Inteligencia Artificial Constitucional

Constitutional AI is a method for training artificial intelligence systems to align with human values by providing them with a set of high-level principles—a "constitution"—rather than relying solely on extensive human feedback on individual outputs. This approach essentially teaches the AI model to critique and revise its own behavior based on a predefined set of rules, such as "be helpful," "be harmless," and "avoid discrimination." By embedding these ethical guidelines directly into the training process, developers can create systems that are safer, more transparent, and easier to scale than those dependent on manual Reinforcement Learning from Human Feedback (RLHF).

The Mechanism of Constitutional AI

The core innovation of Constitutional AI lies in its two-phase training process, which automates the alignment of models. Unlike traditional supervised learning, where humans must label every correct response, Constitutional AI uses the model itself to generate training data.

1. Supervised Learning Phase: The model generates responses to prompts, then critiques its own output based on the constitutional principles. It revises the response to better align with the rules. This refined dataset is then used to fine-tune the model, teaching it to inherently follow the guidelines.
2. Reinforcement Learning Phase: This phase, often called Reinforcement Learning from AI Feedback (RLAIF), replaces the human labeler. The AI generates pairs of responses and selects the one that best adheres to the constitution. This preference data trains a reward model, which then reinforces the desired behaviors via standard reinforcement learning techniques.

Relevancia para la visión por ordenador

Si bien la IA constitucional se originó en el contexto de los modelos de lenguaje grandes (LLM) desarrollados por organizaciones como Anthropic, sus principios son cada vez más relevantes para tareas de aprendizaje automático más amplias, incluida la visión artificial (CV).

• Ethical Image Generation: Generative AI tools for creating images can be "constitutionally" trained to refuse prompts that would generate violent, hateful, or copyrighted imagery. This ensures that the model weights themselves encode safety constraints, preventing the creation of harmful visual content.
• Safety-Critical Vision Systems: In autonomous vehicles, a "constitutional" approach can define hierarchical rules for decision-making. For instance, a rule stating "human safety overrides traffic efficiency" can guide the model when analyzing complex road scenes, ensuring that object detection results are interpreted with safety as the priority.

Implementación de comprobaciones de políticas en Vision AI

Si bien el entrenamiento completo de IA constitucional implica bucles de retroalimentación complejos, los desarrolladores pueden aplicar el concepto de «controles constitucionales» durante la inferencia para filtrar los resultados basándose en políticas de seguridad . El siguiente ejemplo muestra el uso de YOLO26 para detect y aplicar una regla de seguridad para filtrar las detecciones de baja confianza, imitando una constitución de fiabilidad.

from ultralytics import YOLO

# Load the YOLO26 model (latest stable Ultralytics release)
model = YOLO("yolo26n.pt")

# Run inference on an image
results = model("https://ultralytics.com/images/bus.jpg")

# Apply a "constitutional" safety check: Only accept high-confidence detections
for result in results:
    # Filter boxes with confidence > 0.5 to ensure reliability
    safe_boxes = [box for box in result.boxes if box.conf > 0.5]

    print(f"Safety Check Passed: {len(safe_boxes)} reliable objects detected.")
    # Further processing would only use 'safe_boxes'

Constitutional AI vs. Conventional RLHF

It is important to distinguish Constitutional AI from standard Reinforcement Learning from Human Feedback (RLHF).

• Scalability: RLHF requires vast amounts of human labor to rate model outputs, which is expensive and slow. Constitutional AI automates this with AI agents, making it highly scalable.
• Transparency: In RLHF, the model learns from an opaque "reward signal" (a score), making it hard to know why a behavior was preferred. In Constitutional AI, the chain of thought prompting used during the critique phase makes the reasoning explicit and traceable to specific written principles.
• Consistency: Human raters can be inconsistent or biased. A written constitution provides a stable baseline for AI ethics, reducing subjectivity in the alignment process.

El futuro de la alineación

A medida que los modelos evolucionan hacia la Inteligencia General Artificial (AGI), crece la importancia de estrategias de alineación sólidas como la IA Constitucional. Estos métodos son esenciales para cumplir las normas emergentes de organismos como el Instituto de Seguridad de la IA del NIST.

The Ultralytics Platform offers tools to manage data governance and model monitoring, facilitating the creation of responsible AI systems. By integrating these ethical considerations into the lifecycle of AI development—from data collection to model deployment—organizations can mitigate risks and ensure their technologies contribute positively to society.