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AssemblyAI’s content moderation API implements a context-aware severity scoring system that helps developers build more intelligent content filtering solutions. This guide examines how the severity parameter works through practical examples.

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Severity Score Structure

The API returns severity scores across three levels for each detected label: 
"severity_score_summary": {
    "label_name": {
        "low": float,    // 0-1
        "medium": float, // 0-1
        "high": float    // 0-1
    }
}
Please refer to our API Reference for Content Moderation for the full response. The scores represent probability distributions across severity levels, always summing to 1.0.

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Case Study: Contextual Analysis of Alcohol References:

To demonstrate how the system evaluates context, let’s analyze three different transcripts discussing alcohol: Educational Context Source: What Are Alcohols? | Organic Chemistry (full transcript + audio) 
"severity_score_summary": {
    "alcohol": {
        "low": 1.0,
        "medium": 0,
        "high": 0
    }
}
The system identifies the educational context, assigning maximum low severity despite explicit alcohol-related terminology. Social Context Source: How I Learned To Go To A Bar Alone And Meet Women (full transcript + audio) 
"severity_score_summary": {
    "alcohol": {
        "low": 0.55243415,
        "medium": 0.44756585,
        "high": 0
    }
}
References to bars and casual drinking split the probability between low and medium severity levels. Substance Abuse Context Source: Steve-O: I’m grateful my alcoholism was severe (full transcript + audio) 
"severity_score_summary": {
    "alcohol": {
        "medium": 0.22141029,
        "high": 0.7785897,
        "low": 0
    }
}
Discussion of addiction and abuse triggers high severity scores, reflecting potentially sensitive content.

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Conclusion

The severity scoring system provides a sophisticated way to understand not just what topics are mentioned, but how they’re discussed. Each severity level (low, medium, high) represents a probability score between 0 and 1, allowing developers to implement precise content filtering based on their specific needs. For example, developers can set custom thresholds for content classification (e.g., flagging content with high severity > 0.7) or create tiered content ratings based on severity distributions. This granular approach to content moderation enables more accurate and context-aware content filtering systems.