Hi Everyone,
The next Research Showcase will be live-streamed this Wednesday, Aug 17,
2016 at 11:30 AM (PST) 18:30 (UTC).
YouTube stream:
http://youtu.be/rsFmqYxtt9w
As usual, you can join the conversation on IRC at #wikimedia-research. And,
you can watch our past research showcases here
<https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#Archive>.
This month's showcase includes.
Computational Fact Checking from Knowledge NetworksBy *Giovanni Luca
Ciampaglia <https://www.mediawiki.org/wiki/User:Junkie.dolphin>*Traditional
fact checking by expert journalists cannot keep up with the enormous volume
of information that is now generated online. Fact checking is often a
tedious and repetitive task and even simple automation opportunities may
result in significant improvements to human fact checkers. In this talk I
will describe how we are trying to approximate the complexities of human
fact checking by exploring a knowledge graph under a properly defined
proximity measure. Framed as a network traversal problem, this approach is
feasible with efficient computational techniques. We evaluate this approach
by examining tens of thousands of claims related to history, entertainment,
geography, and biographical information using the public knowledge graph
extracted from Wikipedia by the DBPedia project, showing that the method
does indeed assign higher confidence to true statements than to false ones.
One advantage of this approach is that, together with a numerical
evaluation, it also provides a sequence of statements that can be easily
inspected by a human fact checker.
Deploying and maintaining AI in a socio-technical system. Lessons
learnedBy *Aaron
Halfaker <https://www.mediawiki.org/wiki/User:Halfak_(WMF)>*We should
exercise great caution when deploying AI into our social spaces. The
algorithms that make counter-vandalism in Wikipedia orders of magnitude
more efficient also have the potential to perpetuate biases and silence
whole classes of contributors. This presentation will describe the system
efficiency characteristics that make AI so attractive for supporting
quality control activities in Wikipedia. Then, Aaron will tell two stories
of how the algorithms brought new, problematic biases to quality control
processes in Wikipedia and how the Revision Scoring team
<https://meta.wikimedia.org/wiki/R:Revision_scoring_as_a_service> learned
about and addressed these issues in ORES
<https://meta.wikimedia.org/wiki/ORES>, a production-level AI service for
Wikimedia Wikis. He'll also make an overdue call to action toward
leveraging human-review of AIs biases in the practice of AI development.
We look forward to seeing you!
--
Sarah R. Rodlund
Project Coordinator-Engineering, Wikimedia Foundation
srodlund(a)wikimedia.org