Results
Sahil Verma, Roland H. C. Yap
Symbolic execution is a powerful technique for bug finding and program testing. It is successful in finding bugs in real-world code. The core reasoning techniques use constraint so…