A company trains a generative AI model designed to classify customer feedback as positive, negative, or neutral. However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don’t reflect current customer communication styles. When the model is deployed, it shows a strong bias in its sentiment analysis for new customer feedback, misclassifying reviews from underrepresented demographics and struggling to understand current slang or phrasing. What type of model limitation is this?

A company wants to build a model to classify customer reviews as positive, negative, or neutral. They have collected a dataset of thousands of customer reviews, and each review has been manually tagged with the corresponding sentiment: positive, negative, or neutral. What machine learning should the company use?

An order fulfillment team has an agent that automatically processes orders, updates inventory, sends shipping notifications, and handles returns. What type of agent is this?

A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information. They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?

A company collects customer feedback through open-ended survey questions where customers can write detailed responses in their own words, such as “The product was easy to use, and the customer support was excellent, but the delivery took longer than expected.” What type of data is this?