Algorethics - Library Documentation

Algorethics Library

Welcome to the Algorethics Library documentation. This section provides comprehensive information on how to utilize our library to ensure ethical compliance in AI projects. Below you will find detailed explanations of various components, their usage, and code examples in Python.

Overview

The Algorethics Library is designed to help developers integrate ethical standards into AI systems. It includes tools for validating text and image data against ethical guidelines and provides a mechanism for obtaining project certification.

Library Components

TextValidator: Validates text data to ensure it complies with ethical standards.
ImageValidator: Screens image data for privacy and content compliance.
Certification: Facilitates the certification process for AI projects to confirm adherence to ethical practices.

Installation

To install the Algorethics Library, clone the repository from GitHub and install the required dependencies.

Python Installation


# Clone the repository
git clone https://github.com/smartkuttan/Algorethics.git

# Navigate to the library directory
cd Algorethics

# Install required dependencies
pip install -r requirements.txt

TextValidator

The TextValidator class is used to ensure that the text data meets ethical standards, such as avoiding bias and ensuring respectfulness.

Python Example


# Import the necessary modules
from algorethics import TextValidator

# Initialize the TextValidator
text_validator = TextValidator()

# Validate a text string
text = "Sample text for validation"
result = text_validator.validate(text)

if result.is_valid():
    print('Text is valid.')
else:
    print('Text validation failed:', result.get_errors())

ImageValidator

The ImageValidator class is used to ensure that image data complies with ethical guidelines, including privacy and content standards.

Python Example


# Import the necessary modules
from algorethics import ImageValidator

# Initialize the ImageValidator
image_validator = ImageValidator()

# Validate an image file
result = image_validator.validate('/path/to/image.jpg')

if result.is_valid():
    print('Image is valid.')
else:
    print('Image validation failed:', result.get_errors())

Certification

The Certification class is used to request certification for an AI project, demonstrating that it adheres to ethical standards.

Python Example


# Import the necessary modules
from algorethics import Certification

# Initialize the Certification instance
certification = Certification()

# Submit a project for certification
response = certification.request_certification('Project Name', 'Project Description')

if response.is_successful():
    print('Certification request submitted successfully.')
else:
    print('Certification request failed:', response.get_errors())

Advanced Usage

For more advanced use cases, such as integrating the library with other systems or custom configuration, refer to the following examples:

Custom Configuration


# Import the necessary modules
from algorethics import TextValidator

# Initialize the TextValidator with custom configuration
config = {
    'max_length': 1000,
    'allowed_languages': ['en', 'es'],
    'exclude_terms': ['profanity']
}
text_validator = TextValidator(config=config)

# Validate a text string with custom settings
text = "Custom configured text validation"
result = text_validator.validate(text)

if result.is_valid():
    print('Text is valid according to custom configuration.')
else:
    print('Text validation failed:', result.get_errors())

Batch Processing


# Import the necessary modules
from algorethics import ImageValidator

# Initialize the ImageValidator
image_validator = ImageValidator()

# List of image paths
image_paths = ['/path/to/image1.jpg', '/path/to/image2.jpg']

# Validate multiple images
results = [image_validator.validate(path) for path in image_paths]

for path, result in zip(image_paths, results):
    if result.is_valid():
        print(f'Image {path} is valid.')
    else:
        print(f'Image {path} validation failed:', result.get_errors())