270 citations · 624 across the 13 of their papers we have counts for
13 papers
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions
Manish Nagireddy, Moninder Singh, Samuel C. Hoffman +3
Ensuring trustworthiness in machine learning (ML) models is a multi-dimensional task. In addition to the traditional notion of predictive performance, other notions such as privacy…
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Dennis Wei, Rahul Nair, Amit Dhurandhar +3
Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interp…
Anomaly Attribution with Likelihood Compensation
Tsuyoshi Idé, Amit Dhurandhar, Jiří Navrátil +2
This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption…
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
Hammaad Adam, Ming Ying Yang, Kenrick Cato +6
Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpet…
Ground-Truth, Whose Truth? -- Examining the Challenges with Annotating Toxic Text Datasets
Kofi Arhin, Ioana Baldini, Dennis Wei +2
The use of machine learning (ML)-based language models (LMs) to monitor content online is on the rise. For toxic text identification, task-specific fine-tuning of these models are…
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness
Moninder Singh, Gevorg Ghalachyan, Kush R. Varshney +1
To ensure trust in AI models, it is becoming increasingly apparent that evaluation of models must be extended beyond traditional performance metrics, like accuracy, to other dimens…