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...
share
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...
research
∙
02/27/2023
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse
Machine learning (ML) recourse techniques are increasingly used in high-...
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...
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...
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...
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...
research
∙
08/04/2022
Interpretable Distribution Shift Detection using Optimal Transport
We propose a method to identify and characterize distribution shifts in ...
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...
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...
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...
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...
research
∙
12/06/2021
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Recent strides in interpretable machine learning (ML) research reveal th...
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...
research
∙
03/10/2021
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Several pieces of work have uncovered performance disparities by conduct...
research
∙
01/05/2021
Incentive-Compatible Forecasting Competitions
We initiate the study of incentive-compatible forecasting competitions i...
research
∙
07/07/2020
Mathematical Foundations for Social Computing
Social computing encompasses the mechanisms through which people interac...
research
∙
05/19/2020
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits
Online learning algorithms, widely used to power search and content opti...
research
∙
02/20/2020
No-Regret and Incentive-Compatible Online Learning
We study online learning settings in which experts act strategically to ...
research
∙
10/29/2019
Weight of Evidence as a Basis for Human-Oriented Explanations
Interpretability is an elusive but highly sought-after characteristic of...
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...
research
∙
05/01/2019
Truthful Aggregation of Budget Proposals
We consider a participatory budgeting problem in which each voter submit...
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...
research
∙
08/27/2018
The Disparate Effects of Strategic Manipulation
When consequential decisions are informed by algorithmic input, individu...
research
∙
08/27/2018
The Disparate Effects of Strategic Classification
When consequential decisions are informed by algorithmic input, individu...
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...
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...
research
∙
03/23/2018
Datasheets for Datasets
Currently there is no standard way to identify how a dataset was created...
research
∙
02/21/2018
Manipulating and Measuring Model Interpretability
Despite a growing body of research focused on creating interpretable mac...
research
∙
02/24/2016
The Possibilities and Limitations of Private Prediction Markets
We consider the design of private prediction markets, financial markets ...
research
∙
07/30/2014
Market Making with Decreasing Utility for Information
We study information elicitation in cost-function-based combinatorial pr...
research
∙
02/26/2010