The Impartial Machines Project

[abhayrjoshi]

Project Lead: @abhayrjoshi
@SaiThejeshwar

GitHub Repository: https://github.com/abhayrjoshi/The-Impartial-Machines-Project

Mentor: @gorlapraveen

Welcome to OL7, Cohort A! This issue will be used to track your project and progress during the program. Please use this checklist over the next few weeks as you start Open Leadership Training 🎉.

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Before Week 1 (Jan 29): Your first mentorship call

• Complete the OLF self-assessment (online, printable). If you're a group, each teammate should complete this assessment individually. This is here to help you set your own personal goals during the program. No need to share your results, but be ready to share your thoughts with your mentor.
• Make sure you know when and how you'll be meeting with your mentor.

Before Week 2 (Feb 5): First Cohort Call (Open by Design)

• Complete Assignment 'Write Your Vision or Mission Statement'. Leave a comment on this issue with your draft vision statement & be ready to share this on the call.
• Watch 'Mozilla & Working Open'.
• Check the Syllabus for notes and connection info for all the cohort calls.

Before Week 3 (Feb 12): Mentorship call

• Look up two other projects and comment on their issues with feedback on their vision statement.
• Complete your Open Canvas (instructions, canvas). Comment on this issue with a link to your canvas.
• Start your Roadmap. Comment on this issue with your draft Roadmap.

Before Week 4 (Feb 19): Cohort Call (Build for Understanding)

• Look up two other projects and comment on their issues with feedback on their open canvas.
• Pick an open license for the work you're doing during the program.
• Use your canvas to start writing a README, or landing page, for your project. Link to your README in a comment on this issue.

Week 5 and more

• Create a repository for your project. New to GitHub? You can fork our templates.
• Add Contributing Guidlines
• Add a Code of Conduct
• Submit your project to Mozilla Pulse, add the tag #mozsprint
• Invite new contributors to into your work!

This issue is here to help you keep track of work as you start Open Leaders. Please refer to the OL7 Syllabus for more detailed weekly notes and assignments past week 4.

[abhayrjoshi]

Our Vision

We are working with the community to help deliver a Dual AI Engine which tries to eliminate the potential influences/bias in the news by the means of similarity indexing and version control and helps connect the dots in facilitating the dissemination of impartial news conforming to the highest code of ethics possible.

We are working open because we believe that news should be non-influential to circumstances, operate independently from any influences/bias and should be prioritised based on the importance, free of any ulterior motives and validated by employing a proper community feedback loop.

[abhayrjoshi]

Open Canvas

Please find the Open Canvas for our project here

[abhayrjoshi]

Our Roadmap

Please find the roadmap for The Impartial Machines Project here

[pablodiegoss]

Hey! We from #36 Jandig read your vision, looks really nice, but maybe you could improve the "Who you’re working with" and "Who you’re doing it for", are you guys helping journalists or the general news public? Not very clear on first glance :)

[annefou]

Very intersting project. Your open canvas is very good too. Your Key Metrics could be a bit clearer; I mean how to assess an "Active contribution by community for datasets": will you count the number of contribution per dataset?
Same comment for your second Key Metrics: "Able to identify the bias in a given data". How will you be able to say whether you have identified or not the bias in a given data? Will you count how many biases you have identified in a given dataset?

[SaiThejeshwar]

Hey! We from #36 Jandig read your vision, looks really nice, but maybe you could improve the "Who you’re working with" and "Who you’re doing it for", are you guys helping journalists or the general news public? Not very clear on first glance :)

Hey! Thank you for your views. We have updated the canvas, with clear problem statement. We are going to provide an unbiased news to the public.

[SaiThejeshwar]

Very intersting project. Your open canvas is very good too. Your Key Metrics could be a bit clearer; I mean how to assess an "Active contribution by community for datasets": will you count the number of contribution per dataset?

Hey! Right now, we aren't able to find any datasets on news articles as a whole. So, we are hoping the community would contribute in building the dataset. A roadmap of how to contribute and other details will be shared soon!

Same comment for your second Key Metrics: "Able to identify the bias in a given data". How will you be able to say whether you have identified or not the bias in a given data? Will you count how many biases you have identified in a given dataset?

Yes. First we decide with human intelligence and we metrics against the model.