Responsible Human–AI Content Workflow

Overview

A working demonstration of the principles my research argues for: a documented workflow in which generative AI assists with production tasks — drafting, organising, coding support, formatting — while a human remains identifiably responsible for meaning, accuracy, ethics, safety and final approval of every output. This website and my research materials are produced under this workflow.

Problem

“Responsible AI use” is easy to endorse and hard to specify. Most guidance stops at principles. The practical question is where exactly human judgement must sit in a real production process, and what records make responsible use demonstrable rather than merely claimed.

My role

Designed the workflow, applies it in daily research practice, and maintains the documentation and checklists.

Research question

What does a defensible division of labour between human and AI look like in research and content production — and how can it be documented so that responsibility is traceable?

Method

Iterative design in practice: each stage of my research production (literature work, drafting, coding assistance, formatting) was mapped, and for each stage the workflow specifies what AI may do, what only a human may do, and what record is kept.

The workflow

Stage	AI may assist with	Human is responsible for
Planning	Structuring options, outlines	Goals, scope, framing
Research	Locating and summarising sources	Reading sources, judging relevance and quality
Drafting	First-pass prose, reformulation	Claims, argument, interpretation
Analysis / code	Boilerplate, refactoring suggestions	Analytic decisions, correctness
Verification	Flagging inconsistencies	Checking every factual claim against sources
Approval	—	Final sign-off on every published output

Two rules are absolute: no AI-generated claim is published without human verification, and the human approver is accountable for the final artefact as if they had produced every word.

Tools

General-purpose LLM assistants, verification checklists, disclosure notes, version history as the audit trail.

Process

Each output carries a short internal record of what AI assisted with and who verified what. The overhead is deliberately small — a workflow that is too heavy to follow produces false documentation, which is worse than none.

Findings

• [Add verified observations here — e.g. where the workflow catches errors most often]

Outputs

• The documented workflow (this page)
• Verification checklist — see the Technical Writing section for the derived guides
• The AI-use statement on this site’s Trust & Integrity section

Impact

• [Add verified impact here]

Limitations

• Self-imposed workflows depend on discipline; there is no external enforcement.
• The workflow is designed for individual research practice and would need adaptation for team settings.
• It documents process, which constrains but cannot guarantee output quality.

Lessons learned

• The verification stage is where responsibility becomes real; every stage before it is preparation.
• Recording AI assistance at the moment of use is cheap; reconstructing it later is impossible.
• [Add further lessons here]

Related publication

• [Add related publication when available]

Downloadable materials

• [Add the checklist to public/downloads/ and list it in the frontmatter downloads field]