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PROJECT IN PROGRESS

Exploring Microsoft Responsible AI Toolkit with Diabetes Dataset

Project Description

This project applies the Microsoft Responsible AI toolkit to the scikit-learn Diabetes dataset, focusing on gaining practical experience with the toolkit and understanding its metrics in a real-world machine learning context. Key components explored include fairness assessment, interpretability techniques, and error analysis. The project aims to demonstrate responsible AI practices in healthcare-related machine learning, potentially improving model transparency and reducing bias in medical predictions.

Dataset Description

This project utilizes the Diabetes dataset from scikit-learn, a well-established dataset in the machine learning community. Here are key details about the dataset:

• Source: The dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.
• Target: The target variable is a quantitative measure of disease progression one year after baseline.
• Features: The dataset contains 10 baseline variables:
  • Age
  • Sex
  • Body Mass Index (BMI)
  • Average Blood Pressure
  • Six blood serum measurements
• Samples: It contains 442 samples.
• Task: The primary task is regression, predicting the quantitative measure of disease progression.
• Ethical Considerations: As this dataset relates to health information, it's crucial to consider privacy and fairness implications in its use and analysis.

This dataset provides a realistic scenario for applying responsible AI practices, as healthcare applications often involve sensitive data and have significant real-world impacts.

Features

• Utilizes the Microsoft Responsible AI toolkit for comprehensive model analysis
• Applies responsible AI practices to the scikit-learn Diabetes dataset
• Explores fairness assessment in healthcare-related machine learning
• Implements interpretability techniques for model transparency
• Conducts error analysis to improve model performance

Preparation and Installation

Preparing Conda Environment

conda env create -f environment.yaml

conda activate diabetes

In case the pip packages were not installed during the creation of the conda environment, just execute this command to verify the installation:

pip install raiwidgets interpret-community cookiecutter