Machine Learning Games
MoFo FLEX 2019 - Project Proposal
Project Lead: Chad Sansing
Area of Focus: Focusing on how discrimination, a lack of openness and transparency, and a lack of responsibility all contribute to discimination and negatively impact digital inclusion.
Knowledge Level Required: None / Any
We are going to make low-fi and no-fi social, collaborative games that demonstrate how biased inputs lead machine learning products to discriminate against under-represented and wrongly-represented people. Inspirations include Werewolf, Inhuman Conditions (https://robots.management/), indie table-top role-playing games (TTRPGs), storytelling, matching games, and more.
I want to learn how each part of machine learning functions & connects to the others from trainers & data sets, to general adversarial networks & generators & discriminators, to the products that use the machine learning & the people it affects.
Everyone should feel welcome to join, learn, & design games with us, & to help us prototype & publish games to share at All-Hands, MozFest, & beyond via CC licensing & .pdf distribution. Gamers, artists, storytellers, layout designers, editors et al.
Required Time Commitments
Gather contributors --> begin cadence of meetings, mini-design sprints --> prototype --> share at All-Hands --> submit to MozFest --> refine for MozFest --> play at MozFest --> publish betas before the end of 2019. The Project Lead will spend 2 hours working on the project and contributors would need to allow for 2 hours per week to fully get involved.
More to come...