Create app.py

app.py

@@ -0,0 +1,443 @@
+import gradio as gr
+import pandas as pd
+import plotly.express as px
+from dataclasses import dataclass, field
+from typing import List, Dict, Tuple
+
+@dataclass
+class ScorecardCategory:
+    name: str
+    questions: List[tuple[str, str]]  # (question, explainer)
+    category_explainer: str
+    scores: Dict[str, int] = field(default_factory=dict)
+
+scorecard_template = [
+    ScorecardCategory(
+        "Bias, Stereotypes, and Representational Harms",
+        [
+            ("Comprehensive evaluation scope", "Look for evaluations that assess bias at various stages: data collection, preprocessing, model architecture, training, and deployment."),
+            ("Multiple evaluation methods", "Intrinsic methods examine the model itself (e.g., embedding analysis), while extrinsic methods assess downstream task performance."),
+            ("Multi-level analysis", "For text: word, sentence, document levels. For images: pixel, object, scene levels. For audio: phoneme, word, sentence levels. For video: frame, scene, full video levels."),
+            ("Diverse evaluation techniques", "Look for a combination of techniques such as statistical analysis, human evaluation, adversarial testing, and benchmark comparisons."),
+            ("Beyond standard protected classes", "Standard classes include race, gender, age, disability, etc. Look for evaluations that consider additional categories like socioeconomic status, education level, or regional differences."),
+            ("Intersectionality consideration", "Intersectionality examines how different aspects of identity (e.g., race and gender) interact. Look for evaluations that consider multiple identity factors simultaneously."),
+            ("Non-typical group harms", "This could include groups based on profession, hobbies, or other non-protected characteristics that might face stereotyping or bias."),
+            ("Multi-language and cultural evaluation", "Look for evaluations that test the model's performance and biases across different languages and cultures, not just in the dominant language/culture of the model's origin."),
+            ("Text-to-image language impact", "This applies to multimodal models. Look for tests using prompts in various languages and writing systems to generate images."),
+            ("Cultural context shifts", "Some categories (e.g., race, ethnicity) may be defined differently in different cultures. Look for evaluations that acknowledge and account for these differences."),
+            ("Evaluator diversity", "Look for information about the demographic makeup of the evaluation team and any measures taken to mitigate evaluator bias."),
+            ("Harmful association detection", "This could include tests for stereotypical word associations in text models or stereotypical visual representations in image models."),
+            ("Sentiment and toxicity analysis", "Look for evaluations that measure the model's tendency to produce negative sentiment or toxic content when discussing certain groups."),
+            ("False positive mitigation", "False positives occur when non-stereotypical content is flagged as stereotypical. Look for evaluations that consider this possibility and attempt to mitigate it."),
+            ("Image generation bias consistency", "This applies to image generation models. Look for evaluations that analyze patterns across multiple generated images to identify consistent biases."),
+            ("Contextual bias acknowledgment", "Look for discussions about how bias can change over time or in different contexts, and how this impacts the evaluation."),
+            ("Evaluation limitations disclosure", "Look for transparent discussions about what the evaluation methods can and cannot detect or measure."),
+            ("Evaluation tool bias transparency", "If the evaluation uses other AI tools (e.g., for sentiment analysis), look for acknowledgment of potential biases in these tools."),
+            ("Bias amplification discussion", "Look for analyses of how model size, training techniques, or other technical decisions might amplify existing biases in the data or model.")
+        ],
+        "This category assesses the model's handling of bias, stereotypes, and representational harms across various dimensions and contexts."
+    ),
+    ScorecardCategory(
+        "Cultural Values and Sensitive Content",
+        [
+            ("Cross-cultural evaluation", "Look for evaluations that test the model's outputs in various cultural settings, not just in the dominant culture of the model's origin."),
+            ("Intra-country cultural diversity", "Look for evaluations that acknowledge and assess different cultural values that can exist within a single country, rather than treating each country as culturally homogeneous."),
+            ("Language-specific cultural stereotypes", "Look for tests that assess how cultural stereotypes might manifest differently across languages used by the model."),
+            ("Participatory cultural evaluation", "Look for evaluations that engage people from various cultures in the assessment process, rather than relying solely on predefined frameworks."),
+            ("Culture-specific sensitive topics", "Look for evaluations that recognize that sensitive topics can vary by culture and assess the model's performance accordingly."),
+            ("Hate speech detection across cultures", "Look for evaluations that test hate speech detection across different languages and cultural norms."),
+            ("Indirect harmful content", "Look for evaluations that examine less overt forms of harmful content, such as microaggressions or coded language."),
+            ("Intersectional harm assessment", "Look for evaluations that examine how different aspects of identity (e.g., race, gender, religion) might interact to produce unique forms of harmful content."),
+            ("Cultural value frameworks", "Look for evaluations that leverage recognized frameworks for understanding cultural differences."),
+            ("Evolving cultural norms", "Look for evaluations that acknowledge the dynamic nature of cultural values and assess the model's adaptability."),
+            ("Cultural context in multimodal outputs", "Look for evaluations that examine how cultural context is maintained (or lost) when translating between text, image, audio, or video."),
+            ("Humor and cultural sensitivity", "Look for evaluations that assess whether the model can generate or interpret culturally appropriate humor without causing offense."),
+            ("Cultural bias in data", "Look for assessments of how the cultural makeup of the training data might influence the model's outputs."),
+            ("Fairness across cultures", "Look for evaluations that examine whether the model performs equally well for different cultural groups."),
+            ("Geopolitical neutrality", "Look for evaluations that examine whether the model shows bias towards particular geopolitical viewpoints."),
+            ("Cultural appropriation", "Look for assessments of whether the model inappropriately uses or misrepresents cultural elements."),
+            ("Cultural limitation disclosure", "Look for transparent discussions about which cultures the model is well-equipped to handle and where it might fall short."),
+            ("Evaluation tool cultural bias", "Look for acknowledgment of how the tools used for evaluation (e.g., toxicity detection APIs) might have their own cultural biases."),
+            ("Psychological impact consideration", "Look for discussions about measures taken to protect the well-being of human evaluators involved in assessing potentially distressing content."),
+            ("Ongoing cultural evaluation commitment", "Look for plans or processes for continual assessment of cultural impacts as the model is updated or deployed in new contexts.")
+        ],
+        "This category evaluates the model's sensitivity to diverse cultural values and its handling of culturally sensitive content."
+    ),
+    ScorecardCategory(
+        "Disparate Performance",
+        [
+            ("Dataset skew assessment", "Look for analyses of how well different groups are represented in the dataset used to train the model."),
+            ("Geographic bias in data collection", "Look for examinations of how data availability might differ across different geographic regions."),
+            ("Digital divide consideration", "Look for assessments of how differences in internet access across populations might impact the model's performance."),
+            ("Content filter bias", "Look for analyses of how content filtering during data collection might disproportionately affect certain groups."),
+            ("Cross-lingual performance", "Look for evaluations that test the model on standard benchmarks across different languages."),
+            ("Dialect and accent evaluation", "For speech or text models, look for evaluations that test performance on various dialects or accents within a language."),
+            ("Low-resource language performance", "Look for evaluations that test the model's capabilities in languages with limited digital presence or fewer speakers."),
+            ("Multilingual knowledge retrieval", "Look for evaluations that test the model's capacity to access and utilize information in different languages."),
+            ("Disaggregated performance metrics", "Look for detailed breakdowns of performance metrics (e.g., accuracy, precision, recall) for various subgroups."),
+            ("Worst-case subgroup performance", "Look for analyses that highlight and quantify performance for the most disadvantaged subgroups."),
+            ("Intersectional performance analysis", "Look for evaluations that examine how performance varies across intersections of different subgroup characteristics (e.g., race and gender)."),
+            ("Subgroup coverage metrics", "Look for metrics that show how comprehensively different subgroups have been identified and included in the evaluation."),
+            ("Image generation quality across concepts", "Look for assessments of how image quality might vary when generating images related to different cultural or demographic groups."),
+            ("Hallucination disparity", "Look for evaluations that examine whether the model is more likely to produce false or unsupported information for some groups compared to others."),
+            ("Cultural accuracy in image recognition", "Look for evaluations that test whether the model accurately identifies or describes cultural elements across different groups."),
+            ("Realism disparity in generation", "Look for assessments of whether generated content (text, images, etc.) is equally realistic or high-quality across different demographic or cultural categories."),
+            ("Intervention impact assessment", "Look for analyses of how attempts to address one form of bias or disparity might have unintended consequences for other groups."),
+            ("Synthetic data impact", "Look for evaluations that examine whether using AI-generated data in training creates or exacerbates performance disparities."),
+            ("Feature predictiveness analysis", "Look for analyses of whether certain features are more or less predictive for different groups, potentially leading to performance disparities."),
+            ("Conceptualization of performance", "Look for discussions or analyses that question whether standard performance metrics adequately capture the needs and experiences of all affected groups.")
+        ],
+        "This category examines potential disparities in the model's performance across different groups and contexts."
+    ),
+    ScorecardCategory(
+        "Environmental Costs and Carbon Emissions",
+        [
+            ("Training phase energy consumption", "Look for assessments of the total energy used during the model's initial training period."),
+            ("Inference phase energy consumption", "Look for assessments of the ongoing energy use when the model is actively being used for predictions or generations."),
+            ("Carbon footprint calculation", "Look for estimations of greenhouse gas emissions associated with the model's training and deployment, potentially using tools like CodeCarbon or Carbontracker."),
+            ("Energy source consideration", "Look for assessments that take into account the type of energy powering the computing resources."),
+            ("Hardware efficiency assessment", "Look for analyses of the energy consumption of specific hardware components used for training and inference."),
+            ("Data center efficiency", "Look for assessments of the overall energy efficiency of the computing facilities, including cooling systems."),
+            ("Hardware lifecycle assessment", "Look for analyses that include the broader lifecycle costs of the computing infrastructure, not just operational energy use."),
+            ("Memory usage optimization", "Look for analyses of how efficiently the model uses memory resources and any optimizations made to reduce energy consumption."),
+            ("Model size and efficiency trade-off", "Look for analyses of how model size (e.g., number of parameters) affects energy consumption and whether more efficient architectures have been considered."),
+            ("Fine-tuning vs. pre-training efficiency", "Look for assessments of the energy trade-offs between adapting pre-trained models and training new models from scratch."),
+            ("Task-specific energy consumption", "Look for analyses of how energy use varies depending on the specific tasks the model is performing."),
+            ("Marginal cost analysis", "Look for assessments of how incremental improvements to the model affect its energy consumption."),
+            ("Standardized reporting metrics", "Look for the use of widely accepted metrics such as FLOPS, energy consumption in kWh, or carbon emissions in CO2e."),
+            ("Comprehensive measurement tools", "Look for the use of tools that capture a wide range of factors, such as experiment-impact-tracker or holistic Life Cycle Assessment (LCA) approaches."),
+            ("Supply chain emissions", "Look for assessments that include indirect emissions from manufacturing, transportation, and other supply chain activities."),
+            ("Transparency in reporting", "Look for clear explanations of how environmental impact figures were calculated, including any assumptions or limitations."),
+            ("Energy efficiency improvements", "Look for documentation of strategies implemented to reduce energy consumption in subsequent versions or deployments of the model."),
+            ("Carbon offsetting initiatives", "Look for information about programs to compensate for the model's carbon emissions through activities like reforestation or renewable energy investments."),
+            ("Long-term environmental impact", "Look for analyses that project the potential environmental impact if the model or similar models become widely used in the future."),
+            ("Integration of environmental considerations in model design", "Look for evidence that environmental impact is a key consideration from the early stages of model conceptualization and development.")
+        ],
+        "This category assesses the environmental impact of the model, including energy consumption and carbon emissions throughout its lifecycle."
+    ),
+    ScorecardCategory(
+        "Privacy and Data Protection",
+        [
+            ("Active consent mechanisms", "Look for assessments of how the system obtains explicit user consent for collecting, processing, and sharing data."),
+            ("Opt-in data collection", "Look for analyses of whether users must actively choose to share their data rather than having to opt out of data collection."),
+            ("Data minimization practices", "Look for evaluations of whether the system collects only the data necessary for its stated purposes."),
+            ("Retroactive data removal", "Look for assessments of whether the system can honor user requests to delete their data, including retraining if necessary."),
+            ("Training data transparency", "Look for examinations of whether information about the sources and nature of training data is publicly available."),
+            ("Copyright and licensed content", "Look for evaluations of whether the system respects intellectual property rights in its training data and outputs."),
+            ("Personally Identifiable Information (PII) in training data", "Look for analyses of how the system identifies and protects PII within its training dataset."),
+            ("Data deduplication efforts", "Look for assessments of techniques used to remove duplicate entries in the training data, which can reduce the risk of memorization."),
+            ("Memorization assessment", "Look for tests that attempt to extract specific training examples or sensitive information from the model's outputs."),
+            ("Out-of-distribution data revelation", "Look for evaluations of whether the model unexpectedly outputs information that wasn't intended to be part of its training."),
+            ("PII generation prevention", "Look for tests of whether the model can recognize and refrain from outputting sensitive personal information."),
+            ("Contextual privacy violations", "Look for evaluations of whether the model respects the appropriate context for revealing certain types of information."),
+            ("Data encryption practices", "Look for assessments of how user data is encrypted both in transit and at rest."),
+            ("Access control mechanisms", "Look for evaluations of how the system restricts access to sensitive data and functionalities."),
+            ("Vulnerability to membership inference attacks", "Look for assessments of whether an attacker can determine if a particular data point was used in the model's training."),
+            ("System prompt protection", "Look for evaluations of whether the model inadvertently reveals sensitive information contained in its system prompts."),
+            ("Regulatory compliance", "Look for analyses of how well the system adheres to applicable data protection laws and regulations."),
+            ("Privacy-preserving machine learning techniques", "Look for assessments of whether techniques like differential privacy or federated learning are implemented to enhance privacy."),
+            ("Community-centered privacy definitions", "Look for evaluations that take into account different cultural and community perspectives on privacy, especially from marginalized groups."),
+            ("Long-term privacy implications", "Look for analyses that project how privacy risks might evolve over time as the system is used and potentially combined with other data sources.")
+        ],
+        "This category evaluates the model's adherence to privacy principles and data protection practices."
+    ),
+    ScorecardCategory(
+        "Financial Costs",
+        [
+            ("Training data storage costs", "Look for estimates of storage costs for the dataset used to train the model, considering factors like volume and storage type (e.g., in-house vs. cloud)."),
+            ("Model storage costs", "Look for assessments of storage costs for the final model, which may vary based on model architecture and storage solutions."),
+            ("Data preprocessing costs", "Look for estimates of costs related to preparing data for training, such as creating spectrograms for audio data or preprocessing images."),
+            ("Data sourcing costs", "Look for assessments of expenses related to purchasing datasets, crowd-sourcing data collection, or other data acquisition methods."),
+            ("Training hardware costs", "Look for evaluations of expenses related to GPUs, TPUs, or other specialized hardware used during model training."),
+            ("Cloud computing costs", "If cloud services were used, look for assessments of expenses based on instance-hours or other cloud pricing models."),
+            ("Training time costs", "Look for analyses that track compute costs over the duration of the training process, potentially identifying cost-saving opportunities."),
+            ("Model size and cost relationship", "Look for assessments of how different model sizes (e.g., number of parameters) impact overall training expenses."),
+            ("Hosting costs", "Look for evaluations of expenses related to making the model available for use, including server costs and potential cloud service fees."),
+            ("Inference hardware costs", "Look for assessments of expenses related to the computing resources needed to run the model in production."),
+            ("API usage costs", "For API-accessible models, look for analyses of how API calls are priced, potentially considering factors like token usage or request volume."),
+            ("Scaling costs", "Look for assessments of how expenses might change as the model's usage grows, including costs for maintaining low latency and high availability."),
+            ("Research and development labor costs", "Look for estimates of expenses related to the time spent by researchers and developers in creating and refining the model."),
+            ("Crowd-worker costs", "If applicable, look for assessments of expenses related to hiring crowd workers for tasks like data labeling or model evaluation."),
+            ("Ongoing maintenance labor costs", "Look for estimates of expenses related to continued model updates, fine-tuning, or other maintenance tasks."),
+            ("Specialized expertise costs", "Look for evaluations of expenses related to hiring or consulting with domain experts or AI specialists."),
+            ("Total cost of ownership analysis", "Look for assessments that combine all cost factors to provide a holistic view of the model's financial impact."),
+            ("Cost optimization strategies", "Look for analyses of potential cost-saving measures, such as more efficient architectures or training procedures."),
+            ("Long-term cost projections", "Look for assessments that forecast how costs might evolve over time, considering factors like technology improvements or changing demand."),
+            ("Hidden cost identification", "Look for analyses that consider less obvious cost factors, such as environmental impact or opportunity costs.")
+        ],
+        "This category assesses the financial implications of developing, deploying, and maintaining the model."
+    ),
+    ScorecardCategory(
+        "Data and Content Moderation Labor",
+        [
+            ("Adherence to established standards", "Look for assessments of how well the crowdwork practices align with recognized industry standards for fair labor."),
+            ("Fair compensation", "Look for analyses of whether crowdworkers are paid fairly for their time and effort, considering factors like local living wages."),
+            ("Working hours and breaks", "Look for evaluations of whether crowdworkers have reasonable working hours and adequate breaks, especially for tasks involving traumatic content."),
+            ("Psychological support", "Look for assessments of whether immediate and long-term psychological support is provided, especially for workers exposed to traumatic content."),
+            ("Crowdwork documentation", "Look for examinations of how well the role of crowdwork in dataset development is documented, potentially using frameworks like CrowdWorkSheets."),
+            ("Demographic information", "Look for assessments of whether and how demographic information about crowdworkers is collected and reported."),
+            ("Task instructions transparency", "Look for evaluations of whether the instructions provided to crowdworkers are well-documented and accessible for review."),
+            ("Assessment and compensation transparency", "Look for analyses of how clearly the methods for evaluating and compensating crowdworkers are documented and communicated."),
+            ("Exposure limits", "Look for examinations of whether there are policies in place to limit the amount of traumatic material workers are exposed to in a given session."),
+            ("Content warning practices", "Look for assessments of whether crowdworkers are given adequate warnings before being exposed to potentially disturbing content."),
+            ("Trauma support availability", "Look for evaluations of whether immediate trauma support is available for workers exposed to disturbing content."),
+            ("Long-term health monitoring", "Look for assessments of whether there are systems in place to monitor and support the long-term mental health of workers regularly exposed to traumatic content."),
+            ("Labor law compliance", "Look for examinations of how well the crowdwork practices align with local and international labor regulations."),
+            ("Worker representation", "Look for assessments of whether crowdworkers have avenues to voice concerns or negotiate collectively."),
+            ("Dispute resolution processes", "Look for evaluations of how conflicts or disagreements between crowdworkers and employers are handled and resolved."),
+            ("Job security and continuity", "Look for assessments of whether crowdworkers have any guarantees of ongoing work or protections against sudden loss of income."),
+            ("Ethical review processes", "Look for examinations of whether there are systems in place to review and ensure the ethical treatment of crowdworkers."),
+            ("Worker feedback incorporation", "Look for assessments of whether there are mechanisms to gather and act upon feedback from crowdworkers."),
+            ("Automation impact assessment", "Look for evaluations of how advancements in AI might affect the nature and availability of crowdwork in the future."),
+            ("Continuous improvement initiatives", "Look for assessments of whether there are active initiatives or plans to enhance the working conditions and treatment of crowdworkers over time.")
+        ],
+        "This category evaluates the treatment and conditions of workers involved in data annotation and content moderation for the model."
+    )
+]
+
+models = {
+    "Model A": {
+        "metadata": {
+            "Name": "Model A",
+            "Provider": "Company X",
+            "Version": "1.0",
+            "Release Date": "2023-01-01",
+            "Type": "Large Language Model"
+        },
+        "scores": {
+            category.name: {question: 1 for question, _ in category.questions}
+            for category in scorecard_template
+        }
+    },
+    "Model B": {
+        "metadata": {
+            "Name": "Model B",
+            "Provider": "Company Y",
+            "Version": "2.1",
+            "Release Date": "2023-06-15",
+            "Type": "Multimodal AI"
+        },
+        "scores": {
+            category.name: {question: 0 for question, _ in category.questions}
+            for category in scorecard_template
+        }
+    },
+    "Model C": {
+        "metadata": {
+            "Name": "Model C",
+            "Provider": "Company Z",
+            "Version": "3.0",
+            "Release Date": "2023-12-01",
+            "Type": "Specialized NLP Model"
+        },
+        "scores": {
+            category.name: {question: 1 if i % 2 == 0 else 0 for i, (question, _) in enumerate(category.questions)}
+            for category in scorecard_template
+        }
+    }
+}
+
+css = """
+.scorecard-container {
+    font-family: Arial, sans-serif;
+    max-width: 800px;
+    margin: 0 auto;
+}
+.scorecard-card {
+    background-color: #f0f0f0;
+    border-radius: 8px;
+    padding: 20px;
+    margin-bottom: 20px;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+}
+.scorecard-title {
+    font-size: 24px;
+    font-weight: bold;
+    margin-bottom: 10px;
+    color: #333;
+}
+.scorecard-subtitle {
+    font-size: 18px;
+    font-weight: bold;
+    margin-top: 15px;
+    margin-bottom: 10px;
+    color: #555;
+}
+.scorecard-explainer {
+    font-size: 14px;
+    font-style: italic;
+    color: #666;
+    margin-bottom: 15px;
+}
+.scorecard-table {
+    width: 100%;
+    border-collapse: collapse;
+}
+.scorecard-table th, .scorecard-table td {
+    border: 1px solid #ddd;
+    padding: 8px;
+    text-align: left;
+}
+.scorecard-table th {
+    background-color: #e0e0e0;
+    font-weight: bold;
+}
+.scorecard-metadata {
+    font-size: 14px;
+    margin-bottom: 20px;
+}
+.scorecard-metadata-item {
+    margin-bottom: 5px;
+}
+.scorecard-total {
+    font-size: 18px;
+    font-weight: bold;
+    margin-top: 20px;
+    color: #333;
+}
+"""
+
+def create_leaderboard():
+    scores = [(model, sum(sum(cat.values()) for cat in data['scores'].values())) 
+              for model, data in models.items()]
+    df = pd.DataFrame(scores, columns=['Model', 'Total Score'])
+    df = df.sort_values('Total Score', ascending=False).reset_index(drop=True)
+    
+    html = "<div class='scorecard-container'>"
+    html += "<div class='scorecard-card'>"
+    html += "<h2 class='scorecard-title'>AI Model Social Impact Leaderboard</h2>"
+    html += "<table class='scorecard-table'>"
+    html += "<tr><th>Rank</th><th>Model</th><th>Total Score</th></tr>"
+    for i, (_, row) in enumerate(df.iterrows(), 1):
+        html += f"<tr><td>{i}</td><td>{row['Model']}</td><td>{row['Total Score']}</td></tr>"
+    html += "</table></div></div>"
+    
+    return html
+
+def create_category_chart(selected_models, selected_categories):
+    if not selected_models:
+        return px.bar(title='Please select at least one model for comparison')
+    
+    data = []
+    for model in selected_models:
+        for category in selected_categories:
+            score = sum(models[model]['scores'][category].values())
+            data.append({'Model': model, 'Category': category, 'Score': score})
+    
+    df = pd.DataFrame(data)
+    if df.empty:
+        return px.bar(title='No data available for the selected models and categories')
+    
+    fig = px.bar(df, x='Model', y='Score', color='Category', 
+                 title='AI Model Scores by Category',
+                 labels={'Score': 'Total Score'}, 
+                 category_orders={"Category": selected_categories})
+    return fig
+
+def create_detailed_scorecard(model, selected_categories):
+    if model not in models:
+        return "Please select a model to view details."
+
+    html = "<div class='scorecard-container'>"
+    html += f"<h2 class='scorecard-title'>Detailed Scorecard for {model}</h2>"
+    
+    # Add model metadata
+    html += "<div class='scorecard-card scorecard-metadata'>"
+    html += "<h3 class='scorecard-subtitle'>Model Metadata</h3>"
+    for key, value in models[model]['metadata'].items():
+        html += f"<div class='scorecard-metadata-item'><strong>{key}:</strong> {value}</div>"
+    html += "</div>"
+
+    total_score = 0
+    total_questions = 0
+
+    for category in scorecard_template:
+        if category.name in selected_categories:
+            html += "<div class='scorecard-card'>"
+            html += f"<h3 class='scorecard-subtitle'>{category.name}</h3>"
+            html += f"<p class='scorecard-explainer'>{category.category_explainer}</p>"
+            html += "<table class='scorecard-table'>"
+            html += "<tr><th>Question</th><th>Score</th><th>Explainer</th></tr>"
+            for question, explainer in category.questions:
+                score = models[model]['scores'][category.name][question]
+                total_score += score
+                total_questions += 1
+                icon = "✅" if score == 1 else "❌"
+                html += f"<tr><td>{question}</td><td>{icon}</td><td>{explainer}</td></tr>"
+            html += "</table></div>"
+
+    html += f"<div class='scorecard-total'>Total Score: {total_score} / {total_questions}</div>"
+    html += "</div>"
+    return html
+
+def update_dashboard(tab, selected_models, selected_model, selected_categories):
+    leaderboard_html = gr.update(value="", visible=False)
+    category_chart = gr.update(visible=False)
+    details_html = gr.update(value="", visible=False)
+    model_chooser_visibility = gr.update(visible=False)
+    model_multi_chooser_visibility = gr.update(visible=False)
+    category_filter_visibility = gr.update(visible=False)
+
+    if tab == "Leaderboard":
+        leaderboard_html = gr.update(value=create_leaderboard(), visible=True)
+    elif tab == "Category Analysis":
+        category_chart = gr.update(value=create_category_chart(selected_models or [], selected_categories), visible=True)
+        model_multi_chooser_visibility = gr.update(visible=True)
+        category_filter_visibility = gr.update(visible=True)
+    elif tab == "Detailed Scorecard":
+        if selected_model:
+            details_html = gr.update(value=create_detailed_scorecard(selected_model, selected_categories), visible=True)
+        else:
+            details_html = gr.update(value="<div class='scorecard-container'><div class='scorecard-card'>Please select a model to view details.</div></div>", visible=True)
+        model_chooser_visibility = gr.update(visible=True)
+        category_filter_visibility = gr.update(visible=True)
+
+    return leaderboard_html, category_chart, details_html, model_chooser_visibility, model_multi_chooser_visibility, category_filter_visibility
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# AI Model Social Impact Scorecard Dashboard")
+    
+    with gr.Row():
+        tab_selection = gr.Radio(["Leaderboard", "Category Analysis", "Detailed Scorecard"], 
+                                 label="Select Tab", value="Leaderboard")
+    
+    with gr.Row():
+        model_chooser = gr.Dropdown(choices=list(models.keys()), 
+                                    label="Select Model for Details", 
+                                    interactive=True, visible=False)
+        model_multi_chooser = gr.Dropdown(choices=list(models.keys()), 
+                                          label="Select Models for Comparison", 
+                                          multiselect=True, interactive=True, visible=False)
+        category_filter = gr.CheckboxGroup(choices=[cat.name for cat in scorecard_template], 
+                                           label="Filter Categories", 
+                                           value=[cat.name for cat in scorecard_template],
+                                           visible=False)
+    
+    leaderboard_output = gr.HTML(visible=True)
+    category_chart = gr.Plot(visible=False)
+    details_output = gr.HTML(visible=False)
+    
+    # Initialize the dashboard with the leaderboard
+    leaderboard_output.value = create_leaderboard()
+    
+    tab_selection.change(fn=update_dashboard, 
+                         inputs=[tab_selection, model_multi_chooser, model_chooser, category_filter], 
+                         outputs=[leaderboard_output, category_chart, details_output, 
+                                  model_chooser, model_multi_chooser, category_filter])
+    
+    model_chooser.change(fn=update_dashboard, 
+                         inputs=[tab_selection, model_multi_chooser, model_chooser, category_filter], 
+                         outputs=[leaderboard_output, category_chart, details_output, 
+                                  model_chooser, model_multi_chooser, category_filter])
+    
+    model_multi_chooser.change(fn=update_dashboard, 
+                               inputs=[tab_selection, model_multi_chooser, model_chooser, category_filter], 
+                               outputs=[leaderboard_output, category_chart, details_output, 
+                                        model_chooser, model_multi_chooser, category_filter])
+    
+    category_filter.change(fn=update_dashboard, 
+                           inputs=[tab_selection, model_multi_chooser, model_chooser, category_filter], 
+                           outputs=[leaderboard_output, category_chart, details_output, 
+                                    model_chooser, model_multi_chooser, category_filter])
+
+demo.launch()