Recent scrutiny of machine learning (ML) usage in healthcare systems has revealed harmful medical decisions made at the expense of minority and vulnerable populations. For example, in 2019, a commercial healthcare prediction algorithm resulted in black patients not qualifying for extra care, even though they were considerably sicker than white patients. The problem of bias in healthcare ML models compels us to take a new, human-centered approach to development that will mitigate the potential for unfairness in digital decision-making tools.
Medical professionals and researchers relying on ML models to assist in decision making are often unaware of the bias unintentionally introduced into algorithms by real-world data. Models trained using historically collected data will imitate the socio-economic inequalities and racial and gender biases embedded within the data. The result is that the models trained on biased data may lead to decisions that treat individuals unfavorably on the basis of characteristics such as e.g. race, gender, and disabilities and worsen the disparity in medical decisions.
Even in a perfectly equitable world, every model is a function of the human(s) who designed and executed the analysis. A series of judgment calls are made in the process of selecting the data, choosing or computing features, cleaning and preparing the data, and fitting and interpreting the model. Simply changing who performs the analysis could lead to a completely different version of the data or results.
Data scientists are the newest members of the healthcare team, bringing with them the power of machine learning to clinical decision making. They must stand alongside clinicians in accepting greater responsibility to ensure that models are applied fairly. Equality AI empowers digitally enabled care teams to become health equity heroes with tools that provide insight into fairness and bias and functionality to produce equitable results.
Fairness is one of the principles of Responsible AI, an emerging framework that guides the development, deployment and governance of artificial intelligence systems to ensure ethical, moral and legal compliance. Fairness-based ML offers a potential solution by incorporating fairness assessment and bias mitigation methods into ML Operations (MLOps).
At Equality AI we recognize the missing pieces in traditional MLOps and provide tools with new functionality and an updated workflow that includes:
Our tools ensure the machine learning workflow includes bias reduction and fairness modeling - Every algorithm, every time.