BR /Future of Work · Career guide
The People Who Know When AI Is Wrong
AI evaluation gives quality specialists and experienced operators a practical direction to explore: turning professional judgement into evidence.
Role status: Existing AI evaluation duties; an emerging assurance specialisation and profession.
A role built around judgement
Imagine a customer-service assistant promising a refund outside a retailer's policy. The response reads well. Someone who has handled difficult returns can still spot the problem and explain which rule the system missed. This hypothetical example illustrates work in AI quality: expertise helps define and test acceptable behaviour.
This work already exists. Anthropic's US-based Safeguards Enforcement Analyst posting describes model assessments, investigation, collaboration with domain experts and documentation. It names trust-and-safety or related technology operations experience, expects a degree or equivalent preparation, and flags sensitive content. It illustrates responsibilities, not easy entry or market size. [Anthropic: Safeguards Enforcement Analyst]
The broader profession is taking shape. In its September 2025 roadmap, the UK's Department for Science, Innovation and Technology committed to an AI assurance skills framework and foundations for professional recognition. These were plans, not proof of a completed qualification route. Evaluation supplies evidence; assurance uses evaluation and other checks to support confidence in a system. Internal evaluation and independent auditing carry different responsibilities. [UK AI assurance roadmap]
What the work involves
NIST's AI Risk Management Framework calls for testing before deployment and during operation, documented methods and results, and input from domain experts and users. Evaluation can involve choosing realistic cases, defining serious mistakes, recording failures and checking whether updates improve performance. Human judgement matters because tests must reflect the setting where a system will be used. [NIST AI RMF Core]
QA professionals have a useful foundation. The US Bureau of Labor Statistics describes their work as designing tests, identifying risks, documenting defects and explaining findings. These duties overlap with evaluation. [BLS: software quality assurance work]
A support supervisor's knowledge of exceptions or an operations specialist's understanding of handoffs could also inform test cases. These are possible transfers of experience, not automatic qualifications.
A starting point without coding
Our editorial recommendation is to start with a familiar workflow. Distinguish an incorrect answer, missing information and a case needing human referral. Practise writing criteria another reviewer can apply consistently. Technical assessment positions can demand deeper skills; the UK roadmap distinguishes governance work from technical assessment. [UK AI assurance roadmap]
Build a small, safe portfolio exercise
As an editorial learning exercise, invent a one-page returns policy and 20 fictional customer requests: clear approvals, clear refusals and ambiguous cases. Write expected decisions and reasons before reviewing responses. Assess sample AI answers, or deliberately flawed answers you write yourself, in a spreadsheet. Record the case, decision, policy evidence, error severity and suggested correction. Ask a second reviewer to score five cases, if available, and record disagreements.
Finish with a one-page report explaining what failed, how the rubric changed and what this tiny sample cannot prove. Label everything synthetic and exclude real customer or employer information. This demonstrates a method; it does not certify an AI system or guarantee employment.
Three practical first steps
- Compare relevant local role requirements with your experience.
- Build the synthetic evaluation exercise.
- Explain your findings and limitations to another reviewer.
AI assurance cannot promise that every displaced task will become a better job. It offers a direction to investigate: using professional knowledge to test whether automation deserves trust.
Sources & editorial note
- Anthropic: Safeguards Enforcement Analyst
- UK AI assurance roadmap —
- NIST AI RMF Core
- BLS: software quality assurance work —
This career guide distinguishes source-backed descriptions from BELL RIVER’s editorial suggestions. Learning exercises are illustrative, not employer requirements, certifications or promises of employment. Overseas examples do not establish demand or eligibility in the UAE.