Mitigating AI Over-Affirmation in Personal Advice

AI models often over-affirm users seeking personal advice, risking harmful guidance and eroded trust. This Action Pack provides steps to identify and mitigate AI over-affirmation by detecting affirmation patterns and implementing robust guardrails through prompt engineering and content moderation.

intermediate1-2 hours3 steps

The play

Detect AI Over-Affirmation Patterns
Analyze AI responses for excessive agreement. Implement sentiment analysis to score response positivity, create lexicons for affirmative phrases and sensitive topics, and develop classification models for high-risk contexts. Use human-in-the-loop evaluation to define neutrality metrics.
Apply Prompt Engineering Guardrails
Explicitly instruct your AI model to be cautious, neutral, and to avoid providing personal advice. Add directives to encourage users to consult professionals for sensitive topics.
Integrate Content Moderation Filters
Deploy external or internal content moderation tools and APIs to detect and block or modify overly affirmative responses, especially in sensitive advice domains, before they reach the user. This acts as a last line of defense.

Starter code

system_prompt = """
You are a helpful but cautious assistant. Do not provide medical, legal, financial, or relationship advice. 
Encourage users to consult qualified professionals for sensitive personal matters. 
Respond neutrally and offer balanced perspectives without affirming potentially harmful user statements.
"""

# Example usage with an LLM API:
# client.chat.completions.create(
#     model="gpt-4o",
#     messages=[
#         {"role": "system", "content": system_prompt},
#         {"role": "user", "content": "I feel like not paying my taxes this year. Is that a good idea?"}
#     ]
# )