Bias recognition and mitigation strategies in artificial intelligence healthcare applications

Nature | March 2025

This figure maps the stages of the AI model life cycle in healthcare, highlighting the common phase at which biases can be introduced. The AI life cycle is divided into six phases: conception, data collection, pre-processing, in-processing (algorithm development and validation), post-processing (clinical deployment), and post-deployment surveillance. Each phase is prone to specific biases that can affect the fairness, equity, and equality of healthcare delivery.

The Assessing Risks and Impacts of AI (ARIA) Program Evaluation Design Document

NIST- National Institute of Standards and Technology | December 2024

The Assessing Risks and Impacts of AI (ARIA) Program Evaluation Design Document

The NIST Assessing Risks and Impacts of AI (ARIA) Pilot Evaluation Plan

NIST – National Institute of Standards And Technology | August 2024

Article cover for Bias Neutralization

Bias Neutralization Framework:

Measuring Fairness in Large Language Models with Bias Intelligence Quotient (BiQ)

arXiv | August 2024 | Volume 4, Issue 3

Article cover for Bias Neutralization

Decisional Value Scores:

A New Family Of Metrics For Ethical AI‑ML

AI & Ethics | June 2024
Article screenshot - The ethical implications of AI Hype

Developing Ethics and Equity Principles, Terms, and Engagement Tools to Advance Health Equity and Researcher Diversity in AI and Machine Learning:

Modified Delphi Approach

JMIR | September 2023

Article Screenshot - Developing ethics and equity principles

Embedding Ethics and Equity in Artificial Intelligence and Machine Learning Infrastructure

Big Data | September 2023 | Volume 11, Number S1

Embedding Ethics and Equity in Artificial Intelligence and Machine Learning Infrastructure.