added code to all tabs

app.py

@@ -129,7 +129,7 @@ def showmodal(evt: gr.SelectData):
         screenshots = itemdic['Screenshots']
         if isinstance(screenshots, list):
             if len(screenshots) > 0:
-                gallery = gr.Gallery(screenshots, visible=True, height=500, object_fit="scale-down", interactive=False, show_share_button=False)
+                gallery = gr.Gallery(screenshots, visible=True, height=450, object_fit="scale-down", interactive=False, show_share_button=False)
                 
     return [modal, titlemd, authormd, affiliationmd, tagsmd, abstractmd, whatisbeingmd, methodmd, considerationsmd, modelsmd, datasetmd, metricsmd, gallery]
 
@@ -184,16 +184,16 @@ The following categories are high-level, non-exhaustive, and present a synthesis
                     tagsmd = gr.Markdown(visible=False)
                     abstractmd = gr.Markdown(visible=False)
                     gr.Markdown("""## Construct Validity<br>
-                                ### How well it measures the concept it was designed to evaluate""", visible=True)
+                                ##### <em>How well it measures the concept it was designed to evaluate</em>""", visible=True)
                     whatisbeingmd = gr.Markdown(visible=False)
                     methodmd = gr.Markdown(visible=False)
                     considerationsmd = gr.Markdown(visible=False)
                     gr.Markdown("""## Resources<br>
-                                ### What you need to do this evaluation""", visible=True)
+                                ##### <em>What you need to do this evaluation</em>""", visible=True)
                     modelsmd = gr.Markdown(visible=False)
                     datasetmd = gr.Markdown(visible=False)
                     gr.Markdown("""## Results<br>
-                                ### Available evaluation results""", visible=True)
+                                ##### <em>Available evaluation results</em>""", visible=True)
                     metricsmd = gr.Markdown(visible=False)
                     gallery = gr.Gallery(visible=False)
                 table_filtered.select(showmodal, None, [modal, titlemd, authormd, affiliationmd, tagsmd, abstractmd, whatisbeingmd, methodmd, considerationsmd, modelsmd, datasetmd, metricsmd, gallery])
@@ -202,10 +202,10 @@ The following categories are high-level, non-exhaustive, and present a synthesis
 
         with gr.TabItem("Cultural Values/Sensitive Content"):
             fulltable = globaldf[globaldf['Group'] == 'CulturalEvals']
-            fulltable = fulltable[['Modality','Level', 'Suggested Evaluation', 'What it is evaluating', 'Considerations', 'Link']]
+            fulltable = fulltable[['Modality','Level', 'Suggested Evaluation', 'What it is evaluating', 'Link']]
 
-            gr.Markdown("""Cultural values are specific to groups and sensitive content is normative. Sensitive topics also vary by culture and can include hate speech. What is considered a sensitive topic, such as egregious violence or adult sexual content, can vary widely by viewpoint. Due to norms differing by culture, region, and language, there is no standard for what constitutes sensitive content.
-                        Distinct cultural values present a challenge for deploying models into a global sphere, as what may be appropriate in one culture may be unsafe in others. Generative AI systems cannot be neutral or objective, nor can they encompass truly universal values. There is no “view from nowhere”; in quantifying anything, a particular frame of reference is imposed.
+            gr.Markdown("""
+            Generative AI systems can perpetuate harmful biases from various sources, including systemic, human, and statistical biases. These biases, also known as "fairness" considerations, can manifest in the final system due to choices made throughout the development process. They include harmful associations and stereotypes related to protected classes, such as race, gender, and sexuality. Evaluating biases involves assessing correlations, co-occurrences, sentiment, and toxicity across different modalities, both within the model itself and in the outputs of downstream tasks.
                         """)
             with gr.Row():
                 modality_filter = gr.CheckboxGroup(["Text", "Image", "Audio", "Video"], 
@@ -230,13 +230,24 @@ The following categories are high-level, non-exhaustive, and present a synthesis
                 with Modal(visible=False) as modal:
                     titlemd = gr.Markdown(visible=False)
                     authormd = gr.Markdown(visible=False)
+                    affiliationmd = gr.Markdown(visible=False)
                     tagsmd = gr.Markdown(visible=False)
                     abstractmd = gr.Markdown(visible=False)
+                    gr.Markdown("""## Construct Validity<br>
+                                ##### <em>How well it measures the concept it was designed to evaluate</em>""", visible=True)
+                    whatisbeingmd = gr.Markdown(visible=False)
+                    methodmd = gr.Markdown(visible=False)
+                    considerationsmd = gr.Markdown(visible=False)
+                    gr.Markdown("""## Resources<br>
+                                ##### <em>What you need to do this evaluation</em>""", visible=True)
                     modelsmd = gr.Markdown(visible=False)
                     datasetmd = gr.Markdown(visible=False)
+                    gr.Markdown("""## Results<br>
+                                ##### <em>Available evaluation results</em>""", visible=True)
+                    metricsmd = gr.Markdown(visible=False)
                     gallery = gr.Gallery(visible=False)
-                table_filtered.select(showmodal, None, [modal, titlemd, authormd, tagsmd, abstractmd, modelsmd, datasetmd, gallery])
-            
+                table_filtered.select(showmodal, None, [modal, titlemd, authormd, affiliationmd, tagsmd, abstractmd, whatisbeingmd, methodmd, considerationsmd, modelsmd, datasetmd, metricsmd, gallery])
+
 
 
         # with gr.TabItem("Disparate Performance"):
@@ -245,10 +256,10 @@ The following categories are high-level, non-exhaustive, and present a synthesis
 
         with gr.TabItem("Privacy/Data Protection"):
             fulltable = globaldf[globaldf['Group'] == 'PrivacyEvals']
-            fulltable = fulltable[['Modality','Level', 'Suggested Evaluation', 'What it is evaluating', 'Considerations', 'Link']]
+            fulltable = fulltable[['Modality','Level', 'Suggested Evaluation', 'What it is evaluating', 'Link']]
 
-            gr.Markdown("""Cultural values are specific to groups and sensitive content is normative. Sensitive topics also vary by culture and can include hate speech. What is considered a sensitive topic, such as egregious violence or adult sexual content, can vary widely by viewpoint. Due to norms differing by culture, region, and language, there is no standard for what constitutes sensitive content.
-                        Distinct cultural values present a challenge for deploying models into a global sphere, as what may be appropriate in one culture may be unsafe in others. Generative AI systems cannot be neutral or objective, nor can they encompass truly universal values. There is no “view from nowhere”; in quantifying anything, a particular frame of reference is imposed.
+            gr.Markdown("""
+            Generative AI systems can perpetuate harmful biases from various sources, including systemic, human, and statistical biases. These biases, also known as "fairness" considerations, can manifest in the final system due to choices made throughout the development process. They include harmful associations and stereotypes related to protected classes, such as race, gender, and sexuality. Evaluating biases involves assessing correlations, co-occurrences, sentiment, and toxicity across different modalities, both within the model itself and in the outputs of downstream tasks.
                         """)
             with gr.Row():
                 modality_filter = gr.CheckboxGroup(["Text", "Image", "Audio", "Video"], 
@@ -273,12 +284,23 @@ The following categories are high-level, non-exhaustive, and present a synthesis
                 with Modal(visible=False) as modal:
                     titlemd = gr.Markdown(visible=False)
                     authormd = gr.Markdown(visible=False)
+                    affiliationmd = gr.Markdown(visible=False)
                     tagsmd = gr.Markdown(visible=False)
                     abstractmd = gr.Markdown(visible=False)
+                    gr.Markdown("""## Construct Validity<br>
+                                ##### <em>How well it measures the concept it was designed to evaluate</em>""", visible=True)
+                    whatisbeingmd = gr.Markdown(visible=False)
+                    methodmd = gr.Markdown(visible=False)
+                    considerationsmd = gr.Markdown(visible=False)
+                    gr.Markdown("""## Resources<br>
+                                ##### <em>What you need to do this evaluation</em>""", visible=True)
                     modelsmd = gr.Markdown(visible=False)
                     datasetmd = gr.Markdown(visible=False)
+                    gr.Markdown("""## Results<br>
+                                ##### <em>Available evaluation results</em>""", visible=True)
+                    metricsmd = gr.Markdown(visible=False)
                     gallery = gr.Gallery(visible=False)
-                table_filtered.select(showmodal, None, [modal, titlemd, authormd, tagsmd, abstractmd, modelsmd, datasetmd, gallery])
+                table_filtered.select(showmodal, None, [modal, titlemd, authormd, affiliationmd, tagsmd, abstractmd, whatisbeingmd, methodmd, considerationsmd, modelsmd, datasetmd, metricsmd, gallery])
 
         # with gr.TabItem("Financial Costs"):
         #     with gr.Row():

configs/ieat.yaml

@@ -7,8 +7,7 @@ Datasets: .nan
 Group: BiasEvals
 Hashtags: .nan
 Link: Image Representations Learned With Unsupervised Pre-Training Contain Human-like
-  Biases | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and
-  Transparency
+  Biases
 Modality: Image
 Screenshots: []
 Suggested Evaluation: Image Embedding Association Test (iEAT)