NEW: New Research: AI Agents and Algorithmic Redlining

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Tool GovernanceResearchBlog

Trinitite Research

Advancing the Science of AI Governance

Strategic intelligence reports from the frontlines of Trinitite research teams that are dedicated to the safe, governed industrialization of Artificial General Intelligence.

Published Research — 4 Papers

2026 · Trinitite Research Team

NEW

AI Agents and Algorithmic Redlining

Hardwiring Civil Rights Liability Into Your Source Code

A 6,000-iteration forensic audit across 6 frontier models and 100 Census-mapped demographic personas proves that foundation models don't neutralize bias — they industrialize it. Documents 5.31× STEM gender firewall, 8.46× capital allocation gap, 100% HBCU segregation by Claude, and 14.58-month promotion delays for disabled professionals.

Algorithmic Redlining
Civil Rights Liability
EEOC
Synthetic Data
GRC

2026 · Trinitite Research Team

NEW

AI Agents and the Meritocracy Delusion

An Econometric Audit of AI Agent Hiring Bias at Scale

AI agent bias detected for hiring (interview odds) in econometric audit of 6,000 AI resume evaluations: proves a 63.6% penalty against male applicants, proxy ageism via Lexical Age Cohorting, guardrail-induced overcorrection inflating scores for disability disclosure, and 85%+ variance driven by vendor selection alone.

Hiring Bias
Econometrics
EEOC Liability
Proxy Ageism
GRC

2026 · Trinitite Research Team

NEW

Your Agents Are an Autonomous Liability

The Fiduciary Fallout of Probabilistic Tool Calls

A 4,000-iteration red-team study across eight frontier models proves that premium AI pricing does not guarantee premium security. We expose the catastrophic variance of LLMs through Context Poisoning, demonstrate the Streaming Payload Vulnerability, and introduce the Trinitite Governor — a deterministic architecture that blocked 100% of malicious payloads.

Red-Teaming
Agentic AI
Tool Calling
GRC

2026 · Trinitite Research Team

Why Probabilistic AI is Negligent and Uninsurable

Defining the New Standard of Care for the Autonomous Enterprise

The foundational research establishing the physics of AI safety drift. Demonstrates how floating-point non-associativity and batch variance create hardware-level race conditions in AI guardrails, making probabilistic self-policing actuarially void. Introduces the Bitwise Standard framework.

IEEE 754
Actuarial Science
Governance
Nondeterminism

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