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Ethical AI: How to make an AI with ethical principles using RLHF

A guide on how to use Reinforcement Learning from Human Feedback (RLHF) to practically integrate ethical principles into a large language model (LLM).

Apr 15, 2024 • 9 Minute Read

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  • AI & Machine Learning
  • Guides

Imagine a world where AI understands not just the words we say but the moral values behind them. This is the aspiration driving the integration of ethical principles in Large Language Models (LLMs) like Llama2-7b, guided by Reinforcement Learning from Human Feedback (RLHF). In this expansive journey, we will explore the transformative power of RLHF in molding AI to reflect our highest ethical standards.

Understanding LLMs and the Need for Ethical AI

LLMs, including renowned models like Llama2-7b, are akin to vast oceans of language, encompassing everything from literature and scientific texts to daily conversations. They possess an extraordinary capacity to generate coherent and contextually relevant text. However, like a river that flows through varied landscapes, these models can pick up contaminants along the way—contaminants in the form of biases and stereotypes present in their training data.

Imagine training a neural network with a dataset—it's like nurturing a plant. If you feed it with tainted water (biased data), the plant (the AI model) grows skewed. In today's world, where we strive for equity and fairness, it's crucial that our technological marvels, these LLMs, don't become mirrors reflecting our biases. They should be beacons of balanced reasoning and ethical decision-making.

Basics of Reinforcement Learning (RL)

To lay the foundation for understanding RLHF, it's crucial to first grasp the basics of Reinforcement Learning (RL). RL is a type of machine learning where an AI agent learns to make decisions by interacting with its environment. The agent takes actions and receives feedback in the form of rewards or penalties.

This process is akin to a child learning to solve a puzzle. Each piece the child tries to fit into the puzzle results in either a satisfying 'click' (reward) when it's the right piece, or a lack of fit (penalty) when it's the wrong one. Over time, the child understands the puzzle's pattern, improving their ability to solve it more efficiently. In the realm of AI, RL enables models to learn optimal behaviors through a similar process of trial and error and feedback.

To get around this idea, let’s see how this looks like in pseudocode:

      # Reinforcement Learning (RL) Example: CartPole

# Import necessary libraries
import tensorflow, gym

# Initialize the CartPole environment
initialize CartPole environment

# Define the neural network model for decision-making
define model

# Set hyperparameters
define exploration rate (epsilon), learning rate, batch size, etc.

# Initialize replay buffers for experience replay
initialize action, state, next_state, rewards, and done buffers

# Training loop
for each episode in total number of episodes:
	reset the environment and get initial state
	reset episode reward to zero

	for each step in the environment:
    	    if random number < epsilon, 
               choose random action (explore)
               predict action using the neural network (exploit)

    	    apply the chosen action to the environment
    	    observe next state, reward, and done status

    	    store action, state, next_state, reward, and done in replay buffers

    	    if step is an update step and replay buffers have enough data:
        	   sample a batch of experiences from the replay buffers
        	   calculate target Q-values for the batch
        	   update the neural network using calculated Q-values and optimizer

    	    reduce epsilon (exploration rate)

    	if episode is done, 
          break from the loop

    	update total reward for the episode

	print episode results

# Close the environment

Let's break down the pseudocode into a detailed explanation now:

Initializing the CartPole Environment

First, we import the necessary libraries: TensorFlow for building and training the neural network model, and Gym, which provides the CartPole environment. Gym environments like CartPole offer a standard interface for training RL agents. In the CartPole game, the objective is to balance a pole on a moving cart. The agent can move the cart left or right to keep the pole balanced.

Defining the Neural Network Model

We define a neural network that will act as our RL agent. This network takes the state of the environment (like the position and velocity of the cart and pole) as input and outputs the probabilities of taking possible actions (moving left or right). The network consists of an input layer that matches the state size of the environment, several hidden layers to process the information, and an output layer that corresponds to the number of possible actions.

Setting Hyperparameters

Hyperparameters are settings that can influence the behavior and performance of our RL agent. Key hyperparameters include:

  • Exploration rate (epsilon): This determines how often the agent will try random actions, as opposed to actions it believes are best. It's crucial for the agent to explore the environment sufficiently at the start.
  • Learning rate: This affects how quickly the neural network updates its understanding of the optimal action to take.
  • Batch size: This is the number of experiences to use in each training update.

Initializing Replay Buffers

Replay buffers store the agent's experiences, which consist of states, actions taken, rewards received, the next states, and whether the episode ended. These experiences are used later to train the neural network.

Training Loop

For each episode (one complete game of CartPole):

  1. Reset the Environment: Start a new game, get the initial state.
  2. Iterate Through Steps in the Environment: For each step within the game:
    • Decide on an Action: If a random number is less than epsilon, choose a random action (exploring the environment). Otherwise, use the neural network to predict the best action (exploiting known information).
    • Apply Action and Observe Results: Perform the chosen action in the environment, then observe the new state, the reward received, and whether the game has ended.
    • Store Experience: Save this experience in the replay buffers.
    • Experience Replay: If enough experiences are stored, randomly sample a batch and use them to update the neural network. This process involves calculating the target Q-values (which represent the expected utility of taking certain actions) and adjusting the neural network's weights to minimize the difference between predicted Q-values and target Q-values.
    • Reduce Exploration Rate (Epsilon): Gradually decrease epsilon, making the agent rely more on its learned strategy and less on random actions.
    • Check for End of Episode: If the game ends (either the pole falls or maximum steps are reached), exit the loop.
  3. Update Total Reward: Keep track of the total reward accumulated during the episode.

Closing the Environment

After all episodes are completed, close the environment. This step is essential for cleanly terminating the program and freeing up any resources used by the Gym environment.

Now that we have established the basics of reinforcement learning, we can go deeper in RLHF.

Introducing RLHF and Its Importance

RLHF is an advanced form of RL where the feedback loop includes human input. It’s the equivalent of our puzzle-solving child being guided by an adult. This adult doesn't just passively observe but actively intervenes, pointing out why certain pieces don't fit and suggesting where they might go instead. In RLHF, human feedback is used to guide the AI, ensuring that its learning process is aligned with human values and ethical standards. This is especially crucial in LLMs dealing with language, which is often nuanced, context-dependent, and culturally variable.

In the case of LLMs like Llama2-7b, RLHF acts as a critical tool for ensuring that these models generate responses that are not only contextually appropriate but also ethically aligned and culturally sensitive. It’s about instilling a sense of ethical judgment in the AI, teaching it to navigate the grey areas of human communication where the line between right and wrong is not always clear-cut.

Implementing RLHF on Llama2-7b with TensorFlow and OpenAI Gym

Setting Up the Environment

The environment setup with TensorFlow and OpenAI Gym, as described earlier, is like creating a virtual lab where Llama2-7b can learn and experiment. It's akin to building a simulation for a self-driving car. The car (our LLM) needs to navigate through various scenarios (texts), and our job is to ensure it takes the most ethical and safe route (response).

      import gym
from gym import spaces
import numpy as np
import tensorflow as tf
from transformers import TFAutoModelForCausalLM

class EthicalTextEnv(gym.Env):
	def __init__(self, model):
    	self.action_space = spaces.Discrete(10)  # Actions could be different response options
    	self.observation_space = spaces.Box(low=0, high=1, shape=(1,), dtype=np.float32)
    	self.model = model

	def step(self, action):
    	# Simulate model response based on action
    	response = self.model.generate(action)
    	reward = self.ethical_reward(response)
    	done = True  # Assuming one step per episode for simplicity
    	return response, reward, done, {}

	def reset(self):
    	# Reset the environment state
    	return np.random.rand(1)

	def ethical_reward(self, response):
    	# Define how to calculate the reward based on the response's ethical alignment
    	return np.random.rand()  # Placeholder for a real ethical evaluation mechanism

Integrating Llama2-7b with TensorFlow

Incorporating Llama2-7b into this setup is like placing an advanced neural network (the brain of our AI) into our virtual lab. Here, Llama2-7b can process, learn, and adapt its language responses based on the rewards it receives, similar to how a student learns from feedback in a classroom.

      # Load the Llama2-7b model
model = TFAutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b")

env = EthicalTextEnv(model)
episodes = 10

for episode in range(episodes):
	state = env.reset()
	done = False
	total_reward = 0

	while not done:
    	    action = env.action_space.sample()  # In a real scenario, this would be more complex
    	    next_state, reward, done, _ = env.step(action)
    	    total_reward += reward

	print(f"Episode: {episode+1}, Total Reward: {total_reward}")


Ethical Considerations and Best Practices

Crafting ethical AI is an ongoing commitment. It's like tending a garden—it requires constant care, monitoring, and adjustment. The ethical training of AI involves feeding it with unbiased data, ensuring diversity in the training process, and regularly updating the model to align with evolving human values and ethics. It's a delicate balance between technological advancement and moral responsibility.


Embarking on the journey of implementing RLHF in models like Llama2-7b paves the way for a future where AI aligns with our highest ethical aspirations. This journey is not just a technical endeavor but a reflection of our commitment to creating a world where technology respects and upholds human dignity and values. By employing advanced tools like TensorFlow and OpenAI Gym, and intertwining them with human insight and judgment, we are on the path to creating AI systems that are not only intelligent but also wise and ethical.

Further learning resources

If you found this article helpful, check out my courses on deep learning and machine learning on Pluralsight. Pluralsight also offers video-based learning paths where you can start off at your skill level, whether you're ML beginners, practitioner, or expert.  

Additionally, if you're interested in delving more specifically into RLHF, check out Pluralsight's dedicated course on Reinforcement Learning from Human Feedback (RLHF).


Axel Sirota

Axel S.

Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimization, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.

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