AI and Indigenous Data Sovereignty: 

Responsible Use for Biodiversity Monitoring

What does ‘AI for Tribal biodiversity monitoring’ actually mean?

Use Cases

1: Species Classification from Audio and Image Data

Current process: Once a network of camera and audio traps are set up, images and sounds are hand labeled by those with intimate knowledge of their ancestral land being monitoring

AI Use Case: Use image and audio classifiers to identify species from camera images or acoustic monitor sound bites. This can help land managers understand wildlife population size, track endangered and/or invasive species presence.

Benefits: This would reduce the time and effort needed to label all of the image and audio data. Without having to label each data point by hand, there is substantially more bandwidth for other activities. 

Considerations:

2: Using ML to Model Population Estimates of Wildlife or Vegetation

Current process: Once a network of camera and audio traps are set up, images and sounds are hand labeled by those with intimate knowledge of their ancestral land being monitoring

AI Use Case: Use image and audio classifiers to identify species from camera images or acoustic monitor sound bites. This can help land managers understand wildlife population size, track endangered and/or invasive species presence.

Benefits: Knowing the patterns of biodiversity in a region is incredibly helpful for making land management decisions. 

Considerations:


3: Plain Text Processing for Data Analysis and Visualization 

Current process: Once labeled image and sound data is acquired, code must be written to both filter and vizualize the data. 

AI Use Case: Fine tuning an LLM to convert plain text requests into code to filter data, and code to visualize data. 

Benefits:

Considerations:

Open Questions

Reach out to us to chat, with questions, or to read the white paper!

contact: ccmartinez@berkeley.edu, debruyn@berkeley.edu