Define Startup App Moderation Categories for Sexual, Violence, Illegal, and Spam (Portably)

Short answer: to define moderation categories for a startup app, name observable harassment, sexual, self-harm, violence, illegal, spam, and PII signals, then route them by severity through a provider-neutral contract. That gives fintech reviewers useful queues without welding policy decisions to one provider's labels.

The hard constraint isn't producing a label. It's preserving the same review meaning when a classifier, threshold, or provider changes. A report that contains a threat, a phone number, and repeated promotional text can match violence, PII, and spam at once. If the data model permits only one category, useful evidence disappears before a human sees it.

Category is a routing signal, not a verdict.

One report can require four queues

Take a compound report: repeated payment solicitations arrive beside a threat to publish a customer's phone number, followed by language implying physical harm. The normalized result needs spam, pii, harassment, and violence; policy selects the most urgent queue but retains all four signals for the reviewer. If the system stores only the top label, a superficially correct spam classification can bury both a disclosure risk and a threat. If it stores labels but lets the classifier choose enforcement, a provider change can silently alter who gets urgent attention. This single example sets the architecture: preserve overlap, separate observation from action, and treat provider output as untrusted input to a stable policy boundary.

Overlap wins.

How should a startup app define practical moderation categories for harassment, self-harm, spam, and PII?

Start with a small multi-label vocabulary whose terms describe observable content. For a fintech app that classifies user reports before human review, seven primary families are a workable first pass:

These are families, not final enforcement rules. Keep at least four other fields beside them: severity, confidence, target, and recommended_route. A mild insult and a credible threat can both be harassment, but they should never wait in the same queue. Likewise, a phone number posted by its owner in an expected support flow isn't equivalent to someone publishing another person's account details. Context changes the action even when the observable category stays the same.

Don't hide overlap. Store every supported category with independent evidence and confidence, then let a deterministic policy layer choose the queue. The policy can send imminent self-harm or targeted violence to an urgent human lane, isolate exposed PII for restricted handling, and place suspected spam in a lower-priority campaign-analysis lane. A model can propose the signals; application code owns the operational decision.

There is a compliance reason for that boundary. Moderation text can itself contain sensitive data, so logs, traces, reviewer screens, and retry payloads need the same data-handling care as the original report. Redact where reviewers don't need raw values. Restrict access where they do. I'm not sure which retention period applies to your jurisdiction and product obligations; legal and security owners need to settle that before launch, not after the first deletion request.

Separate the taxonomy from the provider response

A portable system accepts provider-specific output only at the adapter edge. The rest of the application receives one internal object with a schema version, normalized categories, evidence spans, confidence, and an explicit fallback route. This contract should be narrower than any provider's full response. Otherwise, an optional field quietly becomes a dependency and the next migration turns into a policy rewrite.

The adapter should map, validate, and reject ambiguity. It shouldn't decide whether to suspend an account, notify a safety team, or suppress a message. Those actions belong in a policy module that can be reviewed by trust, legal, security, and operations without reading provider SDK code.

from dataclasses import dataclass, field
from enum import Enum


class Category(str, Enum):
    HARASSMENT = "harassment"
    SEXUAL = "sexual"
    SELF_HARM = "self_harm"
    VIOLENCE = "violence"
    ILLEGAL_ACTIVITY = "illegal_activity"
    SPAM = "spam"
    PII = "pii"


@dataclass(frozen=True)
class Signal:
    category: Category
    confidence: float
    evidence: tuple[str, ...] = ()


@dataclass(frozen=True)
class ModerationResult:
    schema_version: str
    signals: tuple[Signal, ...]
    requires_human_review: bool
    route: str
    source_metadata: dict[str, str] = field(default_factory=dict)


def validate(result: ModerationResult) -> None:
    if result.schema_version != "1.0":
        raise ValueError("unsupported moderation schema")
    if not result.signals:
        raise ValueError("at least one signal is required")
    if any(not 0.0 <= signal.confidence <= 1.0 for signal in result.signals):
        raise ValueError("confidence must be between 0 and 1")

Notice what isn't in that type: a vendor model ID, a vendor category enum, or an enforcement decision. Preserve those details in restricted diagnostic metadata only when operations genuinely needs them. The durable record should say what the application understood and which policy version acted on it.

Provider portability also needs a stable input envelope. Include the reported content, content modality, locale when known, report reason, actor and target roles, and only the conversation window required to understand the event. A single message can look harmless while the preceding ten messages establish stalking or repeated solicitation. Yet sending an entire account history increases exposure and makes evaluations harder to reproduce. Pick a context rule, version it, and test it.

Audio requires a staged path. An open-source speech-recognition system such as Whisper can turn speech into text before classification, but transcription uncertainty should remain visible rather than being converted into false moderation certainty. Batch processing is a separate operational mode: the OpenAI Batch API guide is one public example of an asynchronous batch interface. Batch can fit backlog reclassification and evaluation, while a report that may involve imminent harm needs an interactive path and a human fallback. Those are workload distinctions, not endorsements.

Make failures explicit before choosing a threshold

Accuracy alone doesn't tell a review team what will happen. Build an evaluation set from policy-approved synthetic cases and carefully governed historical examples, then slice it by category, severity, overlap, language, content length, and missing context. The critical question is not “Which system has the best aggregate score?” It is “Which errors place a person, reviewer, or regulated workflow in the wrong lane?”

Use separate measures for classification and routing. A result may identify pii correctly but still choose a general queue that exposes raw data too broadly. Another may find spam and miss a simultaneous threat, producing a technically plausible label with an unsafe priority. Multi-label recall, per-category precision, urgent-route miss rate, abstention rate, and reviewer overturn rate answer different questions. Define them before looking at results so the target doesn't move to flatter whichever provider is under test.

Abstention is useful. When required context is absent, the response doesn't match the schema, or confidence sits inside a policy-defined gray band, route to a human with a reason code. Don't translate “uncertain” into “allowed.” Don't translate it into “blocked,” either. Both shortcuts bury uncertainty and make reviewer feedback nearly useless.

The longest test case should be an overlap case because that is where tidy taxonomies break. A single-label provider response can still be adapted, but the catch is information loss: if overlap is common in your reports, stick with a multi-label classifier or run a second deterministic detection pass for narrowly defined data patterns. That second pass is not a replacement for contextual review.

Test delivery behavior too. A classifier can be statistically acceptable while the surrounding system loses reports through retries, duplicate jobs, or queue starvation. Give each report an idempotency key. Record input-schema, taxonomy, policy, and adapter versions. Cap retries, send exhausted work to a restricted review queue, and alert on age as well as volume. Never put raw report text in an alert, metric label, or exception message — the observability path is still a disclosure path.

Compare replacements with contract tests, then roll out narrowly

Run every candidate behind the same adapter contract and frozen evaluation corpus. Compare normalized output, routing consequences, latency distribution, rejection behavior, batch support, language coverage, data-handling terms, deletion controls, and the engineering work required to operate it. Price can be one column in that review, but it shouldn't outweigh urgent misses, data exposure, or migration cost.

The limitation is real: normalization makes replacement possible, but it can flatten a provider capability that your policy could use. Keep the common contract for production decisions and allow adapter-specific diagnostics in a quarantined evaluation store. If a specialized system materially improves a high-risk category, using it only for that lane may be better than forcing every report through one interchangeable classifier. Portability is a control, not a requirement to ignore useful differences.

Rollout should be compact. First, replay the frozen corpus and require schema-valid results. Second, shadow live traffic without changing reviewer queues, with sensitive payload access limited to the existing review boundary. Third, compare route disagreements and have policy owners adjudicate samples. Then move a small traffic slice, watch urgent-lane age, abstentions, duplicates, and reviewer overturns, and increase only after the distributions hold. Keep the previous adapter available until queued work and rollback windows have cleared.

Ship in slices.

The final decision rule is straightforward: choose the implementation that preserves your internal taxonomy, exposes uncertainty, meets the data-handling boundary, and produces acceptable routing outcomes on your own governed evaluation set. If it can't pass the same contract tests as its replacement, it isn't portable, regardless of how clean its demo response looks.

References

• OpenAI Batch API guide: https://platform.openai.com/docs/guides/batch
• Whisper open-source speech recognition repository: https://github.com/openai/whisper

Sources

• https://platform.openai.com/docs/guides/batch
• https://github.com/openai/whisper