Moninder Singh | DeepAI

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∙ 02/17/2023

Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions

Ensuring trustworthiness in machine learning (ML) models is a multi-dime...

0 Manish Nagireddy, et al. ∙

∙ 11/02/2022

On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Interpretable and explainable machine learning has seen a recent surge o...

0 Dennis Wei, et al. ∙

∙ 08/23/2022

Anomaly Attribution with Likelihood Compensation

This paper addresses the task of explaining anomalous predictions of a b...

0 Tsuyoshi Idé, et al. ∙

∙ 05/08/2022

Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations

Clinical notes are becoming an increasingly important data source for ma...

0 Hammaad Adam, et al. ∙

∙ 12/07/2021

Ground-Truth, Whose Truth? – Examining the Challenges with Annotating Toxic Text Datasets

The use of machine learning (ML)-based language models (LMs) to monitor ...

5 Kofi Arhin, et al. ∙

∙ 09/29/2021

An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness

To ensure trust in AI models, it is becoming increasingly apparent that ...

0 Moninder Singh, et al. ∙

∙ 09/24/2021

AI Explainability 360: Impact and Design

As artificial intelligence and machine learning algorithms become increa...

46 Vijay Arya, et al. ∙

∙ 08/03/2021

Your fairness may vary: Group fairness of pretrained language models in toxic text classification

We study the performance-fairness trade-off in more than a dozen fine-tu...

0 Ioana Baldini, et al. ∙

∙ 11/04/2019

Understanding racial bias in health using the Medical Expenditure Panel Survey data

Over the years, several studies have demonstrated that there exist signi...

0 Moninder Singh, et al. ∙

∙ 09/06/2019

One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques

As artificial intelligence and machine learning algorithms make further ...

36 Vijay Arya, et al. ∙

∙ 10/03/2018

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

Fairness is an increasingly important concern as machine learning models...

12 Rachel K. E. Bellamy, et al. ∙

∙ 09/21/2018

Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration

Autonomous cyber-physical agents and systems play an increasingly large ...

16 Ritesh Noothigattu, et al. ∙

∙ 03/06/2013

An Algorithm for the Construction of Bayesian Network Structures from Data

Previous algorithms for the construction of Bayesian belief network stru...

0 Moninder Singh, et al. ∙

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