Survival of the Best Fit is a webgame that simulates what happens when companies automate their recruitment and selection processes.
You – playing as the CEO of a starting tech company – are asked to select your favorite candidates from a line-up, based on their resumés.
As your simulated company grows, the time pressure increases, and you are forced to automate the selection process.
Fortunately, some smart techies working for your company propose training a computer to hire just like you just did.
They don’t need anything but the data you just generated and some good old supervised machine learning!
To avoid spoilers, try the game yourself and see what happens!
The game only takes a few minutes, and is best played on mobile.
Survival of the Best Fit was built by Gabor Csapo, Jihyun Kim, Miha Klasinc, and Alia ElKattan. They are software engineers, designers and technologists, advocating for better software that allows members of the public to question its impact on society.
You don’t need to be an engineer to question how technology is affecting our lives. The goal is not for everyone to be a data scientist or machine learning engineer, though the field can certainly use more diversity, but to have enough awareness to join the conversation and ask important questions.
With Survival of the Best Fit, we want to reach an audience that may not be the makers of the very technology that impact them everyday. We want to help them better understand how AI works and how it may affect them, so that they can better demand transparency and accountability in systems that make more and more decisions for us.survivalofthebestfit.com
I found that the game provides a great intuitive explanation of how (humas) bias can slip into A.I. or machine learning applications in recruitment, selection, or other human resource management practices and processes.
Finally, here’s a nice Medium post about the game.
Note, as Joachin replied below, that the game apparently does not learn from user-input, but is programmed to always result in bias towards blues.
I kind of hoped that there was actually an algorithm “learning” in the backend, and while the developers could argue that the bias arises from the added external training data (you picked either Google, Apple, or Amazon to learn from), it feels like a bit of a disappointment that there is no real interactivity here.