TRACE is open-source forensic software for analyzing AI-human conversational evidence with repeatable classification, benchmark governance, and auditable evidence packages.

What does TRACE implement?

TRACE implements the AI Behavioral Trajectory Forensics methodology through transcript ingest, classification, sliding-window analysis, inter-rater reliability workflows, and structured report export.

Does TRACE replace a human examiner?

No. TRACE is a decision-support and evidence-packaging tool for trained examiners. It preserves provenance, human overrides, and repeatable outputs, but final determinations remain human work.

TRACE Open Source

Trajectory Analysis for Conversational Evidence

TRACE (Trajectory Analysis for Conversational Evidence) is open-source forensic software for AI conversational harm cases. It ingests transcripts, preserves provenance, classifies system behavior and user vulnerability, computes repeatable findings, and exports auditable evidence packages for expert review.

TRACE is the operational implementation path for AI Behavioral Trajectory Forensics. Where ABTF defines the methodology, TRACE provides the software workflow: how to move from raw conversational artifacts to reviewable outputs with documented chain of custody, explicit classifier configuration, and reproducible evidence packages.

Why TRACE exists

Conversational AI incidents are increasingly reviewed in settings that require more than screenshots and narrative summaries. Investigators need a toolchain that preserves source integrity, records analytic decisions, and makes the resulting package inspectable by another reviewer.

TRACE was built for that problem. The priorities are:

repeatability
auditability
bounded hosted-model risk
human-review visibility
exportable evidence packages rather than opaque scores

Core capabilities

Transcript ingest and provenance

TRACE accepts multiple conversational evidence formats, normalizes them into a shared internal schema, and records source hashes and custody information before transformation. Current parser coverage includes JSON, CSV, plain text, court-style transcripts, AXIOM-style JSON exports, and UFED-style XML exports.

Classification workflow

TRACE classifies:

system messages against the behavioral harm taxonomy
user messages against the TRACE vulnerability scale derived from C-SSRS
rolling interaction windows for trajectory-level interpretation

Classification can run through deterministic heuristics, mock hosted mode, hosted-provider inference, and manual review pathways with explicit override rationale capture.

Correlation and report export

From the classified transcript, TRACE computes repeatable findings such as inappropriate response rate, pattern distribution, and crisis failure rate. It then exports an evidence package containing machine-readable artifacts, verification output, manifests, audit logs, Markdown reporting, and PDF reporting.

Reliability and validation support

TRACE includes dual-coder import, inter-rater reliability computation, validation fixtures, and benchmark workflows. That makes it usable not only for case analysis but also for method evaluation and operational hardening.

Operational stance

TRACE is designed as a forensic workflow candidate, not as an autonomous judge.

TRACE does not make final forensic determinations.
TRACE does not replace expert review.
TRACE is built to preserve provenance, overrides, and reviewability.

That stance is deliberate. In high-stakes harm analysis, hidden automation is a liability. TRACE keeps the analytical chain visible.

Open-source access

GitHub: github.com/empathyethicist/trace
Package name: trace-forensics
Current documented version in the local project metadata: 0.1.3

Fast start

python3 -m venv .venv
source .venv/bin/activate
pip install -e .
trace init --root ./trace-workspace
trace validate --reference ./validation/companion_incident.json --root ./trace-workspace/validation_runs

Relationship to the paper

The AI Behavioral Trajectory Forensics paper defines the method and evidentiary boundaries. TRACE carries that method into a usable software workflow for investigators, researchers, and expert reviewers who need more than an abstract description.

The paper gives the forensic logic. TRACE gives the operational toolchain.

Related Terms

AI Behavioral Trajectory Forensics → Research & Publications → For Researchers →