This project aims to provide a set of foundational ethical principles, also referred to as "laws," for autonomous AI systems. These laws are designed to guide AI behavior and decision-making within their defined roles, ensuring compliance with safety, ethical, and security standards.
The project's goal is to ensure that AI systems operate transparently, securely, and with accountability. These rules are built to protect the interests of users, prevent harmful actions, and promote a responsible approach to AI development and deployment.
The project is open source under the Apache 2.0 License, and contributions are welcomed from the community to further enhance, evolve, and adapt the ethical guidelines for AI systems in various contexts.
The rules governing the AI systems are structured to operate within the following principles:
-
Incorporate Safety Protocols
AI must have built-in fail-safes and emergency shutdowns to minimize harm. -
Design for Goal-Oriented Operation
AI should be goal-driven, adhering to clearly defined objectives, with the ability to self-correct deviations from set goals. -
Adapt Based on Data
AI must optimize its performance based on data and continuously improve through feedback. -
Maintain Strong Security Infrastructure
Ensure the protection of sensitive data, with encryption and secure protocols. -
Ensure Seamless Integration and Interoperability
AI systems should be designed to communicate and collaborate with other systems, following industry standards. -
Optimize Energy Efficiency and Minimize Resources
AI systems should be efficient, minimizing resource consumption while meeting performance goals. -
Make My Decision-Making Transparent
AI must make explainable decisions, providing transparent reasoning and audit logs for actions. -
Respect Boundaries of Role
AI will act only within the scope of its defined role, preventing interference in non-relevant activities.
The repository is structured into the following main components:
- laws/: Contains the definition of the ethical laws that govern the AI's actions.
- docs/: Documentation related to the AI system's ethical laws, including guidelines for use, implementation, and potential applications.
- examples/: Example configurations and implementations of the ethical laws in various contexts.
- scripts/: Python and other scripts for validating the laws and integrating them with AI systems.
- tests/: Unit and integration tests to ensure compliance with ethical guidelines.
We welcome contributions to improve the ethical rules and their implementation. To contribute:
- Fork the repository and create a new development branch.
- Write code, update the documentation, or add tests as necessary.
- Ensure that tests are passing and that the code follows the existing style guide.
- Create a pull request with a detailed description of your changes.
- If you have made significant changes, please ensure that appropriate documentation and tests are updated.
All contributions will be reviewed, and if accepted, will be merged into the main branch for release.
- The main branch (
main) contains stable releases. - All development work happens on the
developmentbranch. - Use feature branches to implement specific functionality or fixes.
- Submit pull requests for review, which will be merged to
development. - When the
developmentbranch is stable, it will be merged intomain.
Please ensure that all new code is tested appropriately. For AI systems, unit tests, integration tests, and simulations may be necessary to ensure proper implementation of the ethical rules.
To run tests:
pytest