Question

All of the statements about applying Machine Learning to issues of health inequity are true except:

1) Different algorithms can be applied to make predictions based on the types of data available
2) Machine Learning techniques can be used to identify previously unexamined solutions for problems of health inequity
3) Improperly applied Machine Learning techniques can lead to an increase in health inequities
4) Machine Learning is a quantitative and neutral approach, so the solutions it provides won't contain biases related to ethnicity, gender, age, or other demographic groups

Answer 1

Machine Learning is not a quantitative and neutral approach, and its improper application can lead to an increase in health inequities.

Step-by-step explanation:

The statement that is not true about applying Machine Learning to issues of health inequity is: 4) Machine Learning is a quantitative and neutral approach, so the solutions it provides won't contain biases related to ethnicity, gender, age, or other demographic groups. While Machine Learning can be a powerful tool in addressing health inequities, it is important to note that it is not inherently unbiased. Machine Learning algorithms can pick up and replicate existing biases, including those related to ethnicity, gender, age, and other demographic groups. Therefore, improper application of Machine Learning techniques can lead to an increase in health inequities.