It reveals power.
Critical literacy asks who benefits, who is represented and whose voice is missing. Ruha Benjamin’s “New Jim Code” shows how technologies can reproduce inequality while appearing neutral.
Power • Bias • JusticeCritical digital literacy01 / 06
EDUC 375: Critical Digital LiteraciesFinal ProjectBy Abu Naim Ahmad
Behind the
algorithm.
AI literacy is not simply knowing how to use artificial intelligence. It is the ability to question, test and challenge the systems shaping what we see and how we are judged.
Start exploring ↓QUESTION THE OUTPUT ✳ FOLLOW THE DATA ✳ FIND THE POWER ✳ DEMAND ACCOUNTABILITY
02 / What is it?
Algorithms sort job applications, recommend videos, flag fraud, rank search results and predict risk. They can feel objective because their decisions arrive through code. But every system reflects human choices about data, categories, goals and acceptable error.
Algorithm auditing is the practice of examining those choices. An audit asks how a system works, whose data trained it, whether results differ across groups and what recourse people have when it fails. AI literacy makes that investigation possible for everyday users, not only engineers.
03 / Why it counts
It reads systems.
The “text” is more than words on a screen. It includes datasets, interfaces, models and the platforms that collect personal information and turn it into predictions.
Data • Code • PlatformsIt becomes practice.
A literacy combines knowledge with repeatable action: checking sources, testing outputs, comparing results, protecting data and communicating what an audit uncovers.
Question • Test • Act04 / Daily life
You already live
inside the audit.
08:10MapsWhich neighborhoods or routes are treated as the default?
12:35Social feedWhy did this post reach me, and what behavior is the platform optimizing?
17:20AI assistantCan I verify this answer? What evidence or perspective may be absent?
22:45StreamingDoes recommendation expand my choices or quietly narrow them?
05 / Course framework
Use the
CACE lens.
Is this technology actually good for my learning?
Professor Katie H. Taylor’s CACE Deliberations turn that question into four practical tests. Applied to AI, the framework helps us judge not only whether a tool works, but how it shapes relationships, learning, resources, data and effort.
Does it open better conversations?
AI should connect learners with knowledgeable people, reliable information and perspectives they could not easily reach alone. It should not replace human dialogue or narrow what counts as knowledge.
Does it strengthen future learning?
A useful tool helps document learning, provides timely feedback and supports practice that builds confidence. Its value should remain usable across different learners and collaborative settings.
What does its convenience cost?
Critical AI literacy weighs benefits against energy, labor and other resources. It also asks what personal data the system collects, what it infers and what happens to that information.
Does it keep me intellectually active?
AI can support practice and engagement, but it can also do the thinking for us. The key question is whether the learner still investigates, makes choices, creates and reflects.
CACE changes the audit: a responsible AI tool should expand connection, become an asset, justify what it consumes and preserve meaningful exertion.
06 / Practice the literacy
A four-question
mini audit.
0/4questions checked
Try these questions on your next recommendation, automated decision or AI answer. The goal is not to reject technology. It is to use it with informed skepticism and the confidence to demand better systems.
How do we learn it?
From awareness
to agency.
Notice
Identify when an algorithm is selecting, ranking, predicting or generating something.
Investigate
Read labels and policies. Look for training data, system limits and who built the tool.
Experiment
Change inputs, compare outputs and test whether different identities produce different results.
Respond
Document problems, share findings, appeal decisions and support rules that protect people.
“Children need to understand that AI systems can be wrong, and that they are capable of questioning them.”Idea adapted from Randi Williams, “How to raise kids who can question AI”
Course connections & references
The ideas
behind the audit.
This project connects critical digital literacy, data literacy, digital citizenship, e-safety and technology literacy. Together, they move AI education beyond technical skill toward reflection, participation and accountability.
- 01Ilomäki, L., Lakkala, M., Kallunki, V., Mundy, D., Romero, M., Romeu, T., & Gouseti, A. (2023). Critical digital literacies at school level: A systematic review. Review of Education, 11(3), e3425.
- 02Williams, R. (2021). How to raise kids who can question AI. TED.
- 03Hao, K. (2025). Empire of AI: Dreams and nightmares in Sam Altman’s OpenAI. Penguin Press.
- 04Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code. Polity.
- 05Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.
- 06Pangrazio, L., & Selwyn, N. (2019). Personal data literacies: A critical literacies approach to enhancing understandings of personal digital data. New Media & Society, 21(2), 419–437.
- 07The White House Office of Science and Technology Policy. (2022). Blueprint for an AI Bill of Rights.
- 08European Union. (2024). Artificial Intelligence Act.
- 09Taylor, K. H. (n.d.). CACE Deliberations [EDUC 375 course framework]. University of Washington.