research
∙
02/05/2023
Improving Fair Training under Correlation Shifts
Model fairness is an essential element for Trustworthy AI. While many te...
share
research
∙
12/13/2021
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
Software 2.0 is a fundamental shift in software engineering where machin...
research
∙
10/27/2021
Sample Selection for Fair and Robust Training
Fairness and robustness are critical elements of Trustworthy AI that nee...
research
∙
01/15/2021
Responsible AI Challenges in End-to-end Machine Learning
Responsible AI is becoming critical as AI is widely used in our everyday...
research
∙
12/03/2020
FairBatch: Batch Selection for Model Fairness
Training a fair machine learning model is essential to prevent demograph...
research
∙
04/07/2020
Inspector Gadget: A Data Programming-based Labeling System for Industrial Images
As machine learning for images becomes democratized in the Software 2.0 ...
research
∙
02/24/2020
FR-Train: A mutual information-based approach to fair and robust training
Trustworthy AI is a critical issue in machine learning where, in additio...
research
∙
04/22/2019
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
The wide use of machine learning is fundamentally changing the software ...
research
∙
11/08/2018