What's going on inside this black box ?
Complex AI algorithms are trying to solve
the world's most difficult problems :
they're fed with data
which they analyze to find patterns.
This is how they provide us
with predictions to answer tricky questions :
from how changes
in a process affect production volume or speed,
to how policies might impact society.
But how can we trust or improve these predictions
if we don't know what happens inside the box ?
BCG’s FACET
is switching on the light.
Our open-source library provides
an unprecedented view of the interactions
between the different variables
in machine learning models.
It applies a unique algorithm
for global model interpretation
enabling data scientists to understand
how the different factors influence each other.
With the release of our new version available now,
FACET offers an enhanced machine learning workflow,
better model inspection, new visualizations,
and much more !
By making the black box transparent,
we take the leap from prediction to explanation.
If we know how parameters relate to each other,
and to outcomes,
we can run “what if” scenarios
and optimize these outcomes
with surgical precision.
We can eliminate bias by detecting its source.
We can also enable all those working with algorithms
to make informed decisions.
Curious ?