"We have developed an amazing machine learning model, for predicting if our customers will be able to pay back their loan" – Bank* Representative
*: You properly shouldn't trust anything said.
"One small issue, our customer-facing advisors don't understand or trust the model and sometimes goes against it." – Bank Spokesperson
"Oh, we just did some visual discretizations and told them to trust it." – Bank Representative
Needless to say, they missed the point.
§2: "... the controller shall, provide the data subject with the information necessary to ensure fair and transparent processing".
§2.f: such as "the existence of automated decision-making, and at least in those cases, meaningful information about the logic involved ...".
You can't say something about memorization from a single hidden unit.
But then how?
"Intuition is developed from a
"Interactive visualization proivides a feedback loop."
"Confirmation bias is when we interpret or favor information that confirms one's personal beliefs."
Transform your intuition into a quantifiable hypothesis!
tl;dr: most methods are not better than a random baseline.
Why where so many papers published, all claiming improved methods?
Use these tools to explain models,
but be hyper-aware of confirmation bias.
Always transform your intuition into
a quantifiable hypothesis.