Artificial intelligence and algorithmic bias: implications for health systems
By Trishan Panch, Heather Mattie, Rifat A. Atun. Journal of Global Health, 2019
429 citations recorded by the source corpus.
in the context of AI and health systems as: “the instances when the application of an algorithm compounds existing inequities in socioeconomic status, race, ethnic background, religion, gender, disability or sexual orientation to amplify them and adversely impact inequities in health systems.”
Subfields: Family Practice, General Health Professions, Health Informatics
View Paper: https://www.semanticscholar.org/paper/00485008bda96930a1616e373dd6428b9d31dc53
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In Family Practice
In General Health Professions
In Health Informatics
Citations in the corpus
- Bias in AI-based models for medical applications: challenges and mitigation strategies (2023, positive): “Additionally, there are likely underlying social inequalities in healthcare access and expenditures that impact how a model might be trained to predict risk 6,10 .”
- Bias in medical AI: Implications for clinical decision-making (2024, background): “However, while bias detection and mitigation methods are relatively well developed for traditional statistical techniques like regression, the literature surrounding similar methods for AI algorithms is still nascent [12,13].”
- Out of One, Many: Using Language Models to Simulate Human Samples (2022, background): “337 2018; Panch, Mattie, and Atun 2019).”
- Bias in machine learning - what is it good for? (2020, background): “However, typical usage of that term usually refers to the societal effects of biased systems [36], while our notion of bias is broader.”