Update data-ethics.qmd

updated typos

Author: angiegarciaa

sections/data-ethics.qmd

@@ -297,9 +297,9 @@ As new AI developments and applications rapidly emerge and transform everyday li
 
 ### Discussion Activity  
 
-Given the discussion of the CARE priciples and the FAST principles, let's discuss what responsible AI considerations might exist in the context of Arctic research, particularly with respect to Indigneous peoples of the Arctic. Geospatial data spanning the Arctic typically includes the traditional lands and waters of Arctic Indigenous peoples, and often intersects with current local communities distributed throughout the Arctic. Recent work by projects like [Abundant Intelligences](https://www.indigenous-ai.net/abundant/]) are starting to explore the intersection of Indigineous Knowledge systems, Artifical Intelligence models, and how to guide the "development of AI \[to support\] a more humane future".
+Given the discussion of the CARE principles and the FAST principles, let's discuss what responsible AI considerations might exist in the context of Arctic research, particularly with respect to Indigneous peoples of the Arctic. Geospatial data spanning the Arctic typically includes the traditional lands and waters of Arctic Indigenous peoples, and often intersects with current local communities distributed throughout the Arctic. Recent work by projects like [Abundant Intelligences](https://www.indigenous-ai.net/abundant/]) are starting to explore the intersection of Indigenous Knowledge systems, Artifical Intelligence models, and how to guide the "development of AI \[to support\] a more humane future".
 
-Let's take, for example, a researcher that wants to run an machine learning model to detect changes in environmental features at a large regional or Arctic scale. We've seen several of these so far, including 1) AI predictions of the distribution of permafrost ice wedges and retrogressive thaw slumps across the Arctic; 2) use of AI to detect changes in surface water extent and lake drainage events across the Arctic; 3) use of AI in a mechanistic process models that helps understand the global source/sink tradeoff of permafrost loss and its impact on climate.
+Let's take, for example, a researcher that wants to run a machine learning model to detect changes in environmental features at a large regional or Arctic scale. We've seen several of these so far, including 1) AI predictions of the distribution of permafrost ice wedges and retrogressive thaw slumps across the Arctic; 2) use of AI to detect changes in surface water extent and lake drainage events across the Arctic; 3) use of AI in a mechanistic process models that helps understand the global source/sink tradeoff of permafrost loss and its impact on climate.
 
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 ## Discussion Questions