A Trustworthy Health AI Development Framework with Example Code Pipelines

Abstract

Trustworthy health Artificial Intelligence (AI) must respect human rights and ethical standards, while ensuring AI robustness and safety. Despite the availability of general good practices, health AI developers lack a practical guide to address the construction of trustworthy AI (TAI). We introduce a TAI development framework (TAIDEV) as a reference guideline for the creation of TAI health systems. The framework core is a TAI matrix that classifies technical methods addressing the EU guideline for Trustworthy AI requirements (privacy and data governance; diversity, non-discrimination and fairness; transparency; and technical robustness and safety) across the different AI lifecycle stages (data preparation; model development, deployment and use, and model management). TAIDEV is complemented with generic, customizable example code pipelines for the different requirements with state-of-the-art AI techniques using Python. A related checklist is provided to help validate the application of different methods on new problems. The framework is validated using two open datasets, the UCI Heart Disease and the Diabetes 130-US Hospitals, with four code pipelines adapting TAIDEV for each dataset. The TAI framework and its example tutorials are provided as Open Source in the GitHub repository: https://github.com/bdslab-upv/trustworthy-ai. The TAIDEV framework provides health AI developers with an extensible theoretical development guideline with practical examples, aiming to ensure the development of ethical, robust and safe health AI and Clinical Decision Support Systems.

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