Addressing Bias in Artificial Intelligence in Health Care.

By Ravi B. Parikh, Stephanie Teeple, A. Navathe. Journal of the American Medical Association (JAMA), 2019

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Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? (2022, background): “It is likely to meager, coexist or replace current systems, starting the healthcare age of artificial intelligence and not using AI is possibly unscientific and unethical (38).”
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation (2023, background): “Bias mitigation techniques include several approaches [45, 85], from generic requirements and toolboxes [86] to concrete taxonomies of bias [87, 88] at different stages of the AI life cycle [89].”
Addressing bias in big data and AI for health care: A call for open science (2021, background): “13 Social bias, by contrast, refers to ineq-uities that may result in suboptimal outcomes for given groups of the human population.”
Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities (2020, background): “Bias in the “real” world, and consequently in historical data, may therefore lead to statistical bias, which again can perpetuate bias in the real world (Parikh et al., 2019).”