Recognizing the Issue: The goal is to develop a chatbot that can respond to commonly asked queries.

1. Data Gathering: Assemble a dataset including queries and responses. This might be hand-picked or obtained from customer support records.
2. Data preprocessing: Eliminate stop words, tokenize, and stem the text data. Divide the data into sets for testing and training.
3. Selecting the Algorithm: To generate replies, use a sequence-to-sequence (Seq2Seq) model.
4. Model Training: Use TensorFlow to put the Seq2Seq model into practice. Utilizing the training dataset, adjust the hyperparameters and train the model.
5. Model Testing and Validation: Test the model’s performance on the test set and validate it using a validation set. Make adjustments
6. Model Deployment: Use a web framework such as Flask to deploy the chatbot. Create an API endpoint for users to interact with the chatbot.
7. Ethical Considerations: Ensure the chatbot does not give biased or inappropriate responses. Implement mechanisms to handle sensitive data securely.

In summary, building artificial intelligence systems that are not only efficient but also morally and legally compliant can be achieved by following this guide’s steps. Whether you’re building a basic chatbot or a sophisticated predictive model, the principles are always the same: gather high-quality data, select appropriate algorithms, train and validate your models thoroughly, and deploy them with caution and supervision.