The missing B in AI: why behavioral AI is the missing piece in how we design products, services, and systems
Bibliographic record
Abstract
Your analytics platform logged the visit. Your dashboard called it engagement. But the system saw only the click; it missed the hesitation, fatigue, frustration, and regret behind it. The Missing B in AI argues that today’s AI systems are often technically impressive yet behaviourally blind. Businesses collect vast amounts of behavioral data, but too often reduce human action to clicks, conversions, dwell time, and churn scores. The result is a generation of tools that predict what people may do next without understanding why they do it, how they feel about it, or what consequences follow. Drawing on behavioural data science, business analytics, and real-world cases from digital platforms, financial services, policy, and enterprise decision support, Ganna Pogrebna shows how apparently successful systems can quietly create misalignment: customers who receive what the model predicted but not what they needed, employees who comply without trusting, and organisations that mistake stable metrics for healthy relationships. At the centre of the book is the Built on Behavior™ framework, a practical approach to designing AI that listens to meaning, context, motivation, and friction—not just observable action. Rigorous, accessible, and deeply practical, The Missing B in AI is a guide to building systems that understand behavior before they optimise it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".