Call-for-Code-for-Racial-Justice / Call-for-Code-for-Racial-Justice/TakeTwo
Provide alternate/ recommendations that are not racially biased
- Dominant language
- No language data
- Stars
- 27
- Forks
- 9
- PR merge metrics
- No merged PRs in 30d
Description
### Background on the problem the feature will solve/improved user experience
People who use TakeTwo might not only want to detect racially biased content but receive help on how to debias their content.
### Describe the solution you'd like
A solution that uses ML models, updated the API and updates the UI to provide the user with terms that are alternatives to the detected racially biased content
Related to : https://github.com/Call-for-Code-for-Racial-Justice/TakeTwo-DataScience/issues/20
### Tasks
- update ML models
- update API output
- update any UIDescription of the development tasks needed to complete this issue, including tests,
### Acceptance Criteria
Standards we believe this issue must reach to be considered complete and ready for a pull request. E.g precisely all the user should be able to do with this update, performance requirements, security requirements, etc as appropriate.
- As a user, if content I've input for analysis is found to be biased, I receive accurate alternatives that are unbiased to replace them with
Contributor guide
Research direction
Start by reviewing the existing ML models, API output, and UI referenced in the issue, along with the related issue. Determine how biased content is currently detected and where alternative terms could be returned and displayed. Done means the system provides accurate, unbiased replacement alternatives for detected biased content, with tests covering the model, API, and UI changes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning
- Domain
- api, frontend, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100