Wikimedia Apps/Team/Android/Machine Assisted Article Descriptions

Experiment background
The Android team is teaming up with Research and EPFL to improve article descriptions, also known as short descriptions and occasionally referred to as beams.

Currently Android app users can create and edit article descriptions via suggested edits. Article descriptions go to Wikidata with the exception of article descriptions for English Wikipedia. The Android team has received feedback that new users produce low-quality article descriptions (T279702). In 2022, the team placed a temporary restriction on Suggested Edits for users that had less than 3 edits for English Wikipedia users (T304621) with the intent on finding methods of improving the quality of article descriptions by new users.

EPFL and Research reached out to the Android team with a model called Descartes that can generate descriptions performing on par with human editors. Descartes takes the information on a Wikipedia article page and provides a short description of the article while adhering to the guidance of what makes an article description helpful. During initial evaluation of the model, it was preferred more than 50% of the time over human generated article descriptions. Additionally, Descartes held a 91.3% accuracy rate in testing. Despite these very promising results, the team wanted to do our due diligence by conducting an ABC test to ensure the suggestions will improve the quality of article descriptions when suggested to new editors, without introducing or increasing existing bias. We created an API which is hosted on Toolforge and will integrate the model into our existing interface in order to conduct our experiment. We will patrol edits made through the experiment in partnership with volunteers to not burden patrollers.

Product requirements

 * Users being able to provide feedback on individual suggestions should they detect issues
 * Accommodate two machine generated suggestions to test which beam is more accurate
 * Onboard users to Machine Generated suggestions
 * Reminder popups of checking for bias when clicking a suggestion on a biography
 * Only experienced users will see suggestions for biographies
 * Ability for users to write in their own response and edit a suggestion
 * Incorporate icon that identifies the product uses machine learning
 * Multilingual compatibility with mBART25

Objective and indicators
As a first step in the implementation of this project, the Android team will develop a MVP with the purpose of:


 * 1) Determine if suggestions made through the Descartes model increases the quality of article description additions and edits made using the Wikipedia Android app. To understand how the suggested article description changes user behavior, we will evaluate:
 * 2) * If introduction of suggestions alters the stickiness of the task type across editing tenure
 * 3) * Variability in task completion time relative to quality of edits
 * 4) * How often users modify suggestions before hitting publish
 * 5) * The optimal design and user workflow to encourage accuracy and task retention
 * 6) * What, if any, additional measures need to be in place to discourage bad or bias suggestions
 * 7) Determine if the algorithm holds up when exposed to more users:
 * 8) * Does the accuracy and preference rate change when exposed to more users
 * 9) * Does the accuracy and preference rate of using the suggestion vary greatly across languages
 * 10) * Is the algorithm introducing bias (e.g. misgendering) or not accurately representing critical nuance for Biographies of Living Persons
 * 11) * How does the accuracy rate and performance change when showing more than one suggestion

Should the 30 day experiment show promising results based on the indicators above, the team will introduce the feature to all users and remove our 3 edit requirement for suggested edits. We will also take steps to expand the number of languages to mBART 50 and migrate the API from toolforge to a more permanent home.

Volunteer Graders
The team will partner with volunteers to patrol edits made during the time of the experiment and assign a grade to the edit.

This will serve as one input for determining if the quality of edits increase when using machine generated article descriptions. Volunteer graders can sign up below or reach out to ARamadan-WMF.

The commitment for serving as a volunteer grader is up to one hour a week for four weeks.

Decision to be made
This A/B test will help us make the following decision:


 * Expand the feature to all users
 * Use suggestion as a means to train new users and remove 3 edit minimum gate
 * Migrate model to more permanent API
 * Show 1 or 2 beams
 * Expand to mBART 50

ABC Logic Explanation

The only users that will see the suggestions are those in mBART25
 * Experiment will include only logged in users, in order to stabilize distribution.
 * Of those in mBART25 half will see suggestions (B: Treatment) and half will not see suggestions (Control)
 * Of those in mBART25 only users that have more than 50 edits can see suggestions for Biographies of Living Persons, and if the users are in the non-BLP group, they will remain in it, even if they cross 50 edits during the experiment.

Additionally, we care about how the answers to our experiment will differ by language wiki and user experience (<50 New vs. 50+ Experienced).

Decision to be made

 * If the accuracy rate for edits that came from the suggestion is less than those manually written, we will not keep the feature in the app. The accuracy rate will be determined based on manual patrolling.
 * If the accuracy rate for edits that came from the suggestion is less than 80%, we will not keep the feature in the app. The accuracy rate will be determined based on manual patrolling.
 * If the time spent to complete the task using the suggestion is double the average rate as those that do not see suggestions we will need to compare it to reports to see if there are performance issues
 * If time spent to complete the task using the suggestion is less than the average without a negative impact to accuracy rate, we will consider it a positive indicator to expand the feature to more users
 * If users that see the suggestion modify the suggestions more often than submitting it without modification, we will evaluate its accuracy rate compared to users that did not see the suggestions and determine if the suggestion is a good starting point for users and how it differs by user experience
 * If users that see the suggestion modify the suggestions more often than submitting them without modification, we will look for trends in the modification and offer a recommendation to EPFL to update the model
 * If beam one is chosen more than 25% of the time than beam two while having an equal or higher accuracy rate, we will only show beam one in the future
 * If users that see treatment return to the task multiple times (1,2,7,14 days) at a rate 15% or more than the control group without a negative impact to accuracy, we will take steps to expand the feature
 * If our risks are triggered we will implement our contingency plan
 * If users that see the treatment do not select a suggestion more than 50% of the time after viewing the suggestions, we will not expand the feature

In aggregate, there should be at least 1500 people with a stretch goal of **2,000 people** and 4,000 edits included in the A/B test across the following mBART25 wikis: English, Russian, Vietnamese, Japanese, German, Romanian, French, Finnish, Korean, Spanish, Chinese (sim), Italian, Dutch, Arabic, Turkish, Hindi, Czech, Lithuanian, Latvian, Kazakh, Estonian, Nepali, Sinhala, Gujarati, and Burmese.

Risk management
Any time Machine Learning is used, we introduce a greater deal of risks than what is already involved in software development. For that reason, we are tracking and managing risks associated with this project alongside our Security and Legal team.

How to follow along
We have created T316375 as our Phabricator Epic to track this work. We encourage your collaboration there or on our Talk Page.

There will also be periodic updates to this page as we make progress. You can also test the model at https://ml-article-descriptions.toolforge.org/.

April 2023: FAQ Page and Model Card
We released our experiment in the 25 mBART languages this month and it will run until mid-May. Prior to release we added a model card to our FAQ page to provide transparency into how the model works.

January 2023: Updated Designs
After determining that the suggestions could be embedded in the existing article descriptions task the Android team made updates to our design. If a user reports a suggestion, they will see the same dialog as we proposed in our August 2022 update as the what will be seen if someone clicks Not Sure.

This new design does mean we will allow users to publish their edits, as they would be able to without the machine generated suggestions. However, our team will patrol the edits that are made through this experiment to ensure we do not overwhelm volunteer patrollers. Additionally, new users will not receive suggestions for Biographies of Living Persons.

November 2022: API Development
The Research team put the model on toolforge and tested the performance of the API. Initial insights found that it took 5-10 seconds to generate suggestions, which also varied depending on how many suggestions were being shown. Performance improved as the number of suggestions generated decreased. Ways of addressing this problem was by preloading some suggestions, restricting the number of suggestions shown when integrated into article descriptions, and altering user flows to ensure suggestions can be generated in the background.

August 2022: Initial Design Concepts and Guardrails for Bias
User story for Discovery

When I am using the Wikipedia Android app, am logged in, and discover a tooltip about a new edit feature, I want to be educated about the task, so I can consider trying it out. Open Question: When should this tooltip be seen in relation to other tooltips?

User story for education

When I want to try out the article descriptions feature, I want to be educated about the task, so my expectations are set correctly.

Guardrails for bias and harm
The team generated possible guardrails for bias and harm:


 * Harm: problematic text recommendations
 * Guardrail: blocklist of words never to use
 * Guardrail: check for stereotypes – e.g., gendered language + occupations
 * Harm: poor quality of recommendations
 * Guardrail: minimum amount of information in article
 * Guardrail: verify performance by knowledge gap
 * Harm: recommendations only for some types of articles
 * Guardrail: monitor edit distribution by topic