<i>Equality Engine:</i> Fostering Critical Machine Learning Bias Literacy Through a Transformational Game

Machine Learning (ML) is now integrated from everyday technologies to sophisticated infrastructures, providing fast, efficient, and scalable decision-making services, with increasing evidence of ML perpetuating invisible harms and biases. The hidden and socio-technical nature of ML biases can make them difficult to detect and prevent without proper literacy. To investigate this, we developed a novel online multiplayer board game Equality Engine, where players learn about various ML biases and debiasing techniques. We conducted a mixed-method formative playtest case study to solicit feedback from post-secondary students (N = 12) with a range of ML experience. We found that students' self-reported ML bias-debias knowledge improved significantly after playing the game. The game was perceived as easy to use because of the social interaction and immersion the game enabled. Students would also use the game in the future because of the self-reported knowledge gained from the game. Although these positive results may be influenced by measurement bias, our study contributes to the design of an approachable game, which not only facilitates exposure, collaboration, and opportunities for critical reflection on ML biases but also provides recommendations for future game designs that can facilitate ML ethics discourse and literacy among a broader audience.