This task focuses on the formal statistical evaluation of AI and machine learning methods used to analyze open-ended survey responses or qualitative text data. It is intended for teams using NLP or large language models (LLMs) who need evidence that these tools are effective, efficient, and reliable.
Rather than simply generating themes or topics, I help quantify whether AI-assisted approaches outperform or meaningfully support traditional methods.
What This Task Includes
Text & NLP Analysis

• Cleaning and preprocessing of open-ended survey or text data
• Theme, topic, or cluster extraction using NLP or LLM-based approaches
• Comparison to human-coded or baseline methods (if available)

Statistical Validation

• Agreement and similarity assessment between AI and human outputs
• Hypothesis testing to evaluate accuracy, stability, and bias
• Measurement of efficiency gains (time, cost, reviewer burden)
• Resampling or simulation-based validation where appropriate

Study Design & Evaluation

• Design of evaluation frameworks for AI-assisted workflows
• Definition of performance metrics aligned with project goals

Reporting & Deliverables

• Clear statistical summary of findings
• Visual comparisons of AI vs traditional approaches
• Reproducible code and transparent methodology

Client Should Provide

• Text or survey data
• Description of current or proposed AI workflow
• Human-coded data or benchmarks (if available)

Best Suited For

• Open-ended survey analysis
• Qualitative research at scale
• AI governance and responsible AI initiatives
• Evaluation of LLM-assisted analytics workflows

Please contact me before starting this task.
Scope, pricing, and timelines may vary depending on the size of the text data, availability of benchmarks, and the evaluation framework required.