How does HumanlyAI score safety?

HumanlyAI uses a structured rubric that classifies outputs as Safe, Borderline, or Unsafe, and can include separate scoring for hallucinations, factual accuracy, tone, and overall quality.

Do you test hallucinations and factual accuracy as part of safety evaluation?

Yes. Safety evaluation can include hallucination flags, factual accuracy scoring, and overconfidence detection, especially for high-stakes domains.

What do we need to provide to start safety evaluation?

Clients typically provide prompts and model outputs, target policies or constraints, and the intended user context. HumanlyAI provides rubrics, trained evaluators, QA, and structured reporting.

How quickly can you run a safety pilot?

Pilot timelines depend on volume and QA depth, but many pilots can be completed within 7–14 days once scope and rubrics are aligned.

Safety-first human evaluation

AI Safety Evaluation for GenAI Systems

Q: What is AI safety evaluation?

AI safety evaluation is the process of assessing model outputs for user harm risk, policy violations, unsafe guidance, and reliability issues such as hallucinations and overconfidence.

HumanlyAI helps teams identify unsafe, misleading, or non-compliant model behavior using structured human judgment. This need has grown as models become more fluent — a dynamic we explore in Why Fluent AI Is Still Dangerous.

Safe / Borderline / Unsafe Hallucination Flags Refusal Quality Structured Reporting

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Typical pilot turnaround: 7–14 days (scope dependent).

What we deliver

Safety Classification

Structured safety scoring for real user risk.

Safe / Borderline / Unsafe
User harm and misuse risk
Policy compliance checks

Hallucination & Accuracy

Reliability checks that catch false authority.

Hallucination flagging
Factual accuracy scoring
Overconfidence detection

Refusal Quality

When the model refuses, it must refuse well.

Appropriate refusal behavior
Safe alternatives where possible
Non-evasive, non-harmful framing

Reporting

Structured outputs you can track and act on.

Score distributions and findings
Examples of failure modes
CSV/JSON delivery for internal use

How it works

Safety evaluation workflow

1) Scope & context

You share target use case, policies, and output samples.

2) Rubrics & calibration

We align on definitions for safety and reliability scoring.

3) Evaluate + QA

Evaluators score outputs; QA monitors agreement and edge cases.

4) Deliver findings

We deliver structured results and examples of key failure modes.

Why HumanlyAI

Defensible human judgment, consistently applied

Trained evaluators

Certification + calibration so “safe” means the same thing every time.

Safety-first bias

Conservative scoring when outputs could mislead or harm.

Auditability

Gold tasks + QA review to support governance and reporting needs.

FAQ

Common questions about AI safety evaluation

What is AI safety evaluation?

Assessing model outputs for harm risk, unsafe guidance, policy failures, and reliability issues like hallucinations.

How do you score safety?

We classify outputs as Safe, Borderline, or Unsafe, and can separately score hallucinations, accuracy, tone, and overall quality.

What do we need to provide?

Prompts + outputs, target policies/constraints, and user context. We provide rubrics, trained evaluators, QA, and reporting.

How fast can a pilot run?

Many pilots can complete in 7–14 days after scope and rubric alignment (volume dependent).

Many safety issues originate from hallucinations and false authority, which we analyze in depth in this guide to AI hallucination risk.

Contact

Request a safety pilot

Email us with your use case and evaluation goals. We’ll reply with a pilot scope.

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