Harmful Content Classification

Trust & Safety Solutions

Harmful Content Classification
AI-driven classification and triage of prohibited and policy-violating content at scale – combining machine-learning models with safeguarded human oversight to keep platforms safe, compliant, and defensible.
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How It Works
THE CHALLENGE

Content volume has outpaced manual review

Modern platforms generate content faster than any human team can assess. Left unclassified, harmful material — abuse, violence, extremism, fraud, self-harm — erodes user trust, invites regulatory penalties, and exposes moderators to psychological harm.

DCube applies its data-science heritage to build classification systems that triage content automatically, surface only what genuinely needs a human decision, and document every action for audit. The result: faster enforcement, lower cost, and far less human exposure to disturbing material.

What DCube Delivers

  • ML classifiers tuned to your content policies
  • Multi-category risk scoring & prioritized queues
  • Text, image, audio & video classification
  • Human-in-the-loop only where automation is uncertain
  • Model monitoring, drift detection & retraining
  • Full audit trails for regulatory defensibility
Coverage

A taxonomy built around your policies

Classification models are mapped to the harm categories that matter to your platform and jurisdiction.

Violence & Extremism

Graphic violence, incitement, terrorist and violent-extremist content.

Hate & Harassment

Hate speech, targeted abuse, coordinated harassment campaigns.

Fraud & Scams

Phishing, financial fraud, impersonation, spam at scale.

Self-Harm & Crisis

Signals of self-harm routed to appropriate escalation and support pathways.

Adult & Explicit

Policy-based classification of sexual and age-inappropriate content.

Misinformation

Coordinated inauthentic behavior and manipulated media detection.

Core Capabilities

Classification engineered for scale and accuracy

Multi-Modal Models

Text, image, audio, and video classification unified under a single policy taxonomy and scoring framework.

Risk Scoring & Triage

Confidence-weighted scoring routes high-risk items for priority action and low-risk items to automated resolution.

Human-in-the-Loop

Only ambiguous, high-stakes cases reach trained reviewers — protected by exposure-limiting tooling and wellness safeguards.

Model Governance

Continuous monitoring for accuracy, bias, and drift, with retraining pipelines and versioned audit records.

Policy Alignment

Classifiers mapped directly to your community standards and the regulatory regimes you operate under.

Explainability

Every decision is logged with rationale and evidence, supporting appeals, transparency reporting, and regulator inquiries.

Our Methodology

From raw content to actionable decision

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Policy & Taxonomy Design

We translate your community standards and legal obligations into a structured, machine-actionable classification taxonomy.

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Model Development & Tuning

Classifiers are trained and calibrated to your content, balancing precision and recall against operational risk.

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Automated Triage

Incoming content is scored and routed — auto-resolved, queued, or escalated — minimizing the volume requiring human review.

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Safeguarded Human Review

Ambiguous cases reach trained, supported reviewers using exposure-limiting interfaces and rotation protocols.

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Monitoring & Continuous Improvement

Model performance, drift, and bias are monitored continuously, with retraining and full audit logging.

Standards & Frameworks

Defensible by design

Classification programs are built to satisfy platform-safety regulation and responsible-AI expectations.

EU Digital Services Act

UK Online Safety Act

NIST AI RMF

Responsible AI

Model Audit Trails

Bias & Drift Monitoring

Transparency Reporting

Classify content at scale — safely

Let’s design a classification program mapped to your policies, your jurisdiction, and your risk tolerance.

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