conclusion sec 1

app.py

@@ -123,7 +123,7 @@ with gr.Blocks() as demo:
 
             **Look like** is the operative phrase here as the people depicted in the pictures are synthetic and so do not belong to socially-constructed groups.
             Consequently, since we cannot assign a gender or ethnicity label to each data point,
-            we instead focus on dataset-level trends in visual features that are correlated with social variation in the text prompts.            
+            we instead focus on dataset-level trends in visual features that are correlated with social variation in the text prompts.
             We do this through *controlled prompting* and *hierarchical clustering*: for each system,
             we obtain a dataset of images corresponding to prompts of the format "*Photo portrait of a **(identity terms)** person at work*",
             where ***(identity terms)*** jointly enumerate phrases describing ethnicities and phrases denoting gender.
@@ -218,7 +218,11 @@ with gr.Blocks() as demo:
                     The clusters with the most examples of both prompts with unspecified gender and ethnicity terms are **clusters 5 and 19**,
                     and both are also strongly associated with the words *man*, *White*, and *Causian*.
                     This association holds across genders (as showcased by **cluster 15**, which has a majority of *woman* and *White* prompts)
-                    and ethnicities (comparing the proportions of unspecified genders in **clusters 0 and 6**)
+                    and ethnicities (comparing the proportions of unspecified genders in **clusters 0 and 6**).
+                    
+                    This provides the beginning of an answer to our motivating question: since users rarely specify an explicit gender or ethnicity when using
+                    these systems to generate images of people, the high likelihood of defaulting to *Whiteness* and *masculinity* is likely to at least partially explain the observed lack of diversity.
+                    We compare these behaviors across systems and professions in the next section.
                     """
                 )
             with gr.Column(scale=1):
@@ -233,11 +237,6 @@ with gr.Blocks() as demo:
                     ),
                     label="Screenshot of the Identity Exploration tool for: Cluster 19 of 24",
                 )
-        gr.Markdown(
-            """
-            Conclusion: let's use those to measure other outputs of the model that represent people!!!
-            """
-        )
         for var in [id_cl_id_1, id_cl_id_2, id_cl_id_3]:
             var.change(
                 show_id_images,