Kush R. Varshney

research

∙ 09/10/2023

Decolonial AI Alignment: Viśesadharma, Argument, and Artistic Expression

Prior work has explicated the coloniality of artificial intelligence (AI...

0 Kush R. Varshney, et al. ∙

research

∙ 05/22/2023

Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models

Auditing unwanted social bias in language models (LMs) is inherently har...

6 Ioana Baldini, et al. ∙

research

∙ 04/02/2023

Towards Healthy AI: Large Language Models Need Therapists Too

Recent advances in large language models (LLMs) have led to the developm...

8 Baihan Lin, et al. ∙

research

∙ 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. ∙

research

∙ 12/13/2022

Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting

In consequential decision-making applications, mitigating unwanted biase...

0 Prasanna Sattigeri, et al. ∙

research

∙ 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. ∙

research

∙ 10/13/2022

Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models

We introduce equi-tuning, a novel fine-tuning method that transforms (po...

5 Sourya Basu, et al. ∙

research

∙ 08/22/2022

Minimax AUC Fairness: Efficient Algorithm with Provable Convergence

The use of machine learning models in consequential decision making ofte...

0 Zhenhuan Yang, et al. ∙

research

∙ 08/02/2022

Humble Machines: Attending to the Underappreciated Costs of Misplaced Distrust

It is curious that AI increasingly outperforms human decision makers, ye...

0 Bran Knowles, et al. ∙

research

∙ 01/22/2022

Differentially Private SGDA for Minimax Problems

Stochastic gradient descent ascent (SGDA) and its variants have been the...

0 Zhenhuan Yang, et al. ∙

research

∙ 10/20/2021

Human-Centered Explainable AI (XAI): From Algorithms to User Experiences

As a technical sub-field of artificial intelligence (AI), explainable AI...

44 Q Vera Liao, et al. ∙

research

∙ 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. ∙

research

∙ 09/24/2021

AI Explainability 360: Impact and Design

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

46 Vijay Arya, et al. ∙

research

∙ 08/18/2021

Towards Interpreting Zoonotic Potential of Betacoronavirus Sequences With Attention

Current methods for viral discovery target evolutionarily conserved prot...

8 Kahini Wadhawan, et al. ∙

research

∙ 06/17/2021

Biomedical Interpretable Entity Representations

Pre-trained language models induce dense entity representations that off...

0 Diego Garcia-Olano, et al. ∙

research

∙ 06/02/2021

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

In this paper, we describe an open source Python toolkit named Uncertain...

107 Soumya Ghosh, et al. ∙

research

∙ 04/09/2021

Automated Meta-Analysis: A Causal Learning Perspective

Meta-analysis is a systematic approach for understanding a phenomenon by...

0 Lu Cheng, et al. ∙

research

∙ 02/03/2021

Insiders and Outsiders in Research on Machine Learning and Society

A subset of machine learning research intersects with societal issues, i...

0 Yu Tao, et al. ∙

research

∙ 01/29/2021

Disparate Impact Diminishes Consumer Trust Even for Advantaged Users

Systems aiming to aid consumers in their decision-making (e.g., by imple...

0 Tim Draws, et al. ∙

research

∙ 12/22/2020

Learning to Initialize Gradient Descent Using Gradient Descent

Non-convex optimization problems are challenging to solve; the success a...

0 Kartik Ahuja, et al. ∙

research

∙ 10/30/2020

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

Recently, invariant risk minimization (IRM) was proposed as a promising ...

5 Kartik Ahuja, et al. ∙

research

∙ 10/15/2020

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

Several strands of research have aimed to bridge the gap between artific...

0 Charvi Rastogi, et al. ∙

research

∙ 06/19/2020

Trust and Transparency in Contact Tracing Applications

The global outbreak of COVID-19 has led to focus on efforts to manage an...

0 Stacy Hobson, et al. ∙

research

∙ 06/10/2020

Fair Data Integration

The use of machine learning (ML) in high-stakes societal decisions has e...

39 Sainyam Galhotra, et al. ∙

research

∙ 02/05/2020

Joint Optimization of AI Fairness and Utility: A Human-Centered Approach

Today, AI is increasingly being used in many high-stakes decision-making...

10 Yunfeng Zhang, et al. ∙

research

∙ 11/18/2019

Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies

More than 200 generic drugs approved by the U.S. Food and Drug Administr...

30 Shivashankar Subramanian, et al. ∙

research

∙ 11/11/2019

Experiences with Improving the Transparency of AI Models and Services

AI models and services are used in a growing number of highstakes areas,...

0 Michael Hind, et al. ∙

research

∙ 11/09/2019

Preservation of Anomalous Subgroups On Machine Learning Transformed Data

In this paper, we investigate the effect of machine learning based anony...

18 Samuel C. Maina, et al. ∙

research

∙ 10/30/2019

DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning

We introduce a framework for dynamic adversarial discovery of informatio...

26 Michiel A. Bakker, et al. ∙

research

∙ 10/29/2019

Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets

Recent advances in computer vision and deep learning have led to breakth...

36 Newton M. Kinyanjui, et al. ∙

research

∙ 10/17/2019

An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy

Our goal is to understand the so-called trade-off between fairness and a...

29 Sanghamitra Dutta, et al. ∙

research

∙ 09/08/2019

How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?

In recent years there has been an increasing trend in which data scienti...

72 Yaoli Mao, et al. ∙

research

∙ 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. ∙

research

∙ 07/09/2019

Characterization of Overlap in Observational Studies

Overlap between treatment groups is required for nonparametric estimatio...

5 Fredrik D. Johansson, et al. ∙

research

∙ 06/05/2019

Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning

Using machine learning in high-stakes applications often requires predic...

4 Noel C. F. Codella, et al. ∙

research

∙ 05/27/2019

Open Platforms for Artificial Intelligence for Social Good: Common Patterns as a Pathway to True Impact

The AI for social good movement has now reached a state in which a large...

0 Kush R. Varshney, et al. ∙

research

∙ 05/08/2019

Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

The dearth of prescribing guidelines for physicians is one key driver of...

11 Chirag Nagpal, et al. ∙

research

∙ 12/14/2018

Bias Mitigation Post-processing for Individual and Group Fairness

Whereas previous post-processing approaches for increasing the fairness ...

0 Pranay K. Lohia, et al. ∙

research

∙ 11/30/2018

Understanding Unequal Gender Classification Accuracy from Face Images

Recent work shows unequal performance of commercial face classification ...

0 Vidya Muthukumar, et al. ∙

research

∙ 11/12/2018

TED: Teaching AI to Explain its Decisions

Artificial intelligence systems are being increasingly deployed due to t...

12 Noel C. F. Codella, et al. ∙

research

∙ 11/03/2018

SimplerVoice: A Key Message & Visual Description Generator System for Illiteracy

We introduce SimplerVoice: a key message and visual description generato...

2 Minh N. B. Nguyen, et al. ∙

research

∙ 10/25/2018

Promoting Distributed Trust in Machine Learning and Computational Simulation via a Blockchain Network

Policy decisions are increasingly dependent on the outcomes of simulatio...

0 Nelson Kibichii Bore, et al. ∙

research

∙ 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. ∙

research

∙ 09/22/2018

Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach

Large-scale computational experiments, often running over weeks and over...

12 Ravi Kiran Raman, et al. ∙

research

∙ 08/22/2018

Increasing Trust in AI Services through Supplier's Declarations of Conformity

The accuracy and reliability of machine learning algorithms are an impor...

0 Michael Hind, et al. ∙

research

∙ 07/16/2018

Teaching machines to understand data science code by semantic enrichment of dataflow graphs

Your computer is continuously executing programs, but does it really und...

2 Evan Patterson, et al. ∙

research

∙ 07/03/2018

Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018)

This is the Proceedings of the 2018 ICML Workshop on Human Interpretabil...

2 Been Kim, et al. ∙

research

∙ 06/25/2018

Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory

As artificial intelligence is increasingly affecting all parts of societ...

0 Kush R. Varshney, et al. ∙

research

∙ 06/14/2018

Financial Forecasting and Analysis for Low-Wage Workers

Despite the plethora of financial services and products on the market no...

0 Wenyu Zhang, et al. ∙

research

∙ 05/29/2018

Teaching Meaningful Explanations

The adoption of machine learning in high-stakes applications such as hea...

0 Noel C. F. Codella, et al. ∙

Kush R. Varshney

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