Measuring Fairness
There are multiple ways to measure accuracy. No matter how we build our model, accuracy across these measures will vary when applied to different groups of people.
Measuring Fairness Explorables Measuring Fairness There are multiple ways to measure accuracy. No matter how we build our model, accuracy across these measures will vary when applied to different groups of people. Measuring Fairness How do you make sure a model works equally well for different groups of people? It turns out that in many situations, this is harder than you might think. The problem is that there are different ways to measure the accuracy of a model, and often it's mathematically impossible for them all to be equal across groups. We'll illustrate how this happens by creating a (f
related reading
- Statistical Fairness - Turing Commonsalan-turing-institute.github.io
- Relative notions of fairnessfairmlbook.org
- [arxiv] an algorithmic framework for fairness elicitationarxiv.org
- Self-Fulfilling Misalignment Data Might Be Poisoning Our AI Modelsturntrout.com
- Can a Model Be Differentially Private and Fair?pair.withgoogle.com
- Datasets Have Worldviewspair.withgoogle.com
- Measuring Diversitypair.withgoogle.com
- Algorithmic Accountabilitydatasociety.net
- Biasjzhao.xyz
- AI and prediction of criminal justice risk: revisiting the COMPAS dataset, experiments, and the problem of algorithmic bias and fairnesslink.springer.com
- Hidden Biaspair.withgoogle.com
- Troubles with the Bias-Variance tradeoffejenner.com