Data augmentation for fairness|aware machine learning

Data augmentation for fairness-aware machine learning

Data augmentation for fairness-aware machine learning. Preventing algorithmic bias in law enforcement systems ... Researchers and practitioners in the fairness ...

Data augmentation for fairness-aware machine learning

This contribution presents an approach for fairness-aware machine learning to mitigate the algorithmic bias / discrimination issues posed by the ...

Foundation Models for Fairness-aware Multi-modal Data ... - arXiv

Computer Science > Machine Learning · Title:Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of ...

Data augmentation for fairness-aware machine learning: Preventing ...

The proposed data augmentation technique to address bias is highlighted, but the "bad possibilities of bias" involve the focus on race as the main attribute for ...

Preventing algorithmic bias in law enforcement systems

Data augmentation for fairness-aware machine learning: Preventing algorithmic bias in law enforcement systems. Abstract. Researchers and ...

Data Augmentation via Subgroup Mixup for Improving Fairness

Abstract: In this work, we propose data augmentation via pairwise mixup across subgroups to improve group fairness. Many real-world applications of machine ...

Data Augmentation via Diffusion Model to Enhance AI Fairness - arXiv

Additionally, reweighting samples from AIF360 was employed to further enhance AI fairness. Five traditional machine learning models—Decision ...

Data augmentation for fairness-aware machine learning - YouTube

Data augmentation for fairness-aware machine learning: Preventing algorithmic bias in law enforcement systems Ioannis Pastaltzidis, ...

Adversarial Latent Feature Augmentation for Fairness - OpenReview

As fairness in machine learning has been increasingly important to mitigate bias in models, various methods to enhance fairness have been proposed.

Drop the shortcuts: image augmentation improves fairness and ...

The ease with which machine learning models identify race from patient data such as CXR images raises the possibility of using these features as shortcuts in ...

The Impact of Data Augmentation: Georgia Tech Researchers Lead ...

Basically, data augmentation artificially increases the amount of training data used in machine learning models. The idea is, a machine ...

Face Recognition Fairness Assessment based on Data Augmentation

Abstract: Deep learning models are affected by the training data when classifying, leading to discrimination in prediction output or disparity in prediction ...

Failures of Fairness in Automation Require a Deeper Understanding ...

Machine learning (ML) tools reduce the costs of performing repetitive, time-consuming tasks yet run the risk of introducing systematic unfairness into ...

Data Augmentation for Reliability and Fairness in Counselling ...

To do so, we inspect the effects of data augmentation on classical machine (CML) and deep learning (DL) approaches for counselling quality classification ...

Preventing algorithmic bias in law enforcement systems - Lirias

Data augmentation for fairness-aware machine learning: Preventing algorithmic bias in law enforcement systems. Author: Pastaltzidis, Ioannis.

Dealing with Bias via Data Augmentation in Supervised Learning ...

in data mining, machine learning, information retrieval, semantic web, and databases on bias discovery and discrimination-aware learning with the goal of ...

Data Augmentation for Discrimination Prevention and Bias ...

Fairness in machine learning has been a growing and interesting field of research. The existence of unwanted discrimination by ma- chine learning models (e.g. ...

Dealing with Bias via Data Augmentation in Supervised Learning ...

This work proposes data augmentation techniques to correct for bias at the input/data layer in supervised learning where biases towards certain attributes ...

Data augmentation for discrimination prevention and bias ...

The proposed method can be used by policy makers-who want to use unbiased datasets to train machine learning models for their applications-to ...

How to do data augmentation and train-validate split?

First do data augmentation on the data, then split the data into training and validation set. machine-learning · classification · cross- ...