Man and machine, sharing the decision making effort
Big Data and Machine Learning in Health Care
From JAMA article
It is perhaps more useful to imagine an algorithm as existing along a
continuum between fully human-guided vs fully machine-guided data
analysis. To understand the degree to which a predictive or diagnostic
algorithm can said to be an instance of machine learning requires
understanding how much of its structure or parameters were predetermined
by humans. The trade-off between human specification of a predictive
algorithm’s properties vs learning those properties from data is what is
known as the machine learning spectrum
Higher placement on the machine learning spectrum does not imply
superiority, because different tasks require different levels of human
involvement. While algorithms high on the spectrum are often very
flexible and can learn many tasks, they are often uninterpretable and
function mostly as “black boxes.” In contrast, algorithms lower on the
spectrum often produce outputs that are easier for humans to understand
and interpret.
