research
∙
05/31/2023
Designing Closed-Loop Models for Task Allocation
Automatically assigning tasks to people is challenging because human per...
share
research
∙
07/06/2022
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
When using medical images for diagnosis, either by clinicians or artific...
research
∙
06/25/2022
Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases
As machine learning (ML) systems become increasingly widespread, it is n...
research
∙
06/06/2022
A Human-Centric Take on Model Monitoring
Predictive models are increasingly used to make various consequential de...
research
∙
04/09/2022
Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks
We show that deep neural networks that satisfy demographic parity do so ...
research
∙
03/21/2022
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Data-driven methods that detect anomalies in times series data are ubiqu...
research
∙
02/17/2022
Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness
Much of machine learning research focuses on predictive accuracy: given ...
research
∙
02/09/2022
Designing Closed Human-in-the-loop Deferral Pipelines
In hybrid human-machine deferral frameworks, a classifier can defer unce...
research
∙
12/23/2021
More Than Words: Towards Better Quality Interpretations of Text Classifiers
The large size and complex decision mechanisms of state-of-the-art text ...
research
∙
11/26/2021
Amazon SageMaker Model Monitor: A System for Real-Time Insights into Deployed Machine Learning Models
With the increasing adoption of machine learning (ML) models and systems...
research
∙
09/07/2021
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
Understanding the predictions made by machine learning (ML) models and t...
research
∙
07/09/2021
Multiaccurate Proxies for Downstream Fairness
We study the problem of training a model that must obey demographic fair...
research
∙
06/08/2021
On the Lack of Robust Interpretability of Neural Text Classifiers
With the ever-increasing complexity of neural language models, practitio...
research
∙
05/14/2021
On Measuring the Diversity of Organizational Networks
The interaction patterns of employees in social and professional network...
research
∙
05/07/2021
Pairwise Fairness for Ordinal Regression
We initiate the study of fairness for ordinal regression, or ordinal cla...
research
∙
03/11/2021
Differentially Private Query Release Through Adaptive Projection
We propose, implement, and evaluate a new algorithm for releasing answer...
research
∙
02/25/2021
Towards Unbiased and Accurate Deferral to Multiple Experts
Machine learning models are often implemented in cohort with humans in t...
research
∙
02/11/2021
Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy
We typically compute aggregate statistics on held-out test data to asses...
research
∙
12/15/2020
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization
Tuning complex machine learning systems is challenging. Machine learning...
research
∙
11/05/2020
Convergent Algorithms for (Relaxed) Minimax Fairness
We consider a recently introduced framework in which fairness is measure...
research
∙
08/14/2020
LiFT: A Scalable Framework for Measuring Fairness in ML Applications
Many internet applications are powered by machine learned models, which ...
research
∙
07/30/2020
Fairness-Aware Online Personalization
Decision making in crucial applications such as lending, hiring, and col...
research
∙
07/10/2020
Evaluating Fairness Using Permutation Tests
Machine learning models are central to people's lives and impact society...
research
∙
06/09/2020
Fair Bayesian Optimization
Given the increasing importance of machine learning (ML) in our lives, a...
research
∙
04/30/2019
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
Recently, policymakers, regulators, and advocates have raised awareness ...
research
∙
04/10/2019
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
There is a growing body of work that proposes methods for mitigating bia...
research
∙
01/27/2019
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
We present a large-scale study of gender bias in occupation classificati...
research
∙
09/20/2018
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
Preserving privacy of users is a key requirement of web-scale analytics ...
research
∙
09/18/2018
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
LinkedIn Talent Solutions business contributes to around 65 annual reven...
research
∙
09/17/2018
Towards Deep and Representation Learning for Talent Search at LinkedIn
Talent search and recommendation systems at LinkedIn strive to match the...
research
∙
03/29/2017
