Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults

Schuch, Helena Silveira; Furtado, Mariane; Silva, Gabriel Ferreira dos Santos; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.; Elani, Hawazin W.

doi:10.1001/jamanetworkopen.2023.41625

Cited by 5 publications

(1 citation statement)

References 35 publications

(60 reference statements)

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“…There is overwhelming evidence that there are biases in various artificial intelligence models (AIMs) applied to machine learning algorithms (MLAGs) used in health care and other industries [ 7 , 16 , 35 , 36 , 37 , 38 , 39 ]. The uses of some of these MLAGs impact and affect many lives and livelihoods, and in many cases, they eventually prove to be devastating to those affected by them [ 22 , 23 , 40 ].…”

Section: The Current State Of Machine Learning (Ml) Models In Health ...mentioning

confidence: 99%

The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective

Franklin,

Stephens,

Piracha

et al. 2024

Life

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Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.

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