AI, Medicine, and Bias: Diversifying Your Dataset is Not Enough
While it's important to consider the diversity of your dataset and the performance of your model across different demographic group, this is just a narrow slice of the issues we need to consider related to bias and fairness. Using machine learning for medicine as a case study, I'll illustrate some of the broader considerations related to bias, power, and participation that all data scientists need to take into account.
This talk was delivered at the Stanford AI in Medicine & Imaging Symposium on August 5, 2020, as part of a session on Fairness in Clinical Machine Learning.
For more on Practical Data Ethics, please check out my free online course at http://ethics.fast.ai/
While it’s important to consider the diversity of your dataset and the performance of your model across different demographic group, this is just a narrow slice of the issues we need to consider related to bias and fairness. Using machine learning for medicine as a case study, I’ll illustrate some of the broader considerations related to bias, power, and participation that all data scientists need to take into account.
This talk was delivered at the Stanford AI in Medicine & Imaging Symposium on August 5, 2020, as part of a session on Fairness in Clinical Machine Learning.
For more on Practical Data Ethics, please check out my free online course at http://ethics.fast.ai/
