As algorithms become more prevalent in everyday life, these key decision-making automations become ever more important for young people to understand.
To help preteens develop algorithmic literacy, the design studio Artefact has created the Most Likely Machine, a free digital learning tool that provides an interactive experience in which students examine the ethics and justice of algorithms—essential concepts for youth engaging independently with the internet.
Designed for self-guided exploration, this browser-based tool invites students to design an algorithm that predicts school yearbook award winners—and discover the impact of personal bias. Students learn about algorithmic bias through the lens of a fictitious middle school that is giving out awards: “Most Likely to Go to a Top University,” “Most Likely to Go Viral,” “Biggest Troublemaker.”
Students start by assigning awards to famous historical figures—for example, Albert Einstein, Marilyn Monroe, and Rosa Parks. They also assign a series of attributes required for each award—such as trailblazing, rebellious, committed—and rank those attributes in order of importance. Then the machine goes to work, providing its choice for each award.
The interactive experience uses the differences between the students’ choices and the algorithm’s response to convey how bias is built into this seemingly simple process of choosing middle school awards. But it also goes a step further, showing how algorithms can have serious real-world consequences. Real-world case studies offer examples that students can critique, covering areas they may feel passionate about, such as unfair grading systems, harmful social media, and racial bias in technology.
The learning experience aligns with computing, digital citizenship, and Personal, Social, Health, and Economic (PSHE) curricula, while linking naturally to discussions around ethics, media literacy, and social responsibility.