Human–AI Ledger (HAIL) v1.0

Authors: Evan P. Troendle
Version: 1.0
Date: 2026-02-19
License: CC-BY-4.0
DOI: https://doi.org/10.5281/zenodo.17597705

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Overview

The Human–AI Ledger (HAIL) defines a structured, repeatable workflow for human–AI collaboration.
It captures ethical, creative, and procedural context through structured checkpoints and a session ledger.
HAIL is designed to facilitate transparency, legibility, and auditability in co-creative AI processes.

HAIL is descriptive, model-agnostic, and implementation-flexible: it can be applied across language models and creative domains to track provenance, decision points, and ethical considerations.

This repository contains:

1. Workflow Instructions — human-readable guidance for conducting HAIL sessions.
2. Session Ledger Schema — machine-readable YAML structure for automated validation.
3. Proposed Extensions & Future Directions — proposed extensions: session comparisons, metrics, prompt libraries.

Examples illustrating HAIL sessions in practice are planned for a future release.

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Repository Structure

HAIL_v1.0/ ├─ HAIL_Workflow_v1.0.md # Core human-readable workflow with step-by-step instructions for Sign-On, Iterative Collaboration, and Sign-Off & Validation. ├─ HAIL_Workflow_Diagram_v1.0.md # Visual overview of the HAIL workflow structure. ├─ HAIL_Schema_v1.0.yaml # Machine-readable YAML schema describing the structure of the Session Ledger for automated validation and process traceability. ├─ HAIL_Extensions_v1.0.md # Proposed extensions / future directions: session comparisons, ledger-based session recovery, quantitative metrics, prompt libraries. └─ README.md # High-level summary, citation info, links to files

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Citation

If using or adapting this workflow, please cite:

Troendle, Evan P. (2026). Human–AI Ledger (HAIL): A Structured Workflow for Traceable, Reproducible Human–AI Collaboration (Version 1.0). Zenodo. https://doi.org/10.5281/zenodo.17597705

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Usage

1. Review the stepwise workflow in HAIL_Workflow_v1.0.md.
2. Adapt the Seed Block, Goal Statement, and Checkpoint structure for your own human–AI collaboration session.
3. Optionally record session provenance (timestamps, model version, reflections) to generate a complete Session Ledger.

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Keywords

• human-AI collaboration
• reproducibility
• ethics
• provenance
• workflow design
• FAIR data
• AI transparency
• AI governance
• human-in-the-loop
• explainable AI