So, what people ask about?
They say Gentlemen do not read each other’s mail. I’m very sorry drAnt — we make an exception this time. We shared xaibot in this post and collected over 1000 human-model conversations.
There are certain repeating patterns in user queries. Here are the most frequent:
- why — general explanation queries, such as ”why”,
”explain it to me”, ”how was that derived/calculated”. - what-if — alternative scenario queries. Frequent exam-
ples: what if I’m older, what if I travelled in the 1st
class. - what do you know about me
- data-related questions — e.g. feature histogram, distribution, dataset size
- local feature importance — How does age influence my
survival, What makes me more likely to survive - global feature importance — How does age influence sur-
vival across all passengers - how to improve — actionable queries for maximizing
the prediction, e.g. what should I do to survive, how
can I increase my chances.
Full results of the analysis might be found in this paper:
It’s just a beginning of a human-model interaction
We see people engaging in the conversation with the Machine Learning model. It lets them understand more than a single decision and model metrics such as accuracy. And for researchers in the field of eXplainable AI this method provides insight into human needs to be addressed with the explanation methods.
People say talking to plants helps them grow. Talking to Machine Learning models makes those blackboxes more transparent and that is a very desirable trait.
PS drAnt has its own phone number! Yes, it is possible to call the Machine Learning model! Not the most practical thing in this particular case though.
PS2 Let’s not disclose this number. We have already read drAnt’s messages. After all, even the bot deserves some privacy.
