Конституционный искусственный интеллект

Как работает конституционный искусственный интеллект

The core idea behind Constitutional AI involves a two-phase training process:

1. Supervised Learning Phase: Initially, a standard pre-trained language model is prompted with scenarios designed to elicit potentially harmful or undesirable responses. The model generates several responses. These responses are then critiqued by another AI model based on the principles outlined in the constitution. The AI critiques its own responses, identifying why a response might violate a principle (e.g., being non-consensual or harmful). The model is then fine-tuned on these self-critiqued responses, learning to generate outputs that align better with the constitution. This phase uses supervised learning techniques.
2. Reinforcement Learning Phase: Following the supervised phase, the model is further refined using Reinforcement Learning (RL). In this stage, the AI generates responses, and an AI model (trained using the constitution) evaluates these responses, providing a reward signal based on how well they adhere to the constitutional principles. This process, often referred to as Reinforcement Learning from AI Feedback (RLAIF), optimizes the model to consistently produce outputs aligned with the constitution, essentially teaching the AI to prefer constitutionally-aligned behavior.

This self-correction mechanism, guided by explicit principles, distinguishes Constitutional AI from methods like Reinforcement Learning from Human Feedback (RLHF), which heavily relies on human labelers rating model outputs.

Ключевые понятия

• The Constitution: This is not a literal legal document but a set of explicit ethical principles or rules guiding the AI's behavior. These principles can be derived from various sources, such as universal declarations (like the UN Declaration of Human Rights), terms of service, or custom ethical guidelines tailored to specific applications. The effectiveness relies heavily on the quality and comprehensiveness of these principles.
• AI Self-Critique and Revision: A fundamental aspect where the AI model learns to evaluate its own outputs against the constitution and generate revisions. This internal feedback loop reduces the need for constant human intervention.
• AI Alignment: Constitutional AI is a technique contributing to the broader field of AI alignment, which seeks to ensure that AI systems' goals and behaviors align with human intentions and values. It addresses concerns about AI safety and the potential for unintended consequences.
• Scalability: By automating the feedback process using AI based on the constitution, this method aims to be more scalable than RLHF, which can be labor-intensive and potentially introduce human biases (algorithmic bias).

Примеры из реальной жизни

1. Anthropic's Claude Models: The most prominent example is Anthropic's family of Claude LLMs. Anthropic developed Constitutional AI specifically to train these models to be "helpful, harmless, and honest." The constitution used includes principles discouraging toxic, discriminatory, or illegal content generation, based partly on the UN Declaration of Human Rights and other ethical sources. Read more in their paper on Collective Constitutional AI.
2. AI Content Moderation Systems: Constitutional AI principles could be applied to train models for content moderation platforms. Instead of relying solely on human moderators or rigid keyword filters, an AI could use a constitution defining harmful content (e.g., hate speech, misinformation) to evaluate user-generated text or images, leading to more nuanced and consistent moderation aligned with platform policies and AI ethics guidelines.

Constitutional AI vs. Related Terms

• Reinforcement Learning from Human Feedback (RLHF): While both aim to align AI, RLHF uses feedback generated by humans rating model outputs. Constitutional AI primarily uses AI-generated feedback based on a predefined constitution, making it potentially more scalable and consistent, though the quality depends heavily on the constitution itself.
• AI Ethics & Responsible AI: AI Ethics is the broad field studying the moral implications of AI. Responsible AI encompasses principles and practices (like fairness, transparency (XAI), accountability, data privacy) for developing and deploying AI systems safely and ethically. Constitutional AI is a specific technical method used during model training to implement certain ethical principles and contribute to responsible AI development.

Applications and Future Potential

Currently, Constitutional AI is primarily applied to LLMs for tasks like dialogue generation and text summarization. However, the underlying principles could potentially extend to other AI domains, including Computer Vision (CV). For instance:

• Guiding image generation models (like Stable Diffusion or DALL-E) to avoid creating harmful, biased, or non-consensual imagery based on constitutional rules.
• Informing decision-making in autonomous vehicles or robotics, ensuring actions align with safety protocols defined in a constitution.
• Ensuring fairness in CV tasks like facial recognition or object detection by incorporating principles against demographic bias, potentially improving models like Ultralytics YOLO11.

The development and refinement of effective constitutions, along with ensuring the AI faithfully adheres to them across diverse contexts, remain active areas of research within organizations like Google AI and the AI Safety Institute. Tools like Ultralytics HUB facilitate the training and deployment of various AI models, and incorporating principles akin to Constitutional AI could become increasingly important for ensuring responsible deployment.