ML Fairness Metrics&Auditing - Learn Data Science with Travis

ML-Fairness-Metrics-and-Auditing

Machine Learning (ML) fairness metrics and auditing are essential components in the development and deployment of AI systems. These metrics help assess whether ML models make unbiased decisions across different demographic groups, such as gender, race, or age. Fairness metrics can include demographic parity, equal opportunity, and others, each providing a unique perspective on model performance. Auditing involves systematically evaluating these metrics to identify and mitigate biases in ML systems. By implementing fairness metrics and conducting thorough audits, organizations can enhance transparency, accountability, and trust in their AI applications, ultimately promoting equitable outcomes in society.

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

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…

[5 min read] Metrics to measure the performance of your classification ML models

Analytics Vidhya

Whilst there are many metrics available to evaluate a Classification ML model, in this post, I am going to focus on the ones I have seen being used most frequently.

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

Performance Metrics for Machine Learning Models

Analytics Vidhya

There are various metrics that we can use to evaluate the performance of ML algorithms, classification as well as regression algorithms. We must carefully choose the metrics for evaluating ML…

Auditing Predictive A.I. Models for Bias and Fairness

Towards AI

For predictive A.I. models to impact more people, developers, psychologists, and outside parties must collaborate to assess them. Recently, two authors published a paper with guidance for conducting ...