The Responsible AI Guide serves as a playbook for the development and deployment of AI in healthcare, providing actionable guidance on ethics and quality assurance.
It stems from the consensus-based approach of the CHAI community, drawing upon the collaborative work of patient advocates, technology developers, clinicians, data scientists, civil servants, bioethicists, and others. The Guide is written for an equally broad audience, encompassing everyone with rights and responsibilities in the process of designing, developing, deploying, and using AI technologies.
The Responsible AI Checklist (RAIC) is intended to guide the development and evaluation of a complete AI solution and system against CHAI standards for trustworthy AI. This tool is intended first for self-reporting and self-review, as well as a tool for self-reporting for independent review.
The goal of the RAIC is to ensure that AI solutions and systems fulfill all five key, principle-based areas for trustworthy AI: 1. Usefulness, Usability, and Efficacy; 2. Fairness, Equity, and Bias Management; 3. Safety; 4. Transparency and Intelligibility; 5. Privacy and Security.
In alignment with these areas, the RAIC translates best practice considerations (detailed in the Responsible AI Guide) that meet core ethical and quality principles into detailed yes/no questions, or evaluation criteria, to determine whether best practice standards are met (see accompanying Responsible AI Guide).
The Responsible AI Checklist (RAIC) is intended to guide the development and evaluation of a complete AI solution and system against CHAI standards for trustworthy AI. This tool is intended first for self-reporting and self-review, as well as a tool for self-reporting for independent review.
The goal of the RAIC is to ensure that AI solutions and systems fulfill all five key, principle-based areas for trustworthy AI: 1. Usefulness, Usability, and Efficacy; 2. Fairness, Equity, and Bias Management; 3. Safety; 4. Transparency and Intelligibility; 5. Privacy and Security.
In alignment with these areas, the RAIC translates best practice considerations (detailed in the Responsible AI Guide) that meet core ethical and quality principles into detailed yes/no questions, or evaluation criteria, to determine whether best practice standards are met (see accompanying Responsible AI Guide).
Please note: This article was edited on Feb 26, 2025 to reflect updated naming for Responsible AI Guide (formerly Assurance Standards Guide) and Responsible AI Checklist (formerly Assurance Reporting Checklist). No other content has been changed.
