This article is part of the “AI, Trust, Privacy, and Responsible AI Tidbits” series.
TL;DR
- Content moderation requires both high recall (catching all the bad) and high precision (not blocking the good). Traditionally, teams combine automation with human reviewers when confidence is low.
- With the rise of Generative AI (GenAI) and the explosion of user-created content, platforms face enormous scaling challenges.
- Fortunately, GenAI offers breakthrough performance in classifying nuanced and complex cases, enabling cost-effective scaling, improving reviewer wellbeing, and speeding up decision-making.
Context
Digital platforms must review the massive volume of content posted daily, from posts, ads, and job listings to private messages, to prevent abusive, harmful, or policy-violating material from slipping through. Failing to do so risks harming user experience, brand reputation, and the sustainability of the platform itself.
Most mature trust teams address this challenge using a two-layer architecture (see Figure 1):
- L1 – Screening: Automated systems (traditional AI, rules, embeddings) scan all content created efficiently and with low latency. When confidence is high, the system takes action automatically; when confidence is low, it flags content for further review.
- L2 – Human Review: A small fraction of content, flagged by AI or reported by users, is escalated to trained human reviewers, who make detailed policy determinations.
In short, the system screens everything but relies on human reviewers to augment performance, especially when precision is lower with automated systems or judgment is critical.
Figure 1: E2E Trust Policy Review (excluding appeals)
As shown in Table 1, each layer has distinct strengths and limitations, especially:
- Screening AI has limited recall. Improving it often leads to cost or speed trade-offs. Models that are efficient at scale are limited in performance, and it would be too expensive (and slow) to flag too many items to the human review layer.
- Human review is costly, slow, and hard to scale, especially as content volume grows exponentially, for example with AI-generated creatives in ads or conversational agents generating interactions.
Table 1: Levels and key characteristics
The Generative AI revolution
Generative AI models introduce a major shift: they can analyze and understand complex content (text, images, video) with nuance approaching human-level comprehension, allowing them to identify and act upon policy violating or harmful content swiftly and accurately. This unlocks powerful improvements in trust systems:
- Scalable Solutions. GenAI can rapidly and cost-effectively handle high-volume, repetitive tasks, freeing up human reviewers to focus on content that requires deeper judgment or nuanced understanding. And it can be further optimized through techniques like distillation, helping systems keep pace with growing demands. These models also understand multiple languages and cultural nuances, making them well-suited to moderate content across diverse regions.
- Adaptive Defenses. These models learn from evolving patterns of digital platforms, helping systems proactively understand and address emerging threats and abuse trends, not just react to known ones. This adaptability is key in an online world where the nature of bad content is constantly changing.
- Better Decision Quality and Reviewer Wellbeing. AI capabilities interpreting nuanced language, imagery, and context accurately detects subtle signals of harmful or inappropriate content that human reviewers might miss. And by shielding human reviewers from exposure to the most harmful content, AI reduces psychological stress and improves workplace wellbeing.
Note: not all moderation tasks (e.g., measurement, appeals, oversight) may be suitable for automation, but many core workflows can benefit dramatically from GenAI integration.
Evolution of Content Moderation Leveraging Gen AI
There are three key areas where GenAI can complement and enhance existing systems, striking a balance between cost, speed, and quality:
- Smaller Language Models (SLMs) at Screening We cannot run massive models (e.g., 70B+ parameters) on all content due to latency and cost constraints. But we can deploy distilled, lightweight SLMs that bring stronger comprehension to the screening layer without sacrificing efficiency.
- AI Agents in Advanced Review In the L2 layer, GenAI-powered AI Agents can complement human reviewers by efficiently handling simpler or repetitive cases. These agents can tap into internal resources (e.g., past decisions) or external (e.g., web knowledge) to make informed judgments. The system can dynamically decide whether an item should be routed to an AI Agent, a human, or a combination, depending on the risk, regulatory requirements, or efficiency goals. Moreover, the increasing scale in this layer allows for the screening layer to flag a much larger fraction of items without significantly compromising latency and cost.
- Human Reviewers with AI Superpowers For high-stakes cases (e.g., appeals, critical measurement or auditing tasks), human reviewers can leverage AI tools effectively turning human reviewers into “super-reviewers” with amplified judgment. Examples of such tools include agents providing recommendations, chatbots offering policy or protocol guidance or additional context, access to prior decisions or similar precedents.
Figure 2: Reviewed E2E Trust Policy Review, including SLMs, AI Agents and superhumans
Unlocking New Opportunities with Generative AI-Powered Agents
Generative AI-powered agents require a more in depth discussion, since they open exciting new possibilities for advanced content review, bringing both technological disruption and practical benefits. These agents can approach human-level performance in understanding nuanced language and visuals, while also offering gains in scalability, cost efficiency, and human reviewer well-being.
One of the biggest bottlenecks in trust systems is the L2 (Advanced Review) layer. It has higher latency, it is expensive and it does not scale well, and as a result, the full end to end suffers i.e. the screening layer is limited into how many things could be flagged for higher-quality review. Human reviews cost 10–100 times more per item than even today’s GenAI models and are much harder to scale for low latency and consistent quality. Yet, while current GenAI models excel in many areas, they still struggle to fully match highly trained humans on the most complex or novel cases, especially for highly nuanced policies. Also, running the most advanced models across all platform content remains prohibitively costly today, and even if that became affordable in the future, yet more powerful and costly models would likely preserve the need for a two layered screening + advanced review system.
Introducing AI Agents alongside human reviewers creates a flexible, optimized system. It allows
- dynamic routing of cases based on risk, regulatory, or latency needs
- the combination of human and AI decisions to improve quality or oversight, and
- the deployment of multiple specialized agents such as different model architectures (i.e. GPT4o or Deepseek-R1), specialized or fine-tuned agents for specific policies (i.e. hateful rhetoric or violence) or content modalities (i.e. text, image or video).
This unlocks new levels of tunability, optimization and efficiency in matching the right reviewers (human or AI) to the right tasks, possibly combining and reconciling multiple recommendations into a final decision (as depicted in Figure 3).
Figure 3: Expanded Advanced Review Layer, optimizing matching of items to human and AI reviewers
Benefits of a Human + AI Agent System
Bringing it all together, Table 2 highlights the key benefits of expanding the concept of a reviewer to encompass both human reviewers and AI Agents.
Table 2: Benefits of human + AI Agents content moderation
Summary
This post highlights how the rise of Generative AI is driving an explosion of digital content, making it essential for platforms to evolve their trust review systems with greater automation and scalability. By adopting an architecture that combines traditional AI, advanced Generative AI models, and human reviewers, platforms can optimize costs, improve decision speed and quality, and ensure their defenses keep pace with the growing volume and complexity of online content. Future posts will explore more general agentic capabilities.
Thanks to my colleagues Jiun-Ren Lin and Rishi Gupta , for reviewing and providing valuable feedback.
Disclaimers: The views here are entirely my own and do not reflect any company positions or confidential information. Banner image AI-generated.