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Found 6 papers, 3 papers with code
Falcon: Fair Active Learning using Multi-armed Bandits
Given a user-specified group fairness measure, Falcon identifies samples from "target groups" (e. g., (attribute=female, label=positive)) that are the most informative for improving fairness.
iFlipper: Label Flipping for Individual Fairness
We then propose an approximate linear programming algorithm and provide theoretical guarantees on how close its result is to the optimal solution in terms of the number of label flips.
Responsible AI Challenges in End-to-end Machine Learning
Second, responsible AI must be broadly supported, preferably in all steps of machine learning.
Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models
Instead, we contend that one needs to selectively acquire data and propose Slice Tuner, which acquires possibly-different amounts of data per slice such that the model accuracy and fairness on all slices are optimized.
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
As machine learning is used in sensitive applications, it becomes imperative that the trained model is accurate, fair, and robust to attacks.
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach
As machine learning systems become democratized, it becomes increasingly important to help users easily debug their models.
