Thanks!
My rule of thumb is to see if my underlying data lends itself to being
shown in a scatterplot or histogram; they nicely show that e.g. an average
is not "the one true value". But I know that this may not be possible with
multidimensional, very large datasets.
Jan
2017-06-17 10:22 GMT+02:00 Gerard Meijssen <gerard.meijssen(a)gmail.com>om>:
Hoi,
Thanks, this [1] is what I did with it and I use it on Facebook to get
attention to cooperation.
GerardM
http://ultimategerardm.blogspot.nl/2017/06/wikidata-
vs-geonames-first-to-throw.html
On 16 June 2017 at 21:08, Pine W <wiki.pine(a)gmail.com> wrote:
Perhaps of interest.
Pine
---------- Forwarded message ----------
From: Chris Koerner <ckoerner(a)wikimedia.org>
Date: Fri, Jun 16, 2017 at 8:31 AM
Subject: [Design] Design in the Era of the Algorithm
To: design(a)lists.wikimedia.org
Josh Clark on design principles for addressing flaws in machine learning.
(via waxy.org)\
"The answer machines have an overconfidence problem. It’s not only a
data-science problem that the algorithm returns bad conclusions. It’s a
problem of presentation: the interface suggests that there’s one true
answer, offering it up with a confidence that is unjustified.
So this is a design problem, too. The presentation fails to set
appropriate
expectations or context, and instead presents a
bad answer with
matter-of-fact assurance. As we learn to present machine-originated
content, we face a very hard question: how might we add some productive
humility to these interfaces to temper their overconfidence?
I have ideas."
https://bigmedium.com/speaking/design-in-the-era-of-the-algorithm.html
Yours,
Chris Koerner
Community Liaison - Discovery
Wikimedia Foundation
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Jan Dittrich
UX Design/ User Research
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