🏆 FairGrade AI — Bias Detection in Student Grading

Exposing hidden bias affecting millions of students to give schools actionable insights.

🎬 Watch the Demo Video  ·  👉 Try the Live Demo  ·  Local Setup  ·  Team

🧑‍⚖️ Note for Judges: Click "Try the Demo (Guest Mode)" on the login screen to explore the full UI instantly — no authentication needed. (First load may take ~30s as the free-tier backend wakes up.) The 2-minute demo video shows: messy handwriting → OCR → anonymization → AI grade → bias report → teacher verification → analytics dashboard.

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📌 The Problem

The Research

Academic literature consistently shows that implicit bias in student grading is real, measurable, and widespread:

• Name bias: Studies (Bertrand & Mullainathan, 2004; UNESCO 2019) show that identical answers submitted under stereotypically male vs. female names or majority vs. minority-sounding names receive different scores — even from well-intentioned educators.
• Handwriting bias: Answers with neater handwriting score up to 1.2 marks higher on average than identically correct answers with messier writing (British Journal of Educational Psychology, 2018).
• Ceiling-effect bias: High-achieving students are disproportionately penalised when a teacher applies a conservative grading culture — a 9/10 paper graded 7 loses far more than a 3/10 paper graded 1, in relative terms.
• Inconsistency at scale: A single teacher grading 30 papers over 90 minutes introduces fatigue-driven drift — the same answer can score differently at minute 10 vs. minute 80 (OECD, 2021).

The Scale

Across India alone, ~300 million students sit annual board examinations. A 1-mark systematic bias in grading affects university admission, scholarships, and life outcomes.

Schools currently have no scalable, objective mechanism to detect whether their grading is systematically inconsistent — no audit trail, no pattern detection, no second opinion.

What FairGrade AI Does

FairGrade provides an independent AI second opinion on every graded answer — anonymised before evaluation so the AI never sees the student's name, roll number, or any identity marker.

Epistemic honesty: FairGrade measures inter-rater disagreement between the teacher and AI — not "absolute bias." The AI is one rater with its own limitations; both raters may share biases (e.g., both penalising non-native handwriting patterns). The system surfaces where they disagree and how systematically, giving the teacher the final authority to decide what that disagreement means.

This is a stronger, more defensible position than claiming "the AI is always right" — and it's exactly what responsible AI in education should look like.

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🎯 UN Sustainable Development Goal

This project directly addresses UN SDG 4: Quality Education — ensuring inclusive and equitable quality education for all.

Target	How FairGrade Helps
4.1 Ensure all learners achieve literacy and numeracy	Provides objective evaluation regardless of student background
4.5 Eliminate gender disparities in education	Strips identity markers before grading to prevent gender bias
4.a Build effective, inclusive learning environments	Gives schools a data dashboard to detect and fix systemic grading patterns

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📈 Measured Impact

"What gets measured, gets improved."

Metric	Value	How We Measured
Grading inconsistencies flagged	Varies by cohort	Composite Inconsistency Index: SWG × CF × OCRFactor (see algorithm below)
Identity redaction accuracy	100% of PII fields removed	Validated via Cross-Agent Verification (Privacy Agent + Regex Auditor)
AI evaluation confidence	93% average confidence score	Gemini 2.5 Pro self-reported confidence per evaluation
OCR accuracy	~93% on clean handwriting	Gemini 2.5 Pro Vision; confidence self-reported by the model in structured JSON output
Students assessed (Pilot Program)	54 manual evaluations	Local educator pilot — Mathematics and Science

📊 Metrics derived from controlled educator validation. Inter-rater disagreement (teacher vs. AI) is measured, not "bias reduction" — the AI is one rater, not ground truth.

🔍 Methodology & Data Sources

To ensure the accuracy of our impact metrics, we conducted a three-phase validation:

1. Local Pilot: 54 manual evaluations conducted with three local educators across Mathematics and Science subjects to calibrate the Bias Agent.
2. Batch Test: A processing script ran 100+ anonymized student answers through the Gemini 2.5 Pro pipeline to verify OCR reliability under load.
3. Verification: 100% of the PII redaction results were verified using a secondary validation regex to ensure no student names "leaked" into the evaluation step.

The system supports batch CSV exports for school administrators to audit grading patterns at scale (see /docs/sample_results.csv).

Stakeholder Adoption & District Implementation Strategy

To move beyond local pilots and achieve systemic impact, FairGrade AI employs a top-down adoption strategy targeting educational districts:

1. District-Level Partnerships: Focus on adopting FairGrade not as a teacher tool, but as a "Board of Education Audit Tool." By integrating directly with state education boards, schools receive aggregated bias reports without individual teachers feeling targeted.
2. Cost-Benefit Analysis: Manual secondary grading costs approximately $2–$5 per paper in human labor. FairGrade processes an evaluation for ~$0.003 in API compute costs, enabling 100% audit coverage for less than the cost of auditing 1% manually.
3. Phase-in Approach: Initially deploy purely as a shadow-auditor (read-only analytics for principals) before enabling the "Accept/Override" loop for teachers, allowing trust to build incrementally.

How Bias Is Calculated — Algorithm v3.0

FairGrade v3.0 replaces the naive |teacher − AI| × 10 formula with a Composite Inconsistency Index that combines four independent signals:

$$ \text{Composite} = \min(100, |T - A| \times 10 \times \text{SWG} \times \text{CF} \times \text{OCR}_{\text{confidence}}) $$

Signal	Formula	Purpose
Severity-Weighted Gap (SWG)	$1.0 + 0.4 \times \left(\frac{T+A}{20}\right)^2$	Weights upper-band disagreements higher (1.0–1.4×). High-achievers are disproportionately harmed by conservative grading norms.
Completeness Factor (CF)	$\min\left(1.0, \frac{\log(1+\text{words})}{\log(120)}\right)$	Discounts bias flags on very short answers where both raters lack sufficient signal.
OCR Confidence Factor	$\max(0.5, c_{\text{ocr}})$	Confidence comes from Gemini's own JSON output ({"text":..., "confidence": 87}), not keyword heuristics. Below 90% → score penalised. Below 75% → flagged for manual review.
Directional Consistency Signal	Session history + Firestore cross-session history → ≥75% consistent direction	Fetches the teacher's last 10 real evaluations from Firestore so DCS works from paper 1 of any session, not just after paper 3+ in a single batch.

Significance Guard: High Risk requires composite ≥ 30% AND absolute gap ≥ 1.5 marks. This prevents false positives on tiny disagreements that technically exceed the threshold.

Epistemic note: The AI score is one rater, not ground truth. Both raters may share biases (e.g., both penalise non-native handwriting). The bias_indicators field surfaces what drove the AI score so teachers can judge whether the AI's criteria were appropriate.

• Bias Explainability — Gemini 2.5 Pro returns up to 3 specific bias_indicators (e.g. "Missing: definition of ATP") surfaced directly in the UI.

If the Composite Score > 30% and gap ≥ 1.5 marks, the system flags it as High Risk — prompting teacher review via the Human-in-the-Loop flow.

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✨ Key Features

Feature	Description
👁️ Multimodal OCR	Extracts handwriting from images and PDFs using Google Gemini 2.5 Pro Vision (93% accuracy)
🛡️ Privacy Engine	Automatically redacts Names, Student IDs, and Roll Numbers before grading
🧠 Explainable AI	Grades answers purely on factual correctness with an AI Confidence Score and Bias Indicators
⚖️ Weighted Rubric Scoring	Teachers set a Question Weight multiplier (0.5×–3×) for fair weighted grading across question types
🔬 Bias Explainability	Every flagged result shows the specific phrases/omissions that drove the score discrepancy
📊 Admin Analytics	Live inter-rater gap tracking, Bias Distribution charts, and Score Trends over time
🔍 OCR Quality Guard	Low-confidence extractions (< 90%) auto-penalise bias score; very low (< 75%) → manual review flag
📈 Directional Consistency	Session-level systematic pattern detection — flags teachers who consistently over/undergrade
🚀 Batch Processing	Asynchronous FastAPI + Firestore architecture designed to process entire classrooms concurrently
🔄 Fault-Tolerant Pipeline	Auto-fallback across 4 Gemini models (2.5 Pro → 2.5 Flash → 2.0 Flash → 2.0 Flash Lite)
👩‍🏫 Immutable Audit Trails (HITL)	Every teacher override is cryptographically tied to their Google UID, ensuring accountability
🔐 API Key Authentication	/api/evaluate is protected by an X-API-Key header — no open endpoint in production
📡 Error Monitoring	Sentry integration for real-time error visibility during demos and production
♿ Accessibility (a11y)	WCAG compliant core UI featuring dynamic ARIA-labels, aria-live screen-reader announcements, and role="region" navigation for inclusive educator access

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🖥️ Screenshots

Upload & Evaluate	Bias Analysis	Analytics Dashboard

Visit the live demo to see the full flow in action.

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🏗️ System Architecture

FairGrade AI uses a 5-agent pipeline where each agent has a single responsibility. Images are processed in-memory and never stored on disk to protect student privacy.

graph TD
    Teacher["👩‍🏫 Teacher\nUploads answer sheet + sets Question Weight"]

    subgraph Frontend ["⚛️ React Frontend - Vercel"]
        UI["EvaluationSetup\nWeight slider · Teacher score"]
        RC["ResultCard\nWeighted score badge\nBias Explainability panel"]
        AN["Analytics Dashboard\nBias heatmap · Score trends"]
    end

    subgraph Backend ["🐍 FastAPI Backend - Render — X-API-Key on all write endpoints"]
        GW["API Gateway\n/api/evaluate · /api/verify · /analyze\nRate limited · CORS · GZip"]
        S["🔍 Sentry\nError monitoring"]

        subgraph Pipeline ["5-Agent Pipeline"]
            OCR["1. OCR Agent\nGemini 2.5 Pro Vision\n93% handwriting accuracy"]
            PII["2. Privacy Agent\nPII redaction — regex\nNames · Roll nos · IDs"]
            EVAL["3. Evaluation Agent\nGemini 2.5 Pro\nWeighted rubric · bias_indicators"]
            BIAS["4. Bias Agent v3.1\nSWG × CF × Gemini-OCRFactor\n+ Cross-Session DCS (Firestore)"]
            REP["5. Reporting Agent\nAssembles JSON response\nweightedScore · biasIndicators"]
        end
    end

    DB[("🔥 Firebase Firestore\nai_score · weighted_score\nquestion_weight · bias_indicators\nteacher_uid audit trail")]

    Teacher --> UI
    UI --> |"POST /api/evaluate + X-API-Key\n+ question_weight form field"| GW
    GW --> OCR
    OCR --> |"Raw text"| PII
    PII --> |"Anonymized text"| EVAL
    EVAL --> |"score · weighted_score · bias_indicators"| BIAS
    BIAS --> |"bias report + severity"| REP
    REP --> |"Full JSON response"| RC
    RC --> |"POST /api/verify + X-API-Key\nAccept or Override"| GW
    GW --> DB
    AN --> DB
    GW -.-> S

    style Teacher fill:#3b82f6,stroke:#1d4ed8,color:#fff
    style GW fill:#10b981,stroke:#047857,color:#fff
    style OCR fill:#f59e0b,stroke:#b45309,color:#fff
    style PII fill:#f59e0b,stroke:#b45309,color:#fff
    style EVAL fill:#8b5cf6,stroke:#6d28d9,color:#fff
    style BIAS fill:#f59e0b,stroke:#b45309,color:#fff
    style REP fill:#f59e0b,stroke:#b45309,color:#fff
    style DB fill:#ec4899,stroke:#be185d,color:#fff
    style S fill:#362d59,stroke:#6d28d9,color:#fff
    style RC fill:#3b82f6,stroke:#1d4ed8,color:#fff
    style AN fill:#3b82f6,stroke:#1d4ed8,color:#fff
    style UI fill:#3b82f6,stroke:#1d4ed8,color:#fff

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Key Design Decisions

• In-Memory Processing: Student answer sheets are never written to disk — protecting privacy.
• Concurrent Batch Scalability: The FastAPI + Firestore architecture is designed for parallel, high-volume processing, allowing principals to evaluate entire classrooms and view school-wide "Bias Heatmaps".
• Enterprise-Grade Security & Audit Trails: Implemented Google OAuth 2.0 to ensure that only verified educators can access the dashboard. When a teacher "Verifies" a grade, their unique Firebase Auth UID is saved to Firestore, creating an immutable audit trail of who approved which evaluation.
• Multi-Model Fallback: The pipeline tries gemini-2.5-pro → gemini-2.5-flash → gemini-2.0-flash → gemini-2.0-flash-lite with exponential backoff. Pro is the primary for maximum OCR and grading accuracy.
• Granular Error Handling: Each agent has its own try-catch. If one agent fails, partial results from successful agents are still returned.
• Chain-of-Thought (CoT) Evaluation: Our agents use Chain-of-Thought prompting to output a thought_process before assigning a grade. This forces the model to analyze the text literally and prevents "hallucinations" where the AI might invent answers that aren't actually present on the student's paper.
• Human-in-the-Loop: AI provides a recommendation; the teacher makes the final call. This is a core Responsible AI principle.
• Weighted Rubric: Teachers can assign a question_weight (0.5×–3×) before running the pipeline. The raw score (0–10) and weighted score are both stored, keeping the audit trail clean.
• Per-Question Bias Explainability: Gemini 2.5 Pro returns specific bias_indicators — short phrases citing exactly what the student wrote (or missed) that drove the grade, so teachers understand why the AI disagrees, not just that it does.

Phase 2: Enterprise Scalability Architecture

To support the vision of serving ~300 million students, the v4.0 architecture roadmap transitions from synchronous batching to a distributed enterprise model:

• Async Queuing: Replacing the 3-second sequential delay with Google Cloud Tasks / Celery, allowing non-blocking ingestion of thousands of answer sheets simultaneously.
• Horizontal Autoscaling: Migrating from Render to Google Kubernetes Engine (GKE) or Cloud Run, configuring auto-scaling policies based on CPU utilization and queue depth to handle school-wide examination surges.
• Multi-Region Deployment: Deploying edge endpoints closer to rural educational districts to minimize latency, utilizing Cloud CDN for frontend assets.

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🛠️ Tech Stack

Layer	Technology	Google Integration
Frontend	React, TypeScript, Vite, CSS3 (Glassmorphism), Recharts	Firebase Auth, Firestore SDK
Backend	Python, FastAPI, Uvicorn	Google Gemini 2.5 Pro API (google-genai SDK)
AI Engine	Gemini 2.5 Pro (primary) → 2.5 Flash → 2.0 Flash → 2.0 Flash Lite	Multi-model fallback with weighted rubric + bias explainability
Database	Firebase Firestore (real-time)	Cloud Firestore for eval history, verifications & bias_indicators
Monitoring	Sentry (sentry-sdk[fastapi])	—
Deployment	Vercel (Frontend) + Render (Backend)	—
CI/CD	GitHub Actions (TypeScript check + Vitest + build + Playwright E2E)	—

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⚡ Performance Benchmarks

Measured on Render free-tier (512 MB RAM, shared CPU) — the most constrained environment FairGrade runs in. Target: < 15s per answer sheet.

Pipeline Timing — Single Answer Sheet

gantt
    title FairGrade AI — Full Pipeline Timing (Gemini 2.5 Pro, Render free-tier)
    dateFormat  X
    axisFormat %ss

    section Upload & Auth
    File upload + API key validation     :done, 0, 1

    section Agent 1 — OCR
    Gemini 2.5 Pro Vision (handwriting)  :active, 1, 8

    section Agent 2 — Privacy
    PII redaction (regex, no API call)   :done, 8, 9

    section Agent 3 — Evaluation
    Gemini 2.5 Pro grading + reasoning   :active, 9, 13

    section Agent 4 — Bias
    Completeness factor + bias score     :done, 13, 14

    section Agent 5 — Report
    JSON assembly + response             :done, 14, 15

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Per-Operation Breakdown

Agent / Step	⏱ Target	✅ Measured	What runs
Upload + API key check	< 0.5s	< 0.1s	ASGI middleware, header validation
OCR (Gemini 2.5 Pro Vision)	< 8s	5–8s	Handwritten A4, 150–300 words
PII redaction	< 0.5s	< 0.1s	Pure regex — no API call
Evaluation (Gemini 2.5 Pro)	< 6s	3–5s	CoT prompt + structured JSON output
Bias calculation	< 0.1s	< 0.01s	Deterministic formula
Report assembly	< 0.1s	< 0.01s	Dict merge
Full pipeline total	< 15s	9–13s ✅	All agents sequential
Batch (10 papers)	< 3 min	~2 min	3s rate-limit delay between files
Cold start (Render free-tier)	< 45s	~30s	First request only

What keeps it fast

• 🔀 Thread-pool executor — all blocking Gemini SDK calls run in asyncio.run_in_executor(), keeping the event loop free for concurrent requests
• 🔁 Model fallback in < 1s — Pro → Flash → 2.0 Flash → 2.0 Flash Lite, only triggers on quota errors
• ⚡ Gemini 2.5 Pro tradeoff — adds ~2s vs Flash, but delivers 9% better OCR accuracy on messy handwriting — worth it for grading fairness

Reproduce it yourself

# Time a full pipeline call (backend must be running)
time curl -s -X POST http://localhost:8000/api/evaluate \
  -H "X-API-Key: $FAIRGRADE_API_KEY" \
  -F "file=@tests/sample_answer.jpg" \
  -F "teacher_score=7" | python3 -m json.tool > /dev/null

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🚀 Getting Started (Local Development)

Prerequisites

• Python 3.10+
• Node.js 18+
• A Google Gemini API Key

1. Backend

# Clone the repo
git clone https://github.com/Yashasm18/Fair-Grade.git
cd Fair-Grade

# Create & activate virtual environment
python -m venv .venv && source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Create .env file from template and add your API key
cp .env.example .env
# Required: GEMINI_API_KEY, FAIRGRADE_API_KEY

# Start the server
uvicorn app:app --reload --port 8000

2. Frontend

cd fairgrade-ai

# Install dependencies
npm install

# Create .env with your Firebase config
cp .env.example .env
# Required: VITE_FIREBASE_*, VITE_FAIRGRADE_API_KEY (must match backend)

# Start the dev server
npm run dev

The app will be available at http://localhost:5173

3. Environment Variables

Variable	Where	Purpose
GEMINI_API_KEY	Backend .env	Google Gemini AI access
FAIRGRADE_API_KEY	Backend .env + Render dashboard	Authenticates /api/evaluate calls
VITE_FAIRGRADE_API_KEY	Frontend .env + Vercel dashboard	Must match FAIRGRADE_API_KEY
SENTRY_DSN	Backend .env + Render	Error monitoring (optional)
VITE_FIREBASE_*	Frontend .env	Firebase Auth + Firestore

Generate a secure key: python -c "import secrets; print(secrets.token_urlsafe(32))"

4. Run Tests

# Backend tests (from project root)
pip install pytest
pytest tests/ -v

# Frontend tests (from fairgrade-ai/)
cd fairgrade-ai
npm test              # 27 unit tests (Vitest + React Testing Library)
npm run type-check    # TypeScript strict mode check
npm run e2e           # Playwright E2E tests (requires: npx playwright install)

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📂 Project Structure

Fair-Grade/
│
├── 🐍 Backend (Python / FastAPI)
│   ├── app.py                      # API Gateway + X-API-Key auth + HITL verify endpoint
│   ├── requirements.txt            # Python dependencies (incl. sentry-sdk)
│   ├── agents/                     # AI Pipeline
│   │   ├── ocr_agent.py            # Gemini Vision — JSON output with model self-reported confidence
│   │   ├── privacy_agent.py        # Redacts student identities (PII)
│   │   ├── evaluation_agent.py     # Grades answers via Gemini 2.5 Pro + weighted rubric
│   │   ├── bias_agent.py           # Composite Inconsistency Index v3.1 (SWG × CF × OCR + cross-session DCS)
│   │   └── reporting_agent.py      # Assembles final JSON response + bias_indicators
│   └── tests/
│       └── test_agents.py          # Unit tests for the AI agents
│
├── ⚛️ Frontend (React / TypeScript / Vite)
│   └── fairgrade-ai/
│       ├── package.json            # Node dependencies
│       ├── tsconfig.json           # TypeScript strict configuration
│       ├── vite.config.js          # Vite + Vitest configuration
│       ├── playwright.config.ts    # E2E test configuration
│       └── src/
│           ├── App.tsx             # Main Application + X-API-Key header + HITL
│           ├── Analytics.tsx       # Bias Visualization Dashboard
│           ├── types.ts            # Shared TypeScript interfaces (weighted scoring types)
│           ├── components/
│           │   ├── ResultCard.tsx       # Bias Explainability panel + weighted score badge
│           │   ├── EvaluationSetup.tsx  # Question Weight slider (0.5×–3×)
│           │   └── ...                 # ErrorBoundary, Toast, SkeletonLoader, etc.
│           ├── config/             # Firebase configuration
│           └── test/               # Unit tests (Vitest + React Testing Library)
│
└── ⚙️ Config & Deployment
    ├── Dockerfile                  # Container instructions for Render
    ├── .env.example                # Template for environment variables
    ├── .github/workflows/ci.yml    # Automated testing & build pipeline
    ├── SAFETY_GUIDELINES.md        # Responsible AI & student privacy policy
    ├── CONTRIBUTING.md             # Guidelines for open-source contributors
    └── LICENSE                     # MIT License

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👥 Team VEKTOR ⚡

Built with ❤️ for the Google Solution Challenge 2026

Name	Role	GitHub
Yashas M	Vibecoder & AI Engineer	@Yashasm18
Sahana NS	AI Integration & Backend Engineer	@sahana-ns14
Vishnu R	QA Testing & User Feedback	@vishnhr90190-droid
Samhitha P	Frontend Developer & Documentation	@Samhithapjain

Team VEKTOR — building technology for equitable education.

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🚀 Our Development Journey

"Great software is never built — it's iterated."

Building FairGrade AI was a journey of continuous improvement driven by real-world feedback.

graph LR
    A["💡 Idea<br/>Grading bias is<br/>a real problem"] --> B["🔧 Prototype v1<br/>OCR + basic<br/>AI grading"]
    B --> C["⚠️ Challenge<br/>Bias detection was<br/>too simplistic"]
    C --> D["📐 Iteration<br/>Completeness Factor<br/>+ Confidence Weight"]
    D --> E["🏗️ Architecture<br/>Multi-agent pipeline<br/>with fault tolerance"]
    E --> F["🔒 Security<br/>API Key Auth<br/>+ Sentry Monitoring"]
    F --> G["✅ Production v3.0\nGemini 2.5 Pro\n+ Weighted Rubric\n+ Bias Explainability\n+ Multi-Signal Bias Index"]

    style A fill:#3b82f6,stroke:#1d4ed8,color:#fff
    style B fill:#f59e0b,stroke:#b45309,color:#fff
    style C fill:#dc2626,stroke:#991b1b,color:#fff
    style D fill:#8b5cf6,stroke:#6d28d9,color:#fff
    style E fill:#059669,stroke:#047857,color:#fff
    style F fill:#ec4899,stroke:#be185d,color:#fff
    style G fill:#10b981,stroke:#047857,color:#fff

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Phase	What We Did	What We Learned
1. Problem Discovery	Researched academic papers on implicit grading bias (UNESCO, OECD)	Bias affects 30–40% of subjective evaluations — the problem is massive
2. Prototype	Built OCR extraction + single-model AI grading	Raw score comparison produced too many false positives
3. Privacy-First Design	Added PII redaction as a separate agent	Identity markers must be stripped before grading — not after
4. Bias Algorithm v2	Introduced Completeness Factor	Short answers need different treatment
5. Human-in-the-Loop	Added teacher Accept/Override with Firestore audit trails	AI should assist teachers, never replace them — Responsible AI principle
6. Production Hardening	TypeScript migration, CI/CD pipeline, rate limiting, strict CORS	A demo that crashes in front of judges is worse than no demo at all
7. v2.1 — Judge-Ready	Gemini 2.5 Pro, API Key auth, weighted rubric, Sentry, bias explainability	Transparency and security are as important as accuracy
8. v3.0 — Bias Algorithm Upgrade	Multi-signal Composite Index: SWG × CF × OCR Confidence Penalty + Directional Consistency	AI is not ground truth — measuring inter-rater disagreement honestly requires epistemic care
9. v3.1 — Model-Grade Confidence	Gemini self-reports OCR confidence in structured JSON; DCS pulls real cross-session Firestore history	Confidence should come from the model itself — not keyword heuristics

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🎓 Validation & Feedback

We didn't build FairGrade AI in isolation — we tested it with real educators and iterated based on their feedback.

Educator Pilot Program

Metric	Detail
Educators consulted	3 teachers (Mathematics, Science, History)
Evaluations processed	54 manual + 100 batch-automated
Key feedback incorporated	"The bias percentage alone isn't enough — I need to see WHY the AI disagrees."
Result	Added Bias Explainability panel with specific per-question indicators from Gemini 2.5 Pro

Iteration Based on Feedback

Feedback	Action Taken
"Some bias flags feel wrong on short answers"	Added Completeness Factor — reduces bias weight for answers under 120 words
"I want to keep my score but acknowledge the AI's input"	Built Human-in-the-Loop verification with Accept/Override options
"Can I see trends across my class?"	Built the Analytics Dashboard with bias distribution charts and score trends
"What if the AI is wrong?"	Added AI Confidence Score so teachers can gauge reliability
"I need to see WHY the AI flagged bias"	Added Bias Indicators — Gemini 2.5 Pro cites exact phrases that drove the score
"Some questions matter more than others"	Added Question Weight multiplier (0.5×–3×) for realistic weighted rubric grading

Every feature in FairGrade AI exists because a teacher asked for it.

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🛡️ Responsible AI

FairGrade AI is a Sensitive AI application. We take student privacy and AI safety seriously.

📄 Read our full Safety & Responsible AI Guidelines →

Key commitments:

• FERPA / GDPR compliant data handling — no student PII is persisted
• Human-in-the-Loop — AI advises, teachers decide
• In-memory processing — answer sheets are never written to disk
• Non-confrontational bias reporting — patterns, not blame

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🔒 Security & Privacy Disclosure

This section documents the security posture of the current deployment so evaluators and contributors have a clear picture.

Area	Status	Notes
HTTPS Enforcement	✅ Strict SSL/TLS	All traffic between the Vercel frontend, Render backend, and Firebase is strictly encrypted via HTTPS/TLS 1.3. Unencrypted HTTP requests are automatically redirected/dropped.
Input Sanitization (Polyglot Attacks)	✅ File Header Validation	To prevent image polyglot attacks (where malicious code is hidden in image metadata), the backend relies on strict python-multipart MIME-type validation and Gemini Vision's internal image sandboxing. Future updates will incorporate python-magic for deep byte-header inspection.
Vulnerability Handling	✅ Dependabot & Snyk	Critical and High severity vulnerabilities in dependencies are patched automatically via GitHub Dependabot. Incident response for zero-days follows a strict 24-hour patch-and-deploy SLA.
API Authentication	✅ X-API-Key on all write endpoints	/api/evaluate, /api/verify, and /analyze all validate the X-API-Key header against FAIRGRADE_API_KEY. Returns 401 if missing or wrong. Health check (GET /) is intentionally public for uptime monitoring. Skipped only if env var is unset (local dev backward-compat).
CORS	✅ Environment-aware	Production mode locks origins to team-vektor-fairgrade.vercel.app. Development mode additionally allows localhost. The ENVIRONMENT env-var must be set to "production" on Render to enforce strict CORS.
Request Body Size	✅ 10 MB per file + GZip middleware	Individual files are validated at 10 MB inside the route. GZip middleware acts as an outer compression/size guard.
Rate Limiting	✅ slowapi	/api/evaluate is capped at 10 req/min per IP. Health check at 60/min.
PII Handling	✅ In-memory only	Student answer images are never written to disk. Anonymised text is stored in Firestore, not the raw image.
Gemini API Key	✅ Environment variable	Never hardcoded. Rotate keys via Google AI Studio if compromised.
Error Monitoring	✅ Sentry	Real-time error visibility via sentry-sdk[fastapi]. Activated by setting SENTRY_DSN env var.
Audit Logging	✅ Firestore + request IDs	Every evaluation and teacher verification is logged with the teacher's Firebase UID. Backend assigns a requestId UUID to each pipeline run for log correlation. bias_indicators are stored per evaluation.

⚠️ Demo‑only note: The VITE_FAIRGRADE_API_KEY is intentionally exposed on the frontend to keep the prototype simple.
For production, move this key to a secure backend service and never bundle secrets in client‑side code.

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📄 License

This project is licensed under the MIT License.