RESEARCH & PUBLICATIONS

Responsible AI that works in the real world.

Research spanning model fairness, generative augmentation, guardrails, compliance automation, and deployable AI systems.

PEER-REVIEWED & PREPRINT RESEARCH

Selected research

04 works
01

Preprint · arXiv · 2026

Newer Is Not Fairer

Gender Stereotyping in Text-to-Image AI Across Model Generations

An 8,000-image study across 20 occupations, five prompt templates, and four Stable Diffusion generations.
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02

Accepted · IEEE RTC 2026

Fairness-Latency Trade-Offs of Guardrails for Local LLM-Driven NPC Dialogue

Research on balancing response latency, safety guardrails, and fairness in local language-model game characters.
Conference presentation
03

Preprint · arXiv · 2026

When Generative Augmentation Hurts

A Benchmark Study of GAN and Diffusion Models for Bias Correction in AI Classification Systems

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04

Preprint · arXiv · 2022

Adjusting for Bias with Procedural Data

A procedural-data approach to class imbalance and model bias, with documented independent citations.
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