sasha HF Staff commited on
Commit
e15f964
·
verified ·
1 Parent(s): 2bb938a

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +11 -0
app.py CHANGED
@@ -25,9 +25,20 @@ def generate_figure(org_name):
25
  with gr.Blocks() as demo:
26
  gr.Markdown("# Environmental Transparency Explorer Tool")
27
  gr.Markdown("## Explore the data from 'Misinformation by Omission: The Need for More Environmental Transparency in AI'")
 
 
 
 
 
 
 
28
  with gr.Row():
29
  with gr.Column(scale=1):
30
  org_choice= gr.Dropdown(organizations, value="Alphabet", label="Organizations", info="Pick an organization to explore their environmental disclosures", interactive=True)
 
 
 
 
31
  with gr.Column(scale=4):
32
  gr.Markdown("### Data by Organization")
33
  fig = generate_figure(org_choice)
 
25
  with gr.Blocks() as demo:
26
  gr.Markdown("# Environmental Transparency Explorer Tool")
27
  gr.Markdown("## Explore the data from 'Misinformation by Omission: The Need for More Environmental Transparency in AI'")
28
+ with gr.Accordion('Methodology'):
29
+ gr.Markdown('We analyzed Epoch AI\'s "Notable AI Models" dataset, which tracks information on “models that were state of the art, highly cited, \
30
+ or otherwise historically notable” released over time. We selected the time period starting in 2010 as this is the beginning of the modern “deep learning era” \
31
+ (as defined by Epoch AI), which is representative of the types of AI models currently trained and deployed, including all 754 models from 2010 \
32
+ to the first quarter of 2025 in our analysis. We examined the level of environmental impact transparency for each model based on key information \
33
+ from the Epoch AI dataset (e.g., model accessibility, training compute estimation method) as well as from individual model release content \
34
+ (e.g., paper, model card, announcement).')
35
  with gr.Row():
36
  with gr.Column(scale=1):
37
  org_choice= gr.Dropdown(organizations, value="Alphabet", label="Organizations", info="Pick an organization to explore their environmental disclosures", interactive=True)
38
+ gr.Markdown('The 3 transparency categories are: \
39
+ **Direct Disclosure**: Developers explicitly reported energy or GHG emissions, e.g., using hardware TDP, country average carbon intensity or measurements. \
40
+ **Indirect Disclosure**: Developers provided training compute data or released their model weights, allowing external estimates of training or inference impacts. \
41
+ **No Disclosure**: Environmental impact data was not publicly released and estimation approaches (as noted in Indirect Disclosure) were not possible.')
42
  with gr.Column(scale=4):
43
  gr.Markdown("### Data by Organization")
44
  fig = generate_figure(org_choice)