Overview
*Overview: Modern AI systems are susceptible to generating biased, toxic, or hallucinated content that can harm users and degrade trust. Our detection service specializes in identifying these risks within LLM outputs through a blend of human expertise and automated verification. We assess model-generated text for socially harmful biases, offensive or toxic language, and fabricated or misleading information that strays from factual truth. How it works: Clients submit LLM-generated datasets or connect data streams via AWS S3/SageMaker integration. Our process involves: Developing customized rubrics addressing bias, toxicity, and hallucination criteria Training human annotators to recognize and tag problematic content with nuanced judgments Employing automated scans to supplement and cross-check human findings Delivering detailed annotations, aggregate metrics, and remediation guidance Deliverables: Annotated datasets in JSONL/CSV with bias, toxicity, and hallucination flags and contextual comments Summary reports featuring quantitative insights on prevalence rates, severity levels, and inter-annotator agreement Suggested model tuning and content moderation improvements Audit trails ensuring review transparency and compliance documentation Quality & Metrics: Our evaluations include bias detection scopes such as gender, racial, and cultural bias, toxicity scoring including hate speech and abusive language, and hallucination identification focusing on fabricated or misleading content. Quality metrics track frequency, severity, and annotation consistency, with customizable alert thresholds. Integrations & Formats: Outputs are compatible with SageMaker Ground Truth, JSONL, and CSV formats. The service integrates seamlessly with AWS S3, SageMaker, and orchestration APIs for automated workflows. Supports evolving data domains. Security & Compliance: We align with best practices for data privacy and security, including encrypted storage, role-based access, and secure deletion compliant with contractual and regulatory mandates.
Highlights
- Expert human and automated detection of bias, toxicity, and hallucination in LLM outputs to ensure safer, fairer, and more factual AI
 
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