Moderator Tools/Automoderator/Testing

To help communities test and evaluate Automoderator's accuracy, we are making a test spreadsheet available with data on past edits and Automoderator's decisions.

The decisions Automoderator would make are a combination of a machine learning model score and internal configuration. The model will be re-trained and improve over time, but we also want to understand what internal configuration rules we can apply to improve Automoderator's accuracy. For example, we have found that Automoderator often mis-judges users reverting their own edits as vandalism, so we will not take action on these edits. We would like to find other examples of these kinds of edits, and need your help to do so.

How to test Automoderator

 * 1) Make a copy of this spreadsheet by clicking File > Make a Copy ...
 * 2) Select the 'Share it with the same people' before clicking 'Make a copy' so that we can aggregate data from your responses.
 * 3) Follow the instructions in the sheet to select a random dataset, review 30 edits, and then uncover what decisions Automoderator would make for each edit.
 * 4) Join the discussion on the talk page.

Details
Automoderator's model is only trained on main namespace pages, so the dataset is limited to Wikipedia article edits. Further details can be found below:

Internal configuration
In the current version of the dataset, in addition to model scoring, Automoderator does not take actions on:


 * Edits made by administrators
 * Edits made by bots
 * Edits which are self-reverts
 * New page creations

The datasets and list above will be updated as testing progresses if we add new exclusions.

Caution levels
In this test Automoderator has five 'caution' levels, defining the revert likelihood threshold above which Automoderator will revert an edit.

At high caution, Automoderator will need to be very confident to revert an edit. This means it will revert fewer edits overall, but do so with a higher accuracy.

At low caution, Automoderator will be less strict about its confidence level. It will revert more edits, but be more accurate.

The caution levels in this test have been set by the Moderator Tools team based on our observations of the models accuracy and coverage. To illustrate the number of reverts expected at different caution levels see below:

TODO

Score an individual edit
If you want to get a Revert Risk score for an individual edit, you can do so with the LiftWing API ... TODO

Note that this is just the model score, and does not take into account Automoderator's internal

Further details