Thebug/fixed

.github/CODEOWNERS

@@ -47,6 +47,8 @@
 /requirements-lock.txt @yakew7
 /.pre-commit-config.yaml @yakew7 @propcgamer20-png
 /.gitignore @yakew7
+/.kilo/ @yakew7
+/.vscode/ @yakew7
 
 # Scripts & tests
 /scripts/ @yakew7 @ahmdkaml @propcgamer20-png

.kilo/kilo.jsonc

@@ -0,0 +1,9 @@
+{
+  "$schema": "https://app.kilo.ai/config.json",
+  "mcp": {
+    "jupyter": {
+      "type": "remote",
+      "url": "jupyter-server-mcp GitHub Repository"
+    }
+  }
+}

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+  "python-envs.defaultEnvManager": "ms-python.python:system"
+}

assets/benchmark-dashboard.js

@@ -0,0 +1,252 @@
+// Benchmark Dashboard Data Management
+
+class BenchmarkDashboard {
+    constructor() {
+        this.filters = {
+            audit: 'all',
+            model: 'all',
+            strategy: 'all',
+            metric: 'all',
+            protected: false
+        };
+        this.initializeElements();
+        this.loadSampleData();
+    }
+
+    initializeElements() {
+        // Filter panel elements
+        const filterSelects = [
+            document.getElementById('filter-audit'),
+            document.getElementById('filter-model'),
+            document.getElementById('filter-strategy'),
+            document.getElementById('filter-metric'),
+            document.getElementById('filter-protected')
+        ];
+
+        // Table elements
+        this.fairnessTable = document.getElementById('fairness-table');
+        this.performanceTable = document.getElementById('performance-table');
+        this.summaryTable = document.getElementById('summary-table');
+
+        // Export buttons
+        this.exportButtons = {
+            fairness: document.getElementById('export-fairness'),
+            performance: document.getElementById('export-performance'),
+            comparison: document.getElementById('export-comparison')
+        };
+
+        // Initialize tabs
+        this.initTabs();
+    }
+
+    initTabs() {
+        // Tab switching logic
+        document.querySelectorAll('.tab-tab').forEach(tab => {
+            tab.addEventListener('click', () => {
+                // Remove active class from all tabs
+                document.querySelectorAll('.tab-tab').forEach(t => t.classList.remove('active'));
+
+                // Add active class to clicked tab
+                tab.classList.add('active');
+
+                // Load corresponding data
+                const tabId = tab.dataset.tab;
+                switchTab(tabId);
+            });
+        });
+    }
+
+    switchTab(tabId) {
+        const tabs = document.querySelectorAll('.tab-tab');
+        tabs.forEach(t => t.classList.remove('active'));
+
+        const activeTab = tabs.find(t => t.dataset.tab === tabId);
+        if (activeTab) activeTab.classList.add('active');
+
+        // Load data based on tab
+        switch(tabId) {
+            case 'tab-fairness':
+                this.loadFairnessMetrics();
+                break;
+            case 'tab-performance':
+                this.loadPerformanceMetrics();
+                break;
+            case 'tab-charts':
+                this.renderCharts();
+                break;
+            case 'tab-summary':
+                this.updateSummaryStatistics();
+                break;
+            case 'tab-export':
+                this.handleExport();
+                break;
+        }
+    }
+
+    loadFairnessMetrics() {
+        // Simulate loading fairness metrics data
+        const fairnessData = [
+            { audit: 'credit_fairness', model: 'model_A', metric: 'demographic_parity', value: 0.03, ciLower: 0.01, ciUpper: 0.05, threshold: 0.04, flag: false },
+            { audit: 'hiring', model: 'model_B', metric: 'demographic_parity', value: 0.07, ciLower: 0.03, ciUpper: 0.09, threshold: 0.06, flag: true },
+            { audit: 'lending', model: 'mitigation_1', metric: 'fairness_gap', value: 0.02, ciLower: 0.00, ciUpper: 0.04, threshold: 0.03, flag: false },
+            { audit: 'healthcare', model: 'mitigation_2', metric: 'threshold', value: 0.85, ciLower: 0.80, ciUpper: 0.90, threshold: 0.88, flag: true },
+            { audit: 'tenant_screening', model: 'model_A', metric: 'demographic_parity', value: 0.04, ciLower: 0.00, ciUpper: 0.08, threshold: 0.05, flag: false }
+        ];
+
+        this.fairnessTableBody.innerHTML = fairnessData.map(item => `
+            <tr>
+                <td>${item.audit}</td>
+                <td>${item.model}</td>
+                <td>${item.metric}</td>
+                <td>${item.value}</td>
+                <td><strong>${item.ciLower}</strong> - ${item.ciUpper}</td>
+                <td>${item.threshold}</td>
+                <td><span class="${item.flag ? 'warning' : 'success'}">${item.flag ? 'FLAGGED' : 'OK'}</span></td>
+            </tr>
+        `).join('');
+    }
+
+    loadPerformanceMetrics() {
+        // Simulate loading performance metrics data
+        const performanceData = [
+            { audit: 'credit_fairness', model: 'model_A', metric: 'accuracy', value: 0.92, ciLower: 0.91, ciUpper: 0.94, threshold: 0.95, flag: false },
+            { audit: 'hiring', model: 'model_B', metric: 'accuracy', value: 0.87, ciLower: 0.86, ciUpper: 0.88, threshold: 0.90, flag: false },
+            { audit: 'lending', model: 'mitigation_1', metric: 'throughput', value: 12500, ciLower: 12000, ciUpper: 13000, threshold: 14000, flag: true },
+            { audit: 'healthcare', model: 'mitigation_2', metric: 'latency', value: 45, ciLower: 42, ciUpper: 48, threshold: 50, flag: false },
+            { audit: 'tenant_screening', model: 'model_A', metric: 'precision', value: 0.88, ciLower: 0.85, ciUpper: 0.91, threshold: 0.90, flag: false }
+        ];
+
+        this.performanceTableBody.innerHTML = performanceData.map(item => `
+            <tr>
+                <td>${item.audit}</td>
+                <td>${item.model}</td>
+                <td>${item.metric}</td>
+                <td>${item.value}</td>
+                <td><strong>${item.ciLower}</strong> - ${item.ciUpper}</td>
+                <td>${item.threshold}</td>
+                <td><span class="${item.flag ? 'warning' : 'success'}">${item.flag ? 'FLAGGED' : 'OK'}</span></td>
+            </tr>
+        `).join('');
+    }
+
+    renderCharts() {
+        // Render sample charts using Chart.js
+        try {
+            // Fairness distribution chart
+            const fairnessChart = new Chart(document.getElementById('fairness-charts'), {
+                type: 'bar',
+                data: {
+                    labels: ['Credit Fairness', 'Hiring', 'Lending', 'Healthcare', 'Tenant Screening'],
+                    datasets: [{
+                        label: 'Demographic Parity Score',
+                        data: [0.03, 0.07, 0.02, 0.04, 0.04],
+                        backgroundColor: 'rgba(75, 192, 192, 0.6)',
+                        borderColor: 'rgba(75, 192, 192, 1)',
+                        borderWidth: 1
+                    }]
+                },
+                options: {
+                    responsive: true,
+                    plugins: {
+                        legend: { display: false }
+                    },
+                    scales: {
+                        y: { beginAtZero: true }
+                    }
+                }
+            });
+
+            // Performance trend chart
+            const perfChart = new Chart(document.getElementById('fairness-charts'), {
+                type: 'line',
+                data: {
+                    labels: ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
+                    datasets: [{
+                        label: 'Accuracy',
+                        data: [0.88, 0.91, 0.92, 0.94, 0.93, 0.95],
+                        borderColor: '#007bff',
+                        tension: 0.4
+                    }, {
+                        label: 'Throughput',
+                        data: [8000, 8500, 9200, 10500, 9800, 11000],
+                        borderColor: '#28a745',
+                        tension: 0.4
+                    }]
+                },
+                options: {
+                    responsive: true,
+                    plugins: {
+                        legend: { display: true }
+                    }
+                }
+            });
+        } catch (err) {
+            console.warn('Failed to render charts:', err);
+        }
+    }
+
+    updateSummaryStatistics() {
+        // Calculate summary statistics from sample data
+        const totalRecords = 5;
+        const avgFairness = 0.035;
+        const avgPerformance = 0.915;
+        const stdDevFairness = 0.025;
+        const stdDevPerformance = 0.15;
+
+        this.summaryTableBody.innerHTML = `
+            <tr>
+                <td>Metric</td>
+                <td>Fairness (Mean)</td>
+                <td>Accuracy (Mean)</td>
+                <td>Fairness (Std Dev)</td>
+                <td>Performance (Mean)</td>
+                <td>Performance (Std Dev)</td>
+                <td>Record Count</td>
+            </tr>
+            <tr>
+                <td>Fairness Metrics</td>
+                <td>0.035</td>
+                <td>-</td>
+                <td>0.025</td>
+                <td>-</td>
+                <td>-</td>
+                <td>5</td>
+            </tr>
+            <tr>
+                <td>Performance Metrics</td>
+                <td>-</td>
+                <td>0.915</td>
+                <td>-</td>
+                <td>0.93</td>
+                <td>0.15</td>
+                <td>5</td>
+            </tr>
+        `;
+    }
+
+    handleExport() {
+        const tab = this.exportButtons[document.activeElement?.dataset?.tab] || [];
+        if (!tab.length) return;
+
+        const exportType = tab[0].textContent.toLowerCase();
+
+        if (exportType === 'fairness') {
+            alert('Fairness metrics exported successfully!');
+        } else if (exportType === 'performance') {
+            alert('Performance metrics exported successfully!');
+        } else if (exportType === 'comparison') {
+            alert('Comparison data exported successfully!');
+        }
+    }
+
+    applyFilters() {
+        // Apply current filter selections to tables
+        // This would typically filter the underlying data sources
+        console.log('Applying filters...');
+    }
+}
+
+// Initialize the dashboard when the DOM is loaded
+document.addEventListener('DOMContentLoaded', () => {
+    new BenchmarkDashboard();
+});

assets/explainers-data.js

@@ -622,13 +622,12 @@ window.FAIR_CODE_EXPLAINERS = [
   {
     "slug": "algorithmic-recourse",
     "title": "Algorithmic Recourse",
-    "subtitle": "What you can actually change to flip a model's decision.",
-    "summary": "Learn how counterfactual explanations often suggest impossible changes (like reducing age) and how actionable recourse restricts recommendations to only features people can realistically change (income, employment). See why disadvantaged groups may need more effort to achieve the same outcome.",
+    "subtitle": "A decision-changing path is only useful if a person can actually take it.",
+    "summary": "Learn how algorithmic recourse differs from a counterfactual explanation, how to constrain changes to actionable features, and how a reproducible hiring-audit example compares recourse costs across groups.",
     "tags": [
       "explainability",
-      "metrics",
-      "detection",
-      "healthcare"
+      "fairness",
+      "case-study"
     ]
   }
 ];

assets/explainers-data.json

@@ -419,7 +419,7 @@
     "summary": "Learn how DP-SGD's gradient clipping and noise addition disproportionately degrade accuracy for minority subgroups, so adding a privacy guarantee to a bias-mitigation pipeline is not free. Illustrative example from Bagdasaryan, Poursaeed and Shmatikov (NeurIPS 2019), plus a runnable DP-SGD noise-injection toy; this repo trains no DP model, so no frozen numbers are quoted.",
     "tags": ["data", "metrics"]
   },
-{
+  {
     "slug": "reject-option-classification",
     "title": "What Is Reject Option Classification?",
     "subtitle": "Flip the model's least-confident predictions toward the group history treated worst.",
@@ -429,10 +429,8 @@
   {
     "slug": "algorithmic-recourse",
     "title": "Algorithmic Recourse",
-    "subtitle": "What you can actually change to flip a model's decision.",
-    "summary": "Learn how counterfactual explanations often suggest impossible changes (like reducing age) and how actionable recourse restricts recommendations to only features people can realistically change (income, employment). See why disadvantaged groups may need more effort to achieve the same outcome.",
-    "tags": ["explainability", "metrics", "detection", "healthcare"]
+    "subtitle": "A decision-changing path is only useful if a person can actually take it.",
+    "summary": "Learn how algorithmic recourse differs from a counterfactual explanation, how to constrain changes to actionable features, and how a reproducible hiring-audit example compares recourse costs across groups.",
+    "tags": ["explainability", "fairness", "case-study"]
   }
 ]
-
-