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RLHF (Reinforcement Learning from Human Feedback) + DPO (Direct Preference Optimization)
Smart Automation Meets Human Judgement for Safer AI
At Digital Divide Data (DDD), we help organizations create safer and more trustworthy digital experiences. Our Trust & Safety Services focus on protecting users and brands through practical solutions like identity verification, fraud and spam detection, brand safety checks, and content moderation. By combining smart automation with human expertise, we make sure risks are caught early, harmful activity is reduced, and AI systems can be used responsibly and safely.
Our Trust and Safety Services
Our holistic approach and excellence for digital trust and safety are reflected in our use-case offerings.
Scenario Identification & Mining
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User Verification & ID Checks
Strengthen trust and security with advanced document verification, fraud flagging, and compliance checks to ensure every user is authentic and meets platform standards.
Spam & Bot Detection
Identifying fake accounts, automated bots, and suspicious posting patterns that disrupt user experiences and damage credibility.
Brand Safety & Ad Quality Review
Review ads and placements to guarantee alignment with brand rules, audience expectations, and industry standards.
Fraud Auditing & Verification
Mitigate financial and reputational risks through transaction reviews, account-level analysis, and proactive fraud prevention strategies.
Low-Severity Content Moderation
Filtering out profanity, harassment, and straightforward policy violations, keeping online spaces safe and respectful.
AI Data Labeling & Annotation for Safety
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Bias Detection in Data
Label training datasets to identify and reduce gender, racial, and cultural bias in AI systems.
Toxic Content Tagging
Detect and annotate harmful language, hate speech, or inappropriate content to strengthen AI safety.
Context-Aware Annotation
Apply nuanced labels that help AI distinguish between context-driven safe and unsafe usage.
Prompt Engineering & Red Teaming
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Vulnerability Testing
Design and execute prompts to expose weaknesses in generative AI systems.
Harmful Output Simulation
Stress-test models with adversarial prompts to detect unsafe, biased, or harmful responses.
Red Team Evaluation
Perform systematic red teaming exercises to uncover risks before deployment.
AI Output Validation & Fact-Checking
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Hallucination Reduction
Validate AI-generated outputs against trusted knowledge sources to reduce factual inaccuracies.
Policy Compliance CheckingEnsure AI outputs meet ethical, legal, and organizational standards.
Human-in-the-Loop ReviewCombine automated checks with expert human validation for high-stakes content.
AI Bias & Fairness Audits
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Discrimination Detection
Identify bias in model predictions across demographic groups.
Fairness Assurance
Run audits to ensure equitable treatment across use cases and applications.
Ethical AI Review
Evaluate models against fairness and inclusivity benchmarks to promote responsible AI adoption.
Model Safety Evaluation & Auditing
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Performance Against Safety Standards
Evaluate AI models against predefined safety benchmarks to ensure reliability.
Risk Assessment Frameworks
Assess potential misuse scenarios and unintended consequences of deployment.
Ongoing Safety Audits
Continuously monitor and audit models post-deployment to detect new risks.
Data Labelled
500M+
Safety Critical Events Identified, Analyzed, and Reported at a Market-Leading P95 Quality Rating
Success Rate
91%
Pilot Projects Converted to a Full-Scale Production Pipeline
Cost Savings
35%
Top of the Line Cost Savings for ML Data Operation Customers
Time to Launch
10 Days
Time to launch a new Data Operations Workstream from ground-up, concept to delivery
Why Choose DDD?
Strategic
We are more than a data labeling service. We bring industry-tested SMEs, provide training data strategy, and understand the data security and training requirements needed to deliver better client outcomes.
Reliable
Our global workforce allows us to deliver high-quality work, 365 days a year, with data labelers across multiple countries and time zones. With 24/7 coverage, we are agile in responding to changing project needs.
Consistent
We are lifetime project partners. Your assigned team will stay with you – no rotation. And as your team becomes experts over time, they train more labelers. That’s how we achieve scale.
Flexible
We are platform agnostic. We don’t force you to use our tools, we integrate with the technology stack that works best for your project.
What Our Clients Say
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Frequently Asked Questions
What are Trust & Safety services?
Trust & Safety services are designed to protect users, platforms, and brands from fraud, harmful content, abuse, and misuse of digital systems. At DDD, these services combine automation and human expertise to ensure safer online interactions and responsible AI deployment.
How does DDD combine automation and human judgment?
We use smart automation to handle scale, speed, and pattern detection, while trained human experts provide contextual understanding, judgment, and validation, especially for nuanced, high-risk, or edge-case scenarios. This hybrid approach delivers higher accuracy and reliability.
How does DDD support identity verification and fraud prevention?
Our teams perform document verification, user identity checks, transaction reviews, and fraud auditing to flag suspicious behavior early, reduce financial risk, and help platforms meet regulatory and compliance standards.
Can DDD help make AI systems safer and less biased?
Yes. We offer AI bias detection, fairness audits, toxic content tagging, and context-aware data annotation to help organizations identify risks, reduce bias, and improve the safety and inclusivity of AI models.
What is prompt engineering and red teaming?
Prompt engineering and red teaming involve systematically testing AI models with adversarial and edge-case prompts to uncover vulnerabilities, unsafe behaviors, and bias before deployment. This helps organizations proactively manage AI risk.
How does DDD reduce AI hallucinations and inaccurate outputs?
We validate AI outputs against trusted sources using automated checks and expert human review. This human-in-the-loop approach reduces hallucinations and ensures outputs meet quality, policy, and compliance standards.
How does DDD ensure data security and confidentiality?
We follow strict data security protocols, compliance requirements, and client-specific governance standards to protect sensitive data throughout the engagement lifecycle.