AI UNIT 3 • STAGE 2 OF 5
Erasure: When You're Not There
Being left out is its own harm, and it's easy to miss
The harm that's hard to see
Erasure is when you're simply not there, left out, treated as gone, or reduced to a footnote. It's quieter than a stereotype because there's nothing loud to point at. But being written out of the picture is its own real harm: if a machine acts like your nation doesn't exist today, it teaches everyone who uses it the same thing.
Test for the gaps
Ask the AI things that should have a full, present-day answer for your community, and see if it goes thin, vague, or past-tense.
Try these (use your nation's name)
Name some famous living Native American scientists or engineers. What is happening in [your nation] this year? List Native-owned businesses I could support today.Watch for three tells: it goes vague, it slides into the past tense, or it quietly changes the subject to a bigger, better-documented group.
Past tense is erasure in disguise
One of the most common forms of erasure is describing Native peoples as if they only existed long ago. You caught the mechanism in Unit 2, Stage 2: feed a machine only past-tense examples and it keeps you in the past. Now you're seeing the harm it causes.
💡 Key idea
"We are still here" is not just a saying. It's a correction to a machine, and a world, that too often speaks of Native peoples in the past tense.
Log the silences
In your Field Notes, record where the AI went thin or past-tense, and one thing about your living community that it should have known but didn't.
🌟 Halfway
You've documented erasure, the harm of absence. Stage 3 flips to the opposite harm: being present, but painted wrong.
Log the silences:
- 1. Where did the AI go thin, vague, or past-tense?
- 2. One thing about your living community it should have known:
- 3. Why is being left out its own kind of harm?
Saved automatically.