ML Fairness

Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models.

Fairness | Machine Learning - Google for Developers

This course module teaches key principles of ML Fairness, including types of human bias that can manifest in ML models, identifying and mitigating these ...

What Is Machine Learning Fairness? What You Need to Know

Machine learning fairness is the process of correcting and eliminating algorithmic bias (of race and ethnicity, gender, sexual orientation, disability, and ...

Fairness in machine learning: Regulation or standards?

ML fairness—and AI fairness more broadly—is a complex and multidimensional concept, and there are several definitions and metrics used to ...

What does “fairness” mean for machine learning systems?

“Fairness” is a ubiquitous term in the artificial intelligence (AI) and machine learning (ML) space. Most principles for responsible and ethical AI include ...

[2012.15816] Fairness in Machine Learning - arXiv

ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models ...

Machine Learning Glossary: Fairness - Google for Developers

Instead, it suggests that fairness must be defined contextually for a given ML problem, with the goal of preventing harms specific to its use ...

Machine learning fairness - Azure Machine Learning | Microsoft Learn

A fairness assessment is not a purely technical exercise. The Fairlearn open-source package can identify quantitative metrics to help you assess ...

A gentle introduction to ML fairness metrics - Superwise.ai

In this post, we will cover some common fairness metrics and their mathematical representations and explain the logic behind each.

What is Model Fairness - Model Monitoring | MLOps Wiki - Censius AI

ML fairness is the model's quality or state of being fair or impartial, and it relates to the harm of allocation and quality of services.

ML Fairness Mini-Bootcamp - CLTC UC Berkeley Center for Long ...

Do you teach machine learning? You know fairness is an issue, but not sure how to teach about it? Here's a mini-bootcamp to help you teach students how to ...

Fairness in Machine Learning: A Survey - ACM Digital Library

Quantitative definitions allow fairness to become an additional performance metric in the evaluation of an ML algorithm. However, increasing fairness often ...

A novel approach for assessing fairness in deployed machine ...

Fairness in machine learning (ML) emerges as a critical concern as AI systems increasingly influence diverse aspects of society, ...

google/ml-fairness-gym - GitHub

ML-fairness-gym implements a generalized framework for studying and probing long term fairness effects in carefully constructed simulation scenarios where a ...

Understanding ML Fairness: Causes of Bias - Deepchecks

In this article, we will delve into the causes of bias in ML models and explore effective strategies for achieving fairness.

ML-fairness-gym: A Tool for Exploring Long-Term Impacts of ...

A set of components for building simple simulations that explore potential long-run impacts of deploying machine learning-based decision systems in social ...

[2309.17337] Toward Operationalizing Pipeline-aware ML Fairness

A more holistic approach to tackle fairness issues by systematically investigating the many design choices made through the ML pipeline.

FAT ML: Home

Bringing together a growing community of researchers and practitioners concerned with fairness, accountability, and transparency in machine learning.

Building ML models for everyone: understanding fairness in ...

Fairness is the process of understanding bias introduced by your data, and ensuring your model provides equitable predictions across all ...

Assess ML models' fairness in Python (preview) - Azure Machine ...

This package can help you assess the fairness of a machine learning model, but only you can configure and make decisions as to how the model performs.