Objective and audience
This book explores AI psychometrics, which we define as the use of transformers, including Large Language Models, in psychological assessment. Applications of AI Psychometrics include job-competency mapping, skills inference from digital footprints, test design and scoring, translation, feedback generation, and prediction and clustering, all aimed at enhancing precision and impact in psychometrics and people analytics.
The book is intended for applied psychometricians, industrial psychologists, organizational consultants, and HRTech professionals interested in using transformers and Large Language Models to enhance psychological assessment and people analytics. It offers general insights relevant to business leaders alongside technical content for psychometric specialists.
Practical and applied emphasis
The book emphasises ‘learning by doing’ for practical understanding over theory. It discusses topics that will build knowledge for everyday AI data science practitioners. For example, it shows how to reconstruct operational LLMs from the ground up, how you can train a ‘small’ LLM from scratch, how to fine tune an LLM, and how to secure spot pricing from cloud computing vendors.
New transformer based developments in psychological measurement are also covered in detail, including semantic item and construct validity, pseudo factor analysis, and more experimental methods such as the application of convex hulls to item analysis and free text scoring. Some sections are well developed and available, others will be added incrementally.
Vendor agnosticism
I am a fan of open source models wherever it makes sense for their transparency and reproducibility and use open source models in many parts of this book. After working in the technology industry for many years, I also recognize that models from the hyperscalers like Microsoft and Amazon Web Services have their attractions.
These providers can offer well supported enterprise grade models that are integrated into corporate technology ecosystems with clear legal protections. Practitioners should also develop versatility in their methodological approach given that it’s very early in the LLM technology cycle. In this book I use a variety of open and closed source models from various LLM and cloud computing vendors.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
