Human review by design
Responsible AI & What We Do Not Automate
AI should assist bounded work with approved data, source context, human review, and clear escalation, not silently own consequential decisions.
The answer before the details
Tensor Garden evaluates whether a workflow is suitable for AI before selecting a model or automation tool. Good candidates have clear inputs, bounded outputs, source context, review, and fallback. Consequential decisions, unsupported claims, sensitive actions, and ambiguous exceptions remain human-owned unless an accountable and appropriately authorized process says otherwise.
What this page establishes
- Define the approved task, data boundary, source context, reviewer, and fallback.
- Use human review for outputs that affect people, money, rights, safety, or material commitments.
- Label demonstrations and AI-generated examples so they are not mistaken for customer evidence.
When this matters
- Employees are already using general-purpose AI with company information.
- A workflow drafts customer, legal-adjacent, financial, health, insurance, or personnel content.
- Leadership wants internal knowledge retrieval, agents, voice workflows, or automated routing.
What to avoid
- AI does not replace accountable professional, legal, clinical, financial, insurance, employment, or safety judgment.
- A model output is not treated as source evidence when the underlying source can be reviewed.
- High-impact actions do not proceed silently without appropriate authorization and escalation.
What buyers can verify
- Inspect the allowed task, prohibited data, model access, sources, reviewer, and fallback.
- Test ordinary cases, ambiguous inputs, missing sources, unsafe requests, and escalation behavior.
- Review logs, user feedback, model or prompt changes, and known failure patterns over time.
Operating evidence
Make the approach inspectable before the work begins.
- 01
Start with the decision boundary
The workflow must identify what AI may draft, classify, summarize, retrieve, or route, and what requires responsible human judgment.
- 02
Control data and sources
Approved tools, data minimization, permissions, source references, and prohibited-data rules should be defined before broad use.
- 03
Design review and fallback
Users need to know when to verify, reject, escalate, or stop the workflow, and owners need a way to review errors and changes over time.
Questions buyers ask
What work should not be fully automated?
Work involving consequential judgment, unclear authority, sensitive exceptions, unsupported claims, or material commitments should retain appropriate human ownership.
Does human review mean reading every AI output?
The review design depends on impact and risk. Some workflows require approval for every output; others may use sampling, thresholds, source checks, and escalation.
Can AI use internal company knowledge?
Yes, when sources, permissions, data boundaries, retention, access, citations, and ongoing ownership are designed and reviewed explicitly.
Connect the trust boundary to the work
This page describes the approach and boundaries for this topic. The engagement scope remains the source for what is included in a specific project.
Turn trust questions into a scoped, reviewable roadmap.
The assessment identifies owners, systems, vendors, data, risk, workflow friction, evidence gaps, and the boundaries that should shape the first approved phase.