
Why UX Design for Machine Learning Matters - kawera
https://www.fastcodesign.com/90124399/why-ux-design-for-machine-learning-matters
======
cellarpaper
Hi all! I'm actually the author of the article- if you have any comments feel
free to leave them.

I specifically chose to address this to ux designers bx ml will be the future
of product design and what I had noticed, from working at IBM Watson, was a
lack of understanding across design and engineering teams how to "explain" or
show what was happening in products using ux. This is actually the start of a
column for fast co focusing specifically on design for AI and ml.

So how a product is processing information and serving up results has to be
articulated to users- maybe that articulation is a visualization or maybe it's
something else? It depends upon the product- but this was a call for thinking
about how, if your product uses ml, how do you translate that for users in the
design of the product? It's advocating that for ML we move away from minimal
design.

~~~
tensor
No one understands how their car works, or their microwave, or even fridge.
Why should they understand how something like ML works?

~~~
XaspR8d
They understand when a car or a microwave or a fridge is useful, and why you
wouldn't want to use one in place of another.

~~~
crimsonalucard
Yes if it wasn't for UX designers we wouldn't know when to use a fridge or
microwave. Same with ML.

------
GuiA
The "UX design" terminology confused me. It seems that the author is
advocating more for tools and methods that let us visualize & inspect how a
given neural network comes to be based on its training data, and why it gives
a particular output for a particular input.

This ties back into the "neural networks are black boxes" argument that's been
floating around for a while, no?

Is there any concrete research in this field? The initiatives the author
points to seem to be mainly about highlighting the problem, not addressing it.

That being said, while I agree with it in principle, in practice what would
compel companies to make their neural network based systems understandable and
analyzable to users? There are plenty of non deep learning based systems in my
life that I already don't have access to the internals of; for instance, it'd
really be great if I could actually see the set of formulas that determine my
credit score, for one example amongst many.

~~~
hcrisp
Patrick Hall and company wrote a good O'Reilly article on interpreting machine
learning:

[https://www.oreilly.com/ideas/ideas-on-interpreting-
machine-...](https://www.oreilly.com/ideas/ideas-on-interpreting-machine-
learning)

~~~
e2e4
very nice article; thanks for sharing

------
tsunamifury
I think a simpler start might be in order. Most developers, let alone users,
don't know when, why and how, to use ML. I created (with partners) the "smart
reply" designs that are visualizations of ML results that are used by Google
in Allo and Hangouts and were used it various other earlier Gproperties. The
goal was just to allow people to understand how ML uses probabilities to
predict possible futures rather than one off single guesses. You can see and
play a bit with how probability trees create outputs and play around with
them. They were also designed to be implicitly participatory and multiplayer,
rather than just static outputs you could either use or not use. The hope was
that more sophisticated users could get a mental model around this and maybe
become inspired.

I think several other "lessons" like this will be in order to better get
people playing with ML driven interfaces before the average developer really
starts seeing enough value to use tool sets meant to visualize the NNs.

------
ThomPete
I don't think the fundamental premise of this is correct although I understand
the reason why it's being proposed.

Machine learning will mostly remove the need for UX expertise as it will allow
us to worry less about manually interacting with machines as a way to get them
to do things. Instead ML will do all the in between interactions behind the
scenes and present us with results.

If you think about Machine Learning as a feedback loop where only the input
and result become relevant then you are back to something more akin info
graphics which used to be called information design.

A much bigger problem to solve UX wise is to think about how machine learning
will design things and how humans will deal with the results.

[https://twitter.com/Hello_World/status/861735184990961664](https://twitter.com/Hello_World/status/861735184990961664)

In the shown example the result created by ML is many times better than a
human could have done but for it to work it also requires the ability to take
this design and implement it into real life as the result is extremely
complex.

A lot of things to explore in this space but I think we do ourselves a
disservice to speak about is as UX.

~~~
virgil_disgr4ce
As long as there are users, it will be necessary (or desirable, at the very
very least) to design experiences for them. The assumption you make is that
"talking to a humanlike intelligence is so natural as to be trivial." But
salespeople, for instance, still need to be trained in how best to talk to
their customers, what to say, what not to say, etc..

~~~
ThomPete
To the extent that salespeople are relevant and using ML nothing UX wise has
changed. Same rules still apply.

My assumption is that many of the benefits of ML can't be used by humans but
have to be used by other machines to create a final "useful to human" output.

------
sherryye
The biggest challenge I think for designers in ML is, we are very often one
step behind engineers who build ML algorithm. Without a good understanding of
the capacities and limitations of machine learning, it’s hard for designers to
envision opportunities and be involved in the early strategic conversation

