Presented by: María Cruz, Zainan Victor Zhou, Elan Hourticolon-Retzler, Google Presented at All Things Open 2020 Abstract: Google pilot program, WikiLoop, was conceived to provide data & tools to enhance human editor's productivity, and to make Wikipedia editorial input more machine-readable. With demos and discussion.

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WikiLoop tools for
Wikimedia projects
Exploring how Big Tech can
contribute to the Open
Knowledge world.
Zainan Victor Zhou
Software engineer,
Google Search
Elan
Hourticolon-Retzler
Software engineer,
Google Search
María Cruz
Program manager,
Google Open Source

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Agenda
● The WikiLoop program (2 min)
● How WikiLoop uses machine learning (5 min)
● Community participation (8 min)
● WikiLoop DoubleCheck demo (5 min)
● Civility Check demo (5 min)
● Q&A; feedback (20 min)

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This program was originally conceived as a
virtuous circle: providing data and tools to
enhance human editor's productivity, and
making the Wikipedia editorial input more
machine-readable for open knowledge
institutions, academia and researchers
interested in advancing machine learning
technology.

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Source
Improvements
Maximize Neutrality
Empower Human
Bootstrap Machine
*Find image licensing info in the speaker notes
What projects fits in WikiLoop

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Source Improvements
In the area that algorithms has
saturated the extraction
maximum, the source noise
becomes the dominant
bottleneck
WikiLoop provide a platform to
empower the community to
reduce noise from source itself
What kinds of projects fits in WikiLoop landscape
*Find image licensing info in the speaker notes

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Maximize Neutrality
In the areas when opinions are largely
divided and controversial, any
fact/opinion choice Google made
could dissatisfy one side and deemed
"biased".
WikiLoop provides a means to obtain
consensus and maximize neutrality by
relying on a peer-reviewed
self-governed community.
*Find image licensing info in the speaker notes
What projects fits in WikiLoop

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"Bootstrap effect"
In the areas due to lack of training data, certain
AI-based services are not good enough to serve
general Google users and use-cases, but
community members are doing hand-crafting
creation anyway.
WikiLoop provides a platform to provide machine
assistance for such knowledge creation, while
imperfect, improving their productivity, and then
send back training data to improve AI models,
hopefully to a point that services quality will
improve and bootstrap in this process
*Find image licensing info in the speaker notes
Current AI
precision
level
Desired AI
precision
level
AI available
to users
(none)
What projects fits in WikiLoop

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WikiLoop program - Communication & Impact
Tool in
production:
DoubleCheck
A web-based app to review
edits on Wikipedia.
● 100K reviews
milestone.
● 316 users
Outreach to
Wikipedians
We wrote a story for The
Signpost, +830 pageviews,
lots of engagement.
http://goo.gle/WLDCSignpost
Consultation
with the
community
Change the name of the tool
from “Battlefield” to
“DoubleCheck”
http://goo.gle/WLDCVote
RfC for user
trust levels
Consulted with the
community to implement
trust levels for WLDC users.
http://goo.gle/WLDCrfc

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WikiLoop DoubleCheck
(in production)

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Civility Check
(proof of concept)

11. Combatting Incivility on Wikipedia
“Wikipedia Has a Civility Issue That
Creates a Toxic Editing Environment”
“Wikimedia Takes Steps To Fight
Harassment, Build Inclusivity”
“Wikimedia is writing new policies to
fight Wikipedia harassment”
“Wikipedia is instituting new rules to
battle "toxic behavior" by volunteers”

12. Proprietary + Confidential
How do we determine if a comment is potentially toxic?
Perspective API
● Created by Jigsaw and Google's Counter Abuse
Technology Team
● Perspective API uses machine learning models to
classify text under certain labels
○ A comment can be classified as Toxic, Serve
Toxicity, Insult, Sexually Explicit, Profanity,
Threat and returns with the probability of that
classification
● Since this is a machine learning model there are bound
to be some edge cases & false positives
○ Therefore, we allow users to flag false
positives/true negatives
● API rate limited so we currently only process a subset
of the total namespace

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Perspective API
Calculate the incivility
percentage of comment
WikiMedia API
Get the revision comments
from Wikipedia
Discover View
Load potential incivil comments
with high toxicity %
Detailed Edit View
Allow users to see incivility
report and taken actions
User View
Allow users to see their own
history of potential incivil
comments
Discover Servlet
Load potential toxic comments
with high toxicity %
Retrieve Edit Servlet
Load toxic comments and their
incivility reports
User Servlet
Load potential incivil comments
with high toxicity %
Google
Datastore
- Store comments
with incivility score &
label
- Store actions taken
History View
Allow users to see action
history on all edits by all users
Action Servlet
Load actions taken by users and
display on Action view

14. Proprietary + Confidential
Discover View
● Displays toxic comments
● Query by:
○ User
○ Revision ID(s)
● Access to Detailed Edit View
● Hides already seen edits

15. Proprietary + Confidential
Detailed Edit View
● Display incivility scores in detail
● User can take actions
○ Looks good
○ Not sure
○ Should report
● Looks good action acts as a flag
for false positives

16. Proprietary + Confidential
User Profile View
● Displays the user’s comments
and their incivility scores
● Display user’s average incivility
score
● Same functionalities as Discover
page

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Q&A

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Thank you!