FairSense-AgentiX

An agentic fairness and AI-risk analysis platform developed by the Vector Institute.

FairSense-AgentiX is an intelligent bias detection and risk assessment platform that uses agentic AI workflows to analyze text, images, and datasets for fairness concerns. Unlike traditional ML classifiers, FairSense employs a reasoning agent that plans, selects tools, critiques outputs, and refines them iteratively.

✨ Key Features

• 🤖 Agentic Reasoning - ReAct loop with dynamic tool selection and self-critique
• 🔍 Multi-Modal Analysis - Text bias detection, image bias detection, and AI risk assessment
• 🛠️ Flexible Tool Ecosystem - OCR, Vision-Language Models, embeddings, FAISS, and LLMs
• 🌐 Production-Ready APIs - FastAPI REST API + WebSocket streaming + React UI
• ⚙️ Highly Configurable - Swap LLM providers, tools, and models on the fly

📦 Installation

From PyPI (Recommended)

pip install fairsense-agentix

Optional: PaddleOCR backend

The default OCR backend is Tesseract (CPU-friendly, no extra setup). For GPU-accelerated PaddleOCR (higher accuracy on rotated/complex text), install the paddle extra:

pip install fairsense-agentix[paddle]

This pulls in paddlepaddle, which has platform-specific wheels and is much heavier — only install if you need it. Once installed, set FAIRSENSE_OCR_TOOL=paddleocr (or auto to let the runtime pick based on GPU availability).

From Source

git clone https://github.com/VectorInstitute/fairsense-agentix.git
cd fairsense-agentix
uv sync
source .venv/bin/activate

Requirements

• Python 3.12 (3.13 not yet supported)
• 4GB+ RAM (for ML models)
• API key for OpenAI or Anthropic (for LLM functionality)

🚀 Quick Start

First point FairSense at a real LLM provider. The default provider is fake — it lets the package import and the tests run without an API key, but in that mode every result is a synthetic placeholder, not a real analysis (FairSense warns at construction, sets result.metadata.mock_mode = True, and prefixes result.warnings with MOCK MODE).

export FAIRSENSE_LLM_PROVIDER=openai   # or: anthropic
export FAIRSENSE_LLM_API_KEY=sk-...

from fairsense_agentix import FairSense

# Initialize the engine
engine = FairSense()

# Analyze text for bias
result = engine.analyze_text(
    "We're looking for a young, energetic developer to join our startup team."
)

print(f"Bias detected: {result.bias_detected}")
print(f"Risk level: {result.risk_level}")
for instance in result.bias_instances:
    print(f"  - {instance['type']} ({instance['severity']}): {instance['text_span']}")

Full Documentation: https://vectorinstitute.github.io/fairsense-agentix/

🧑🏿‍💻 Developing

Installing dependencies

The development environment can be set up using uv. Hence, make sure it is installed and then run:

uv sync
source .venv/bin/activate

In order to install dependencies for testing (codestyle, unit tests, integration tests), run:

uv sync --dev
source .venv/bin/activate

In order to exclude installation of packages from a specific group (e.g. docs), run:

uv sync --no-group docs

Getting Started

Run the FastAPI service

python -m fairsense_agentix.service_api          # honours FAIRSENSE_API_HOST / PORT
# or: uv run uvicorn fairsense_agentix.service_api.server:app --host 127.0.0.1 --reload

Endpoints (all under /v1/...):

The API auto-detects text/image/CSV inputs, but you can override by setting input_type to bias_text, bias_image, or risk.

Run the Claude-inspired UI

The UI lives in ui/ in this repository (it is not part of the PyPI wheel) and needs Node.js:

cd ui
npm install
npm run dev

Or start backend + UI together from the checkout with python -c "from fairsense_agentix import server; server.start()".

Set VITE_API_BASE (defaults to http://localhost:8000) to point at the API. The UI provides:

• Unified input surface (text field + drag/drop image upload)
• Live agent timeline sourced from telemetry events
• Downloadable HTML highlights and risk tables
• Launchpad for batch jobs

Key configuration knobs

Configure via environment variables (see .env for the full list). Most relevant:

All settings can be overridden at runtime:

FAIRSENSE_LLM_PROVIDER=anthropic \
FAIRSENSE_LLM_MODEL_NAME=claude-3-5-sonnet-20241022 \
uv run uvicorn fairsense_agentix.service_api.server:app

Running tests

uv run pytest

During test collection we automatically override any .env values that point at real providers/devices so the suite always uses the lightweight fake toolchain. This guarantees deterministic, offline-friendly tests even if you have FAIRSENSE_LLM_PROVIDER=openai (or Anthropic) configured locally. To opt-in to exercising the real stack, export FAIRSENSE_TEST_USE_REAL=1 before running pytest.

Acknowledgments

Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.

This research was funded by the European Union's Horizon Europe research and innovation programme under the AIXPERT project (Grant Agreement No. 101214389).

Contributing

If you are interested in contributing to the library, please see CONTRIBUTING.md. This file contains many details around contributing to the code base, including development practices, code checks, tests, and more.

For support or collaborations: shaina.raza@vectorinstitute.ai