ML Fairness Metrics&Auditing - Learn Data Science with Travis

Fairness Metrics Won’t Save You from Stereotyping

Towards Data Science

Fairness metrics are often used to verify that machine learning models do not produce unfair outcomes across racial/ethnic groups, gender categories, or other protected classes. Here, I will…

☝️⚖️ ML Fairness is Everybody’s Problem

TheSequence

📝 Editorial We typically associate fairness issues in machine learning (ML) models with large consumer tech startups like Facebook, Apple and Twitter. It seems easy enough to point the finger at bias...

AI Fairness

Kaggle Learn Courses

Introduction There are many different ways of defining what we might look for in a fair machine learning (ML) model. For instance, say we're working with a model that approves (or denies) credit card...

AI Fairness

Kaggle Learn Courses

Introduction There are many different ways of defining what we might look for in a fair machine learning (ML) model. For instance, say we're working with a model that approves (or denies) credit card...

AI Fairness

Kaggle Learn Courses

Introduction There are many different ways of defining what we might look for in a fair machine learning (ML) model. For instance, say we're working with a model that approves (or denies) credit card...

Fairness in Machine Learning (Part 1)

Towards AI

Contents Fainess in Machine Learning Evidence of the problem Fundamental concept: Discrimination, Bias, and Fairness 1. Fairness in Machine Learning Machine learning algorithms substantially affect ev...

How to define fairness to detect and prevent discriminatory outcomes in Machine Learning

Towards Data Science

This can be achieved is by defining a metric that describes the notion of fairness in our model. For example, when looking at university admissions, we can compare admission rates of men and women…

Designing a Fairness Workflow for Your ML Models

Towards Data Science

In the first blog post of this series, we discussed three key points to creating a comprehensive fairness workflow for ensuring fairness for machine learning model outcomes. They are: We then delved…

Beyond Accuracy: Exploring Exotic Metrics for Holistic Evaluation of Machine Learning Models

Towards Data Science

Machine learning has undoubtedly become a powerful tool in today’s data-driven world, but are we truly tapping into its full potential? Traditional evaluation metrics like accuracy, precision, and rec...

Algorithms for Fair Machine Learning: An Introduction

Towards Data Science

A critical component of responsible ML model development is to make sure that our models are not unfairly harming any subgroups of our population. The first step is identifying and quantifying any…

Evaluating Machine Learning Models Fairness and Bias.

Towards Data Science

Evaluating machine learning models for bias is becoming an increasingly common focus for different industries and data researchers. Model Fairness is a relatively new subfield in Machine Learning. In…...

Analysing Fairness in Machine Learning (with Python)

Towards Data Science

Doing an exploratory fairness analysis and measuring fairness using equal opportunity, equalized odds and disparate impact Continue reading on Towards Data Science