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Lama H. Nazer, Razan Zatarah, Shai Waldrip, Janny Xue Chen Ke, Mira Moukheiber, Ashish Kumar Khanna, Rachel Hicklen, Lama Moukheiber, Dana Moukheiber, Haobo Ma, Piyush Mathur
The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent literature has raised concerns about the propagation of biases and healthcare disparities through implementation of these algorithms. Thus, it is critical to understand the sources of bias inherent in AI-based algorithms. This review aims to highlight the potential sources of bias within each step of developing AI algorithms in healthcare, starting from framing the problem, data collection, preprocessing, development, and validation, as well as their full implementation. For each of these steps, we also discuss strategies to mitigate the bias and disparities. A checklist was developed with recommendations for reducing bias during the development and implementation stages. It is important for developers and users of AI-based algorithms to keep these important considerations in mind to advance health equity for all populations.
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Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., Hicklen, R., Moukheiber, L., Moukheiber, D., Ma, H., & Mathur, P. (2023). Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health, 2(6), e0000278. https://doi.org/10.1371/journal.pdig.0000278
Nazer, Lama H., et al. "Bias in artificial intelligence algorithms and recommendations for mitigation." PLOS Digital Health, vol. 2, no. 6, 2023, pp. e0000278, https://doi.org/10.1371/journal.pdig.0000278.
Nazer, Lama H., Razan Zatarah, Shai Waldrip, Janny Xue Chen Ke, Mira Moukheiber, Ashish Kumar Khanna, Rachel Hicklen, et al. "Bias in artificial intelligence algorithms and recommendations for mitigation." PLOS Digital Health 2, no. 6 (2023): e0000278. https://doi.org/10.1371/journal.pdig.0000278.
Nazer L. H., Zatarah R., Waldrip S. [и др.] Bias in artificial intelligence algorithms and recommendations for mitigation // PLOS Digital Health. — 2023. — Vol. 2, № 6. — P. e0000278. — URL: https://doi.org/10.1371/journal.pdig.0000278
@article{nazer2023bias,
title = {{Bias in artificial intelligence algorithms and recommendations for mitigation}},
author = {Lama H. Nazer and Razan Zatarah and Shai Waldrip and Janny Xue Chen Ke and Mira Moukheiber and Ashish Kumar Khanna and Rachel Hicklen and Lama Moukheiber and Dana Moukheiber and Haobo Ma and Piyush Mathur},
journal = {PLOS Digital Health},
year = {2023},
volume = {2},
number = {6},
pages = {e0000278},
doi = {10.1371/journal.pdig.0000278},
url = {https://doi.org/10.1371/journal.pdig.0000278}
}TY - JOUR AU - Nazer, Lama H. AU - Zatarah, Razan AU - Waldrip, Shai AU - Ke, Janny Xue Chen AU - Moukheiber, Mira AU - Khanna, Ashish Kumar AU - Hicklen, Rachel AU - Moukheiber, Lama AU - Moukheiber, Dana AU - Ma, Haobo AU - Mathur, Piyush TI - Bias in artificial intelligence algorithms and recommendations for mitigation JO - PLOS Digital Health VL - 2 IS - 6 SP - e0000278 PY - 2023 DA - 2023/06/22 DO - 10.1371/journal.pdig.0000278 UR - https://doi.org/10.1371/journal.pdig.0000278 ER -
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