# Improving dataset transparency in dermatologic Artificial Intelligence using a dataset nutrition label

## TL;DR

This paper introduces a dataset nutrition label to improve transparency and reduce bias in dermatology AI datasets.

## Contribution

The novel contribution is the creation of a structured Dataset Nutrition Label for dermatology datasets to enhance transparency and responsible data use.

## Key findings

- The DNL provides a structured summary of dataset attributes, limitations, and risks.
- The label helps users assess dataset suitability and address potential biases proactively.

## Abstract

Biased and poorly documented dermatology datasets pose risks to the development of safe and generalizable artificial intelligence (AI) tools. We created a Dataset Nutrition Label (DNL) for multiple dermatology datasets to support transparent and responsible data use. The DNL offers a structured, digestible summary of key attributes, including metadata, limitations, and risks, enabling data users to better assess suitability and proactively address potential sources of bias in datasets.