diff --git a/01e-AI_Possibilities-possibilities.Rmd b/01e-AI_Possibilities-possibilities.Rmd
index caaf7974..29e67e1f 100644
--- a/01e-AI_Possibilities-possibilities.Rmd
+++ b/01e-AI_Possibilities-possibilities.Rmd
@@ -1,3 +1,6 @@
+---
+always_allow_html: yes
+---
 
 ```{r, include = FALSE}
 ottrpal::set_knitr_image_path()
@@ -315,14 +318,14 @@ Here is the response:
 Here is a toy time series dataset tracking individuals, time points, and coffee consumption:
 
 ```{r echo = FALSE, warning = FALSE, message = FALSE}
-install.packages("kableExtra")
-library(kableExtra)
+install.packages("kableExtra", repos = "https://cloud.r-project.org")
+library(magrittr)
 data.frame(
   ID = c(rep(1:3, each = 3)),
   Time_point = c(rep(1:3, times = 3)),
   Coffee_cups = c(2,3,1,4,2,3,1,0,2)
 ) %>%
-  kbl() 
+  kableExtra::kbl() 
 ```
 
 This tracks 3 individuals over 3 time points (days) and their daily coffee consumption in cups. Individual 1 drank 2 cups on day 1, 3 cups on day 2, and 1 cup on day 3. Individual 2 drank 4 cups on day 1, 2 cups on day 2, and 3 cups on day 3. Individual 3 drank 1 cup on day 1, 0 cups on day 2, and 2 cups on day 3.
diff --git a/02b-Avoiding_Harm-concepts.Rmd b/02b-Avoiding_Harm-concepts.Rmd
index 665c81b5..05b37cd8 100644
--- a/02b-Avoiding_Harm-concepts.Rmd
+++ b/02b-Avoiding_Harm-concepts.Rmd
@@ -1,4 +1,6 @@
-
+---
+always_allow_html: yes
+---
 
 
 ```{r, include = FALSE}
@@ -258,11 +260,7 @@ In the flip side, AI has the potential if used wisely, to reduce health inequiti
 * Consider the possible outcomes of the use of content created by newly developed AI tools. Consider if the content could possibly be used in a manner that will result in discrimination.
 </div>
 
-See @belenguer_ai_2022 for more guidance. We also encourage you to check out the following video for a classic example of bias in AI:
-
-```{r, fig.align="center", fig.alt = "video", echo=FALSE, out.width="90%"}
-knitr::include_url("https://www.youtube.com/embed/TWWsW1w-BVo?si=YLGbpVKrUz5b56vM")
-```
+See @belenguer_ai_2022 for more guidance. We also encourage you to check out [this video for a classic example of bias in AI](https://www.youtube.com/embed/TWWsW1w-BVo?si=YLGbpVKrUz5b56vM).
 
 
 For further details check out this [course](https://www.coursera.org/learn/algorithmic-fairness) on Coursera about building fair algorithms. We will also describe more in the next section.