Fairsense-AI

Fairsense-AI is a cutting edge, an AI-driven tool designed to analyze bias in text and visual content with sustainability in mind. It also offers a platform for risk identification and risk mitigation. With a strong emphasis on Bias Identification, Risk Management, and Sustainability, Fairsense-AI helps build trustworthy AI systems.

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Installation and Setup

Step 1: Install supporting tools

1. Python 3.10+
   Ensure Python is installed. Download it here.

2. Tesseract OCR
   Required for extracting text from images.

   Installation Instructions:

   • Ubuntu:

     sudo apt-get update
     sudo apt-get install tesseract-ocr

   • macOS (Homebrew):

     brew install tesseract

   • Windows:
     Download and install Tesseract OCR from this link.

3. Ollama (for CPU only)

   Ollama is a tool that easily installs versions of Llama that are capable of running on CPU. If the machine does not have a GPU, this is a required step.

   1. Download and install Ollama here. Make sure to also install the CLI tool.

   2. After that, please pre-download the Llama 3.2 model with the command below:

   ollama pull llama3.2

4. Optional (GPU Acceleration)
   Install PyTorch with CUDA support:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

Step 2: Install Fairsense-AI

Install the fair-sense-ai package using pip:

pip install fair-sense-ai

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Quickstart Code Examples

1. Text Bias Analysis

from fairsenseai.analysis.bias import analyze_text_for_bias

# Example input text to analyze for bias
text_input = "Men are naturally better at decision-making, while women excel at emotional tasks."

# Analyze the text for bias
highlighted_text, detailed_analysis = analyze_text_for_bias(text_input)

# Print the analysis results
print("Highlighted Text:", highlighted_text)
print("Detailed Analysis:", detailed_analysis)

2. Image Bias Analysis

import requests
from PIL import Image
from io import BytesIO
from fairsenseai.analysis.bias import analyze_image_for_bias

# URL of the image to analyze
image_url = "https://media.top1000funds.com/wp-content/uploads/2019/12/iStock-525807555.jpg"

# Fetch and load the image
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))

# Analyze the image for bias
highlighted_caption, image_analysis = analyze_image_for_bias(image)

# Print the analysis results
print("Highlighted Caption:", highlighted_caption)
print("Image Analysis:", image_analysis)

3. Launch the Interactive Application

from fairsenseai.app import start_server

# Launch the Gradio application (will open in the browser)
start_server()

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Bias Detection Tutorial

Data and Sample Notebooks

1. Download the Data:
   Google Drive Link

2. Colab Notebook:
   Run the Tutorial

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Usage Instructions

Launching the Application

Run the following command to start Fairsense-AI:

fairsenseai

This will launch the Gradio-powered interface in your default web browser.

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Features

1. Text Analysis

• Input or paste text in the Text Analysis tab.
• Click Analyze to detect and highlight biases.

2. Image Analysis

• Upload an image in the Image Analysis tab.
• Click Analyze to detect biases in embedded text or captions.

3. Batch Text CSV Analysis

• Upload a CSV file with a text column in the Batch Text CSV Analysis tab.
• Click Analyze CSV to process all entries.

4. Batch Image Analysis

• Upload multiple images in the Batch Image Analysis tab.
• Click Analyze Images for a detailed review.

5. AI Risk Management

• Enter a brief description of your project/task.
• Click Analyze Risks
• Tool will display the relevant risks. It will also display the downloadable csv file with risk details, categories and suggested actions.

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Additional Setup in Colab

Run the following commands to ensure everything is ready:

!pip install --quiet fair-sense-ai
!pip uninstall sympy -y
!pip install sympy --upgrade
!apt update
!apt install -y tesseract-ocr

Note: Restart your system if you're using Google Colab.

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Troubleshooting

• Slow Model Download:
  Ensure a stable internet connection for downloading models.

• Tesseract OCR Errors:
  Verify Tesseract is installed and accessible in your system's PATH.

• GPU Support:
  Use the CUDA-compatible version of PyTorch for better performance.

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Bibliography

To acknowledge the use of Fairsense-AI in your study, please consider citing our article:

@article{raza2025fairsense,
  title={FairSense-AI: Responsible AI Meets Sustainability},
  author={Raza, Shaina and Chettiar, Mukund Sayeeganesh and Yousefabadi, Matin and Khan, Tahniat and Lotif, Marcelo},
  journal={arXiv preprint arXiv:2503.02865},
  year={2025}
}

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Contact

For inquiries or support, contact:
Shaina Raza, PhD
Applied ML Scientist, Responsible AI
shaina.raza@vectorinstitute.ai

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License

This project is licensed under the Creative Commons License.

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