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Most Likely Machine
Pros: Gorgeous design. Unique, interactive content with a powerful message.
Cons: No follow-up resources or privacy policy. Lacks nuance.
Bottom Line: This is an effective and well-designed intro to the dangers of algorithms and how we have to approach them with care.
Most Likely Machine is designed for independent work, but there are lots of great prompts to generate classroom discussion (which students should be excited to have). Teachers should be aware, however, that there isn't much support for classroom implementation, so be prepared to generate discussion questions and point students to additional materials should students want to explore more.
Teachers can allow students to work through the entire site independently and then discuss/debrief with the whole class after they try a few different solutions. It would also be possible to project the site and work through it as a class. This whole-class model adds a great level of debate to the experience and can unlock a whole new set of discussions and discoveries, from digital citizenship to ethics to SEL. Whatever route you choose, if students get hooked, they can then return to the site and identify topics they'd like to investigate further. Older students in particular could investigate the algorithmic practices and policies of their favorite sites and design social media platforms or search engines that they feel better respect people's privacy.
Most Likely Machine (MLM) is a free website that teaches algorithmic literacy, or how algorithms function and shape our digital world. The site has a classroom yearbook theme and features a series of explanatory pages followed by interactive activities that model algorithmic thinking. In the activities, students predict which of a set of historical figures (the classmates) will win in three different yearbook categories, such as "Most Likely to Go to a Top University." Students are then asked to select a group of traits, assign them to a category, and rank them in terms of importance to that category. Once they've gotten all that done, the algorithm runs, and chooses the winners of each category. Students can then compare their predictions to the algorithm's results.
Overall, MLM really does a good job showing how various algorithms can be biased and not necessarily give accurate results. However, the selection of terms is a little clunky (you have to choose them one at a time and assign them to each of the categories, but only nine per category). Since you can't see all the choices in advance, it's hard to know what should go where. As a result, some selections could end up being not the ones you want. The bio descriptions of the various "classmates" also stray more into opinion than fact, although a lot of this ends up adding to the experience and the lesson.
It's also important to note that MLM is the product of a design company. While they pride themselves on being socially responsible, there's an opportunity to discuss what it means that this content comes from a company that might also work with algorithms or consult with companies that depend on them. This is particularly relevant given that the site doesn't have a privacy policy.
As an introduction, Most Likely Machine (MLM) does a great job developing students' curiosity about, and awareness of, algorithms in a short, focused, and visually appealing experience. The topic will feel instantly relevant to social media-savvy students and generate a lot of discussion and vectors for further study across content areas. It can get computer science and math students to reflect on how procedural computational decisions influence behavior, knowledge, and culture. Students in the humanities get a glimpse of computational processes and will enjoy pondering how algorithms impact fairness and equity. From a digital citizenship perspective, students will also be encouraged to weigh the benefits and costs of media platforms that depend on these algorithms.
On that note, however, the site is a bit one-sided. The experience doesn't adequately explain why some might see these algorithms as beneficial, or how design might be able to better account for biases or avoid pitfalls. It also doesn't address the business models that drive demand for these algorithms. This is left up to the teacher, as is any extension or classroom implementation. Adding this context as well as better support materials would round out MLM well.