The Most Likely Machine is a free, interactive digital learning module designed to help pre-teens develop algorithmic literacy. Students often learn about algorithms as reliable mathematical tools, but Artefact saw an opportunity to develop an experience that examines the ethics and justice of algorithms – important concepts for children starting to engage independently with the Internet.
Founded on a pedagogy of play, the engaging sandbox experience puts students in the driver's seat designing an algorithm of their own that predicts Middle School yearbook award winners – and discovering the impact of personal bias on a scale small and large.
Designed for both self-guided exploration and to supplement classroom curriculum, the Most Likely Machine demonstrates the possibilities for fun and meaningful digital learning experiences in the era of remote education.
As a free, open-source prototype, The Most Likely Machine is being used in classrooms and digital literacy non-profits across the U.S. and Canada.
A new approach to teaching algorithmic literacy
In the pre-teen years, children begin to understand abstract concepts like causation and fairness, all while developing their own opinions and identity. It is also the period that most American children get their first smartphone or social media account and begin to engage independently with the Internet.
With the average American pre-teen consuming more than four and a half hours of screen media per day, it's crucial that this age group develop the critical analysis and Internet literacy needed to navigate the digital world responsibly.
Inspired by resources from the MIT Media Lab, Pew Research Center, the Algorithm Literacy Project, and Common Sense Media, the Most Likely Machine prototype builds on robust research and curricula to teach pre-teens about algorithmic bias in a creative, meaningful, and fun digital experience.
The Impact of the Most Likely Machine
Technology and the digital world have an immense impact on our ability to receive and understand information, parse fact from fiction, communicate and build relationships. With so much content at our fingertips, digital literacy is proving to be one of the most essential pillars of an engaged and informed civil society. But digital literacy is only one element of being a savvy digital citizen.
Algorithms are the key building blocks of the digital world, and the foundation for many of its greatest capabilities – and worst consequences. Algorithmic literacy – the ability to recognize and understand the inherent bias in computer algorithms – sits at the intersection of math, social studies, civics, and media literacy, and is foundational to understanding how digital platforms and machine learning/AI tools impact our world.
The Most Likely Machine learning experience helps pre-teens understand algorithmic bias and recognize its impact, enabling them to grow into more conscious, savvy, and responsible consumers, creators, and digital citizens.
The Most Likely Machine Lesson
The Most Likely Machine is an interactive experience that asks students to help a fictional Middle School yearbook committee decide on how to determine superlative – or yearbook award – winners. Students create an algorithm to determine the winners, unwittingly introducing their own bias into the tool. They are then able to explore the results of their algorithm, manipulate the algorithm to achieve different results, and reflect on algorithmic bias and how it impacts the real world.
The theme of yearbook superlative awards serves as a metaphor for the impact of algorithms. A familiar school activity, these awards are not so different from how algorithms categorize people in the real world. Superlatives also touch on concepts like personal identity, fairness, and choosing winners and losers that are central to the learning objectives around algorithmic bias.
The three awards (most likely to go to a top university, most likely to go viral, and biggest troublemaker) relate to where algorithms have significant real-world impact (education, media, and the criminal justice system), helping students recognize the wider impact of algorithms on their lives and community.
We then selected a cast of well-known historical characters to ensure students started the lesson with a set of assumptions that we could leverage to help them learn.
Throughout the process, Artefact worked with both Middle School educators and pre-teen users to understand how best to evolve and improve the experience in order to meet learning objectives through an engaging and fun digital experience.
A flexible module for remote learning
Distance learning presents new challenges to communication, engagement, and attention span. We designed the Most Likely Machine prototype to both facilitate self-guided, independent exploration and supplement classroom curriculum.
The module-based approach empowers students to digest curriculum in bite-sized chunks and explore the experience on their own, before regrouping with their class for facilitated discussion with teachers.
As the world adapts to the realities of distance learning, we hope the Most Likely Machine prototype serves as inspiration and a step toward a future where digital learning experiences are not only engaging and meaningful, but support students and teachers as they navigate remote education.