When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews

1. 1Identify potential biases in interviewer prompts and questions
2. 2Analyze datasets for systematic biases
3. 3Develop strategies to mitigate interviewer effects and improve model generalizability
4. 4Evaluate model performance on diverse datasets to ensure robustness

Data scientists and AI engineers working on natural language processing and clinical interview analysis can benefit from understanding these biases to improve model interpretability and accuracy

💡 Systematic biases from interviewer prompts can impact model predictions and interpretability

🚨 Interviewer effects can bias depression detection models! 🤖