Posts tagged: Health Data
Fairness and Bias Amplification in Synthetic Data
Explore how synthetic data can amplify existing biases and affect fairness in health research. Learn why this happens and how it differs from representativeness.
Bias in Synthetic Data
An exploration of bias in synthetic data and its implications for health research.
What Do We Mean by Bias in Health Data Research?
A reference guide to what bias means in health data research and the distinct types you meet in electronic health records and other health datasets.
How Private is Synthetic Data? Understanding the Tradeoff with Utility
Synthetic data is a powerful tool for health research, but it comes with a tradeoff between privacy and utility. This blog explores what this means for researchers and how to navigate the tradeoff.
Is your Synthetic Data actually private?
A practical guide to the three privacy risks in synthetic data, the metrics that quantify them, and why no single number tells you whether your data is safe.
Representativeness in Synthetic Data: What It Means and How to Measure It
Understanding the concept of representativeness in synthetic data and the methods used to measure it.
Your Errors Are Data Too
How Rust's error handling patterns let you treat errors as structured observations about your data - capturing context, categorising failures, and producing data quality reports as first-class pipeline outputs.
Why Use Newtypes? Encoding Domain Knowledge in the Type System
How Rust's newtype pattern lets you encode domain knowledge - valid ranges, clinical thresholds, meaningful operations - directly into the type system, so the compiler enforces what you already know to be true about your data.