diff --git a/README.md b/README.md
index 8bd1b7d..adfdd11 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,9 @@
 
 PaPPI (Protocol-aware Prediction-Powered Inference) estimates a crop map's macro-F1 or accuracy across its deployment area, with a confidence interval. It uses biased field labels and unlabeled deployment points. Crop maps are usually evaluated on labels collected near roads or in a few survey areas, so their reported scores can overstate performance across the area they cover.
 
-![audit output](docs/audit_output.svg)
+Watch the [PaPPI explainer video](https://opendata.aiforgood.ai/videos/pappi-ppi-explainer.mp4).
+
+https://github.com/user-attachments/assets/0cdb3407-373c-40a7-b2dc-8b19ca6da28c
 
 ## Setup
 
@@ -44,6 +46,8 @@ Audit a trained model with labeled rows that it has never seen:
 $ pappi audit test_and_deploy.csv --model my_model.joblib
 ```
 
+![audit output](docs/audit_output.svg)
+
 ## Input
 
 Each row represents one sample. Rows with a `label` form the labeled set. Rows with an empty `label` must be a uniform sample of the deployment population. `--label` changes the label column name, and `--features` changes the feature-column prefix.