Designing Against Automation Bias in Human-AI Systems
When people trust AI outputs too readily, a human-in-the-loop becomes a rubber stamp. Interface and process choices that keep review meaningful.
Author profile
Articles by Dr. Aarzoo
Dr. Aarzoo co-founded AARHIT Systems on 5 October 2017 and serves as its Chief Operating Officer. She writes on delivery, quality assurance, data readiness, monitoring and human review, drawing on the engineering and delivery work of the AARHIT Systems team.
Profile
- Role
- Co-Founder and Chief Operating Officer
- Organisation
- AARHIT Systems
- Articles
- 6
- Topics
- Hybrid Intelligence, Research Notes, Computer Vision, Data Engineering, MLOps, Automation
Editorial standards
How our articles are written and reviewed
She is responsible for delivery operations, quality assurance and service management. The company's delivery framework, resourcing model and service review process, through which engagements are governed, sit under her leadership. Dr. Aarzoo is responsible for delivery operations, quality assurance and service management. The articles below draw on that work.
Articles are written to be useful, accurate and specific. They draw on established methods, recognised frameworks and practical engineering experience, and they avoid claims that cannot be supported.
Where external research or frameworks are referenced, sources are listed. Articles do not describe named client engagements. Statistics are used only when they come from a cited, reliable source.
Articles
Published articles
Writing a Proof of Concept That Actually Decides Something
Many AI proofs of concept end in a demo and a shrug. A short charter written before the build turns them into decisions.
Visual Inspection Projects: Why Conditions Matter More Than Models
Most computer vision inspection problems are solved, or doomed, by lighting, camera placement and data collection long before model architecture is chosen.
A Data Readiness Checklist Before Starting an AI Project
Most AI projects that stall do so because of data. Twelve questions to answer before committing budget, and what to do when the answers are uncomfortable.
What to Monitor After an LLM Application Goes Live
Uptime dashboards will not tell you that answers have quietly become worse. A monitoring plan for quality, grounding, safety, cost and user behaviour.
Measuring Document AI Accuracy at Field Level
An extraction system can be 95 percent accurate and still get the invoice amount wrong. Why field-level measurement drives better automation decisions.
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