Algorethics AI Library - Developer Documentation

Overview

The Algorethics AI Library offers tools for validating AI models and datasets for ethical compliance across multiple dimensions, including inclusivity, fairness, privacy, and more. The library integrates seamlessly with machine learning frameworks like TensorFlow, PyTorch, and Scikit-Learn, providing both a command-line interface and programmatic APIs for validation and certification.

Ethical Policies: Implements ethical checks for inclusivity, fairness, privacy, responsibility, and transparency.

Advanced Data Validation: Supports text and image data validation, with upcoming features like bias detection and real-time monitoring.

Certification API: Certifies AI projects that pass all ethical validations.

Integration: Compatible with TensorFlow, PyTorch, and Scikit-Learn.

Command-Line Interface: Simple CLI for performing validations.

Logging: Keeps detailed logs for auditing and debugging purposes.

The core and policies directories contain the following files:

Core Directory

				
					__init__.py: Initializes the core module.
ethical_policy.py: Likely contains the base classes or logic for defining ethical policies.
validator.py: The main logic for validating projects or data against ethical policies.
policy_validation.py: Possibly handles overall validation based on different ethical principles.

Policies Directory

				
					Impartiality_Policy.py: Enforces policies related to impartiality.
inclusion_policy.py: Ensures inclusion and fairness in AI models.
privacy_policy.py: Implements privacy standards.
Reliability_Policy.py: Enforces reliability in AI models.
responsibility_policy.py: Focuses on accountability and responsibility.
transparency_policy.py: Promotes transparency in AI processes.

Features

  • Ethical Policies: Implements ethical checks for inclusivity, fairness, privacy, responsibility, and transparency.
  • Advanced Data Validation: Supports text and image data validation, with upcoming features like bias detection and real-time monitoring.
  • Certification API: Certifies AI projects that pass all ethical validations.
  • Integration: Compatible with TensorFlow, PyTorch, and Scikit-Learn.
  • Command-Line Interface: Simple CLI for performing validations.
  • Logging: Keeps detailed logs for auditing and debugging purposes.

Installation

Clone the Repository 

				
					git clone https://github.com/smartkuttan/Algorethics.git
cd Algorethics

1.  Install Dependencies 

				
					pip install -r requirements.txt

Usage

1. Command-Line Interface (CLI)

				
					python -m algorethics.interfaces.command_interface --validate-text --input data/sample_text_data.csv

Validate an Image File: 

				
					python -m algorethics.interfaces.command_interface --validate-image --input data/sample_image_data.png

2. Using the Core Validator in Python

You can also use the core validator programmatically within your Python code

Text Validation Example

				
					from algorethics.core.validator import Validator
from algorethics.policies.inclusion_policy import InclusionPolicy

# Initialize validator with policies
validator = Validator(policies=[InclusionPolicy()])

# Validate a sample text
result = validator.validate_text("This is a sample text to validate.")
print(result)

Certification API Integration

The Certification API can be called after a project passes all ethical validations, such as human dignity, inclusion, privacy, and transparency.

				
					 
import requests
from policy_validation import EthicsPolicyValidator

# Initialize the Ethics Validator
validator = EthicsPolicyValidator()

# Validate the project
is_compliant = validator.validate_all("content", "data", "model_output", "process_log", "impact_assessment")

if is_compliant:
    response = requests.post('https://algorethics.info/algorethics_cert_gen.php', data={
        'project_name': 'Your Project',
        'project_url': 'http://yourproject.com',
        'developer_email': 'developer@yourproject.com'
    })
    print(response.json())
else:
    print("Project is not ethically compliant.")

Modules and Structure

1. Core Module

				
					policy_validation.py: Handles validation based on the six ethical principles.
ethical_policy.py: Base classes for creating ethical policies.
validator.py: Main validation logic.

2. Policies Module

				
					inclusion_policy.py: Ensures inclusivity in AI models.
privacy_policy.py: Enforces privacy standards.
transparency_policy.py: Promotes transparency and fairness.
Impartiality_Policy.py: Ensures impartiality.
Reliability_Policy.py: Focuses on model reliability.
responsibility_policy.py: Enforces responsibility and accountability.

3. Data Module

				
					Handles data validation for text and image files for ethical compliance.

4. Interfaces Module

				
					and_interface.py: Provides a command-line interface for validation.

Advanced Features

1. Advanced Data Validation

We’ve enhanced validation with sophisticated techniques for text and image data:

				
					from algorethics.data.advanced_validator import AdvancedValidator

# Initialize the advanced validator
validator = AdvancedValidator()

# Validate a text and image file
text_validation_result = validator.validate_text("Sample text for advanced validation.")
image_validation_result = validator.validate_image("/path/to/image.png")

print(text_validation_result, image_validation_result)

2. Real-time Monitoring (Upcoming)

We’re developing a real-time monitoring feature to ensure continuous ethical compliance.

3. Bias Detection (Upcoming)

Bias detection policies will soon be available to help detect biases in AI datasets.

Machine Learning Library Integration

TensorFlow Example

				
					 
import tensorflow as tf
from algorethics import TensorFlowEthicsValidator

# Load a TensorFlow model
model = tf.keras.models.load_model('/path/to/your/model')

# Initialize the TensorFlow ethics validator
ethics_validator = TensorFlowEthicsValidator(model)

# Validate the model for ethical compliance
ethics_validator.validate()

PyTorch Example

				
					import torch
from algorethics import PyTorchEthicsValidator

# Load a PyTorch model
model = torch.load('/path/to/your/model')

# Initialize the PyTorch ethics validator
ethics_validator = PyTorchEthicsValidator(model)

# Validate the model for ethical compliance
ethics_validator.validate()

Scikit-Learn Example

				
					from sklearn.ensemble import RandomForestClassifier
from algorethics import ScikitLearnEthicsValidator

# Load a Scikit-Learn model
model = RandomForestClassifier()
model.load('/path/to/your/model')

# Initialize the Scikit-Learn ethics validator
ethics_validator = ScikitLearnEthicsValidator(model)

# Validate the model for ethical compliance
ethics_validator.validate()

Future Enhancements

The Algorethics AI Library is constantly evolving. Future plans include:

  • Broader Policy Support: Covering bias detection, sustainability, and more.
  • Integration with AI Ethics Frameworks: Building plugins for major frameworks like the Rome Call for AI Ethics.
  • Enhanced Reporting and Analytics: Advanced dashboards and reports for different needs.

Contributing

We welcome contributions from the community! To contribute:

				
					Fork the repository.
Create a new branch: git checkout -b feature-branch.
Make your changes.
Commit the changes: git commit -m 'Add new feature'.
Push to the branch: git push origin feature-branch.