Model Integrity
Appen as the independent auditor for model trust, safety, and regulatory compliance. We provide the rigorous human verification that ensures your models are accurate, unbiased, and compliant before and after release.
Data Capabilities
Six specialised evaluation services for teams that need to know their model is ready, not just capable.
Hallucination & Factuality Benchmarking
Human-verified evaluation of model outputs against factual ground truth across medicine, law, science, and finance. Appen's hallucination benchmarking service uses domain-verified contributors to identify confabulation, source misattribution, and confident inaccuracy at the rates that matter for enterprise deployment.
Regulatory & Ethics Audits
Structured evaluation of model outputs against the EU AI Act, NIST AI RMF, and organisational ethics frameworks. Appen supports pre-deployment AI regulatory compliance by providing the human audit trail that regulators and enterprise procurement teams require.
A/B Production Arena Testing
Side-by-side human preference evaluation of competing model versions under production-representative prompts. A/B testing AI models with real human evaluators closes the gap between automated benchmarks and actual user preference, providing the preference signal that drives final model selection.
Bias Detection & Cultural Mitigation
Systematic testing across demographic groups, languages, and cultural contexts to identify where model performance degrades inequitably. Appen's AI bias reduction service includes remediation dataset design alongside detection, not just a report of what is wrong.
Continuous Performance Monitoring
Ongoing post-deployment human evaluation at regular intervals to detect model drift, performance degradation, and emerging failure modes before they affect users. Continuous monitoring converts one-time evaluation into a sustained quality guarantee.
LLM-as-a-Judge Rubric Design
Expert-designed scoring rubrics for automated LLM evaluation pipelines, including calibration datasets and human-model agreement scoring. Well-designed rubrics are what separate LLM-as-a-judge systems that correlate with human quality from those that merely produce scores.
Ready-to-Use Datasets
Licensed off-the-shelf data available now or coming soon — accelerate development without starting from scratch.
English (United States) Adversarial Prompts for LLM Red Teaming
Repeatable adversarial prompt dataset for measuring harmful output, refusal behaviour, and policy adherence across model versions.
English Inverse Text Normalisation Test Set
Deterministic inverse text normalisation test set for validating correct rendering of numbers, dates, and identifiers, ideal for regression testing.
English Named Entity Recognition news text
Named entity recognition news corpora for evaluating extraction accuracy, bias, and cross-language consistency.
Case Studies
How leading AI organisations trust Appen for model integrity data.
TrustLab: Creating a Safer Web Experience
Using sentiment analysis and human-powered content moderation to detect harmful content and improve trust and safety across social media platforms.
Children’s Internet Safety
Through Search Relevance Improving search relevance and implementing content filtering on a popular children’s video platform to ensure safe, age-appropriate results.
Johns Hopkins: AI-Powered Behavioral Neuroscience
Using the Appen Data Annotation Platform to precisely label video data, enabling AI models to track spider movements and study underlying behavioral motivations.
Allen Institute for AI: Enhanced Scholarly Research Experience
Using the Appen Platform to improve citation intent classification for Semantic Scholar, an AI-powered academic search engine serving researchers globally.
Insights & Resources
Expert thinking on model integrity from Appen’s data scientists and AI researchers.
Rewarding Responsible Restraint: A New AI Safety Evaluation Paradigm
A tricategorical scoring framework that replaces binary safe/unsafe labels, rewarding models that explain ethical refusals.
Red Teaming at Scale: How to Systematically Stress-Test Your LLM
A practical guide to building adversarial evaluation pipelines that uncover safety failures before deployment.
Building a Responsible AI Framework: From Policy to Practice
How AI developers can move from aspirational principles to concrete, measurable responsible AI implementation.
Ready to build with confidence?
Talk to our team about model integrity solutions—from hallucination benchmarking to regulatory compliance audits.