ML Fairness Metrics&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, ensuring equitable treatment in applications such as hiring, lending, and law enforcement. Auditing involves systematically evaluating these models to identify and mitigate potential biases, thereby enhancing transparency and accountability. By employing various fairness criteria, such as demographic parity and equal opportunity, organizations can better understand the implications of their algorithms and work towards creating more just and inclusive AI solutions.
Fairness Metrics Won’t Save You from Stereotyping
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…
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☝️⚖️ ML Fairness is Everybody’s Problem
📝 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...
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AI Fairness
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...
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AI Fairness
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...
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Fairness in Machine Learning (Part 1)
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...
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How to define fairness to detect and prevent discriminatory outcomes in Machine Learning
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…
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[5 min read] Metrics to measure the performance of your classification ML models
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.
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Algorithms for Fair Machine Learning: An Introduction
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…
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