Algorethics - Usage Instructions

How to Use Algorethics

Algorethics provides a suite of tools to ensure ethical compliance in AI projects. This page includes instructions and examples on how to use various components of the library, including text validation, image validation, and certification processes.

Text Validation

The text validation component ensures that AI-generated text is free from bias and meets ethical standards. Below is a Python example demonstrating how to use the text validation feature:

Python Example


# Import the necessary modules
from algorethics import TextValidator

# Initialize the TextValidator
text_validator = TextValidator()

# Validate a text string
result = text_validator.validate("Example text to validate.")

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

Image Validation

The image validation component allows you to ensure that images comply with ethical standards, including privacy and content appropriateness. Here is a Python example for image validation:

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())

Custom Certification

To request certification for your AI projects, you need to submit a certification request. Below is a Python example showing how to submit a certification request:

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 Use Cases

Here are some advanced use cases for integrating the Algorethics library with other systems or custom configurations.

Custom Configuration for Text Validation


# 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 for Image Validation


# 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())