Bridging the Generation Gap: Age-Parity in Synthetic Customer Profiles
Bibliographic record
Abstract
Synthetic customer profiles are increasingly used to augment limited datasets for marketing analytics and personalization systems. Yet, generative methods often amplify imbalances between demographic segments-particularly younger <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\leq 35)$</tex> and older (>35) customers-leading to biased downstream models. We propose a Fairness-Constrained Tabular Variational Autoencoder (FC-TVAE) that enforces age-band demographic parity during profile generation. Starting with the publicly available Kaggle Customer Personality Analysis dataset (2,240 records with age, marital status, income, and annual spend across six product categories), we min-max normalize continuous features and discretize age into two bands <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\leq 35$</tex> vs. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$>35)$</tex>. A maximum-mean-discrepancy regularizer is applied to the VAE's latent distributions to minimize the normalized demographic-parity gap (DPG) between age slices. We benchmark FC-TVAE against Baseline TVAE, Conditional GAN, FairGAN, and DP-WGAN, using five random seeds. On the normalized spend scale, FC-TVAE achieves an MSE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.0158 \pm 0.0004(\approx 12 \%$</tex> average absolute error), compared to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.0150-0.0170$</tex> for baselines. Crucially, FCTVAE reduces DPG to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.055 \pm 0.006$</tex>, a 27% improvement over FairGAN <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(0.075 \pm 0.006)$</tex> and 42% over Conditional GAN <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(0.095 \pm \mathbf{0. 0 0 7}) (p<\mathbf{0. 0 1})$</tex>. Minority-group recall for the younger cohort rises to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 7 5} \pm \mathbf{0. 0 2}$</tex>, outperforming FairGAN <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{0. 6 8} \pm \mathbf{0. 0 2})$</tex> by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 0}$</tex>% and DP-WGAN <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(0.66 \pm 0.03)$</tex> by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$13.6 \%(p<0.005)$</tex>. These results demonstrate that integrating age-band parity constraints at the model level yields the best balance of fidelity, fairness, and coverage. FC-TVAE offers a practical, extensible framework for generating inclusive synthetic profiles, enabling more equitable personalization across age groups in marketing analytics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".