Jennifer Wortman Vaughan

research

∙ 06/05/2023

Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment

We present the NeurIPS 2021 consistency experiment, a larger-scale varia...

0 Alina Beygelzimer, et al. ∙

research

∙ 06/02/2023

AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap

The rise of powerful large language models (LLMs) brings about tremendou...

0 Q Vera Liao, et al. ∙

research

∙ 02/27/2023

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Machine learning (ML) recourse techniques are increasingly used in high-...

0 Zijie J. Wang, et al. ∙

research

∙ 02/21/2023

Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience

Despite the widespread use of artificial intelligence (AI), designing us...

0 Q Vera Liao, et al. ∙

research

∙ 02/14/2023

Generation Probabilities Are Not Enough: Exploring the Effectiveness of Uncertainty Highlighting in AI-Powered Code Completions

Large-scale generative models enabled the development of AI-powered code...

0 Helena Vasconcelos, et al. ∙

research

∙ 01/18/2023

Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations

AI explanations are often mentioned as a way to improve human-AI decisio...

0 Valerie Chen, et al. ∙

research

∙ 11/22/2022

How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?

How do author perceptions match up to the outcomes of the peer-review pr...

0 Charvi Rastogi, et al. ∙

research

∙ 08/04/2022

Interpretable Distribution Shift Detection using Optimal Transport

We propose a method to identify and characterize distribution shifts in ...

29 Neha Hulkund, et al. ∙

research

∙ 06/30/2022

Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values

Machine learning (ML) interpretability techniques can reveal undesirable...

0 Zijie J. Wang, et al. ∙

research

∙ 06/06/2022

Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata

Data is central to the development and evaluation of machine learning (M...

5 Amy Heger, et al. ∙

research

∙ 05/05/2022

REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research

Transparency around limitations can improve the scientific rigor of rese...

0 Jessie J. Smith, et al. ∙

research

∙ 12/10/2021

Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support

Various tools and practices have been developed to support practitioners...

0 Michael Madaio, et al. ∙

research

∙ 12/06/2021

GAM Changer: Editing Generalized Additive Models with Interactive Visualization

Recent strides in interpretable machine learning (ML) research reveal th...

0 Zijie J. Wang, et al. ∙

research

∙ 04/27/2021

A Human-Centered Interpretability Framework Based on Weight of Evidence

In this paper, we take a human-centered approach to interpretable machin...

16 David Alvarez-Melis, et al. ∙

research

∙ 03/10/2021

Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs

Several pieces of work have uncovered performance disparities by conduct...

5 Solon Barocas, et al. ∙

research

∙ 01/05/2021

Incentive-Compatible Forecasting Competitions

We initiate the study of incentive-compatible forecasting competitions i...

0 Jens Witkowski, et al. ∙

research

∙ 07/07/2020

Mathematical Foundations for Social Computing

Social computing encompasses the mechanisms through which people interac...

0 Yiling Chen, et al. ∙

research

∙ 05/19/2020

Greedy Algorithm almost Dominates in Smoothed Contextual Bandits

Online learning algorithms, widely used to power search and content opti...

9 Manish Raghavan, et al. ∙

research

∙ 02/20/2020

No-Regret and Incentive-Compatible Online Learning

We study online learning settings in which experts act strategically to ...

0 Rupert Freeman, et al. ∙

research

∙ 10/29/2019

Weight of Evidence as a Basis for Human-Oriented Explanations

Interpretability is an elusive but highly sought-after characteristic of...

53 David Alvarez-Melis, et al. ∙

research

∙ 07/04/2019

Toward Fairness in AI for People with Disabilities: A Research Roadmap

AI technologies have the potential to dramatically impact the lives of p...

6 Anhong Guo, et al. ∙

research

∙ 05/01/2019

Truthful Aggregation of Budget Proposals

We consider a participatory budgeting problem in which each voter submit...

0 Rupert Freeman, et al. ∙

research

∙ 12/13/2018

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequi...

0 Kenneth Holstein, et al. ∙

research

∙ 08/27/2018

The Disparate Effects of Strategic Manipulation

When consequential decisions are informed by algorithmic input, individu...

0 Lily Hu, et al. ∙

research

∙ 08/27/2018

The Disparate Effects of Strategic Classification

When consequential decisions are informed by algorithmic input, individu...

0 Lily Hu, et al. ∙

research

∙ 06/14/2018

Using Search Queries to Understand Health Information Needs in Africa

The lack of comprehensive, high-quality health data in developing nation...

0 Rediet Abebe, et al. ∙

research

∙ 06/01/2018

The Externalities of Exploration and How Data Diversity Helps Exploitation

Online learning algorithms, widely used to power search and content opti...

0 Manish Raghavan, et al. ∙

research

∙ 03/23/2018

Datasheets for Datasets

Currently there is no standard way to identify how a dataset was created...

0 Timnit Gebru, et al. ∙

research

∙ 02/21/2018

Manipulating and Measuring Model Interpretability

Despite a growing body of research focused on creating interpretable mac...

0 Forough Poursabzi-Sangdeh, et al. ∙

research

∙ 02/24/2016

The Possibilities and Limitations of Private Prediction Markets

We consider the design of private prediction markets, financial markets ...

0 Rachel Cummings, et al. ∙

research

∙ 07/30/2014

Market Making with Decreasing Utility for Information

We study information elicitation in cost-function-based combinatorial pr...

0 Miroslav Dudík, et al. ∙

research

∙ 02/26/2010

A New Understanding of Prediction Markets Via No-Regret Learning

We explore the striking mathematical connections that exist between mark...

0 Yiling Chen, et al. ∙

Jennifer Wortman Vaughan

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