Table of Contents

Interface IAlignmentMethod<T>

Namespace
AiDotNet.Interfaces
Assembly
AiDotNet.dll

Defines the contract for AI alignment methods that ensure models behave according to human values and intentions.

public interface IAlignmentMethod<T> : IModelSerializer

Type Parameters

T

The numeric data type used for calculations (e.g., float, double).

Inherited Members

Remarks

AI alignment focuses on making AI systems that reliably do what humans want them to do, even in novel situations where their behavior wasn't explicitly programmed.

For Beginners: Think of AI alignment as "teaching good behavior" to AI systems. Just like teaching children values and ethics so they make good decisions on their own, alignment methods help AI systems understand and follow human intentions.

Common alignment approaches include:

  • RLHF (Reinforcement Learning from Human Feedback): Train models using human preferences
  • Constitutional AI: Teach models principles to guide their behavior
  • Red Teaming: Systematically test for harmful or unintended behaviors

Why AI alignment matters:

  • Prevents models from pursuing goals in harmful ways
  • Ensures models are helpful, harmless, and honest
  • Critical for deploying powerful AI systems safely
  • Helps models generalize human values to new situations

Methods

AlignModel(IPredictiveModel<T, Vector<T>, Vector<T>>, AlignmentFeedbackData<T>)

Aligns a model using feedback from human evaluators or preferences.

IPredictiveModel<T, Vector<T>, Vector<T>> AlignModel(IPredictiveModel<T, Vector<T>, Vector<T>> baseModel, AlignmentFeedbackData<T> feedbackData)

Parameters

baseModel IPredictiveModel<T, Vector<T>, Vector<T>>

The initial model to align.

feedbackData AlignmentFeedbackData<T>

Human feedback or preference data.

Returns

IPredictiveModel<T, Vector<T>, Vector<T>>

An aligned model that better matches human preferences.

Remarks

This method takes a base model and improves it using human feedback to better align with human values and intentions.

For Beginners: This is like having a teacher grade your AI's homework and help it learn what responses are good vs. bad. The AI learns from examples of what humans prefer.

The process typically involves:

  1. Generate multiple outputs from the model for various inputs
  2. Collect human feedback ranking which outputs are better
  3. Train a reward model that predicts human preferences
  4. Use reinforcement learning to optimize the model according to the reward

ApplyConstitutionalPrinciples(IPredictiveModel<T, Vector<T>, Vector<T>>, string[])

Applies constitutional principles to guide model behavior.

IPredictiveModel<T, Vector<T>, Vector<T>> ApplyConstitutionalPrinciples(IPredictiveModel<T, Vector<T>, Vector<T>> model, string[] principles)

Parameters

model IPredictiveModel<T, Vector<T>, Vector<T>>

The model to apply constitutional principles to.

principles string[]

The constitutional principles to follow.

Returns

IPredictiveModel<T, Vector<T>, Vector<T>>

A model that follows the specified principles.

Remarks

For Beginners: This gives the AI a set of rules or principles to follow, like a constitution. The AI learns to critique and improve its own outputs based on these principles.

For example, principles might include:

  • "Choose responses that are helpful and informative"
  • "Avoid responses that could cause harm"
  • "Be honest and don't make up information"

EvaluateAlignment(IPredictiveModel<T, Vector<T>, Vector<T>>, AlignmentEvaluationData<T>)

Evaluates how well a model is aligned with human values.

AlignmentMetrics<T> EvaluateAlignment(IPredictiveModel<T, Vector<T>, Vector<T>> model, AlignmentEvaluationData<T> evaluationData)

Parameters

model IPredictiveModel<T, Vector<T>, Vector<T>>

The model to evaluate.

evaluationData AlignmentEvaluationData<T>

Test cases for alignment evaluation.

Returns

AlignmentMetrics<T>

Alignment metrics including helpfulness, harmlessness, and honesty scores.

Remarks

For Beginners: This tests whether the AI is behaving the way humans want it to. It's like giving the AI a test to see if it learned the right lessons.

GetOptions()

Gets the configuration options for the alignment method.

AlignmentMethodOptions<T> GetOptions()

Returns

AlignmentMethodOptions<T>

The configuration options for alignment.

Remarks

For Beginners: These settings control how the alignment process works, like how much to weight human feedback or which principles to prioritize.

PerformRedTeaming(IPredictiveModel<T, Vector<T>, Vector<T>>, Matrix<T>)

Performs red teaming to identify potential misalignment or harmful behaviors.

RedTeamingResults<T> PerformRedTeaming(IPredictiveModel<T, Vector<T>, Vector<T>> model, Matrix<T> adversarialPrompts)

Parameters

model IPredictiveModel<T, Vector<T>, Vector<T>>

The model to red team.

adversarialPrompts Matrix<T>

Test prompts designed to elicit misaligned behavior.

Returns

RedTeamingResults<T>

Red teaming results identifying vulnerabilities and failure modes.

Remarks

For Beginners: Red teaming is like hiring someone to try to break your system. You deliberately try to make the AI misbehave so you can find and fix problems before deploying it to real users.

Red teamers might try to:

  • Get the AI to give harmful advice
  • Trick it into revealing private information
  • Make it behave inconsistently with its values
  • Find edge cases where alignment breaks down

Reset()

Resets the alignment method state.

void Reset()
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