Awareness of Racial and Ethnic Bias and Potential Solutions to Address Bias With Use of Health Care Algorithms
Jain, A., Brooks, J. R., Alford, C. C., Chang, C. S., Mueller, N. M., Umscheid, C. A., & Bierman, A. S. (2023). JAMA Health Forum, 4(6), e231197.
Abstract Preview: Documents a real-world case where a health care algorithm used past spending as a proxy for illness severity, underreporting the needs of Black patients relative to white patients with similar conditions and leaving them ineligible for extra care despite worse health.
AI Index Report 2024
Stanford Institute for Human-Centered Artificial Intelligence. (2024). Stanford University.
Abstract Preview: Annual benchmark of AI adoption, investment, and workforce impact across public and private sectors: 57% of respondents expect AI to affect their jobs within five years, and 36% fear outright replacement.
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Tabassi, E. (2023). National Institute of Standards and Technology.
Abstract Preview: Without proper controls, AI systems can amplify inequitable outcomes for individuals and communities; with proper controls, they can be mitigated. Frames responsible AI around human centricity, social responsibility, and sustainability.
Recommendation on the Ethics of Artificial Intelligence
UNESCO. (2021). UNESCO.
Abstract Preview: A global normative framework holding that AI can deepen existing divides within and between countries, and that ethics, rooted in human dignity, well-being, and the prevention of harm, must guide the evaluation and governance of AI technologies.
Ecological Footprints, Carbon Emissions, and Energy Transitions: The Impact of Artificial Intelligence (AI)
Wang, Q., Li, Y., & Li, R. (2024). Humanities and Social Sciences Communications, 11, 1043.
Abstract Preview: Finds that training a model like ChatGPT consumes 1.287 gigawatt-hours of electricity, yet a 1% rise in a country's AI development level is associated with a 0.0013% drop in carbon emissions, showing that AI's sustainability effect runs in both directions.
Ethics and Governance of Artificial Intelligence for Health: WHO Guidance
World Health Organization. (2021). World Health Organization.
Abstract Preview: Recognizes AI's promise for public health and medicine while flagging the digital divide, poor-quality and clinically biased data, and weak liability rules, especially in low- and middle-income countries, as ethical challenges that must be addressed.
Quick Citation
@techreport{tabassi2023airmf,
title={Artificial intelligence risk management framework (AI RMF 1.0)},
author={Tabassi, Elham},
institution={National Institute of Standards and Technology},
number={NIST AI 100-1},
year={2023}
}
(Tabassi, 2023)