LLM-generated alt-text governance 2025 — Quality scoring and signed audit trails in practice
Published: Sep 29, 2025 · Reading time: 4 min · By Unified Image Tools Editorial
LLM-assisted alt-text generation is mainstream, yet inconsistency, unsafe wording, and missing attribution keep biting teams. In 2025 the bar is higher: web engineers must combine scoring, human review, and signing so AI output ships with confidence. This article breaks down the quality metrics, approval workflow, and C2PA integration needed to govern LLM-generated alt text.
TL;DR
- Three-part scoring: track
Semantic Relevance
,Toxicity
, andPolicy Compliance
on a 0–1 scale; anything below threshold gets regenerated. - Editorial workbench: run alt-safety-linter to flag banned phrases and privacy leaks, then have reviewers sign the corrected version.
- C2PA integration: embed the final alt text inside
assertions
and deliver it with a signed manifest, preventing tampering at the CDN. - Rights attribution: if the LLM forgets
©
marks or names, pull metadata from the asset and auto-complete. - Continuous improvement: collect real screen-reader logs, retrain prompts and scoring models on a regular cadence.
Designing the quality scoring
Metric | Model / calculation | Recommended threshold | When it fails |
---|---|---|---|
Semantic Relevance | CLIP similarity / custom ViT | ≥ 0.78 | Regenerate alt text, add composition cues to the prompt |
Toxicity | Perspective API / OpenAI Safety | ≤ 0.08 | Update banned-word list, strip figurative language from prompts |
Policy Compliance | Regex + custom LLM adjudicator | ≥ 0.9 | Flag style-guide issues, escalate to reviewer for edits |
// pipelines/alt/scoring.ts
import { scoreRelevance } from './models/clip'
import { scoreToxicity } from './models/toxicity'
import { evaluatePolicy } from './rules/policy'
export async function scoreAlt({ imageVector, altText }: { imageVector: Float32Array; altText: string }) {
const [relevance, toxicity, compliance] = await Promise.all([
scoreRelevance(imageVector, altText),
scoreToxicity(altText),
evaluatePolicy(altText)
])
return { relevance, toxicity, compliance }
}
Log scores to alt-moderation.log
and attach them to the C2PA manifest for traceability.
LLM generation and review flow
graph TD
A[Prompt Builder] --> B[LLM Generation]
B --> C[Scoring]
C -->|Pass| D[Reviewer Workbench]
C -->|Fail| E[Prompt Adjuster]
D --> F[C2PA Signer]
F --> G[CDN Delivery]
The workbench displays the generated alt text next to the image preview and captures edit diffs.
// components/AltWorkbench.tsx
function AltWorkbench({ imageUrl, generatedAlt }: Props) {
const [value, setValue] = useState(generatedAlt)
return (
<div className="grid gap-4 md:grid-cols-2">
<img src={imageUrl} alt="preview" className="rounded-lg" />
<textarea value={value} onChange={e => setValue(e.target.value)} className="h-64 font-mono" />
<aside>
<h3>Banned terms</h3>
<ForbiddenList text={value} />
<h3>Accessibility checks</h3>
<AltQualityScore text={value} />
</aside>
</div>
)
}
Reviewers sign the approved text before it ships.
// pipelines/alt/sign.ts
import { sign } from '@contentauth/toolkit'
export async function signAlt({ altText, manifest }: { altText: string; manifest: any }) {
const signed = await sign(Buffer.from(altText), {
signer: {
name: 'Unified Image Tools ALT Review',
certificate: process.env.C2PA_CERT!,
privateKey: process.env.C2PA_KEY!
},
assertions: [
{
label: 'org.unified.alt-text',
data: { altText, version: manifest.version, reviewer: manifest.reviewer }
}
]
})
return signed
}
Embedding signed alt text into HTML
// components/OptimizedImage.tsx
import manifest from '../../data/c2pa-manifest.json'
export function OptimizedImage({ id }: { id: string }) {
const data = manifest[id]
return (
<figure>
<img src={data.src} alt={data.alt.text} data-alt-signature={data.alt.signature} />
<figcaption>{data.caption}</figcaption>
<link rel="alternate" type="application/c2pa" href={data.manifestUrl} />
</figure>
)
}
Use data-alt-signature
for tamper detection; the delivery layer can swap out compromised copies with the signed source.
Audit logs and dashboards
Extend metadata-audit-dashboard with an alt-text table.
CREATE TABLE alt_audit (
asset_id TEXT,
alt_text TEXT,
relevance NUMERIC,
toxicity NUMERIC,
compliance NUMERIC,
reviewer TEXT,
signed_at TIMESTAMP DEFAULT now()
);
Visualize:
- Relevance p50/p90
- Toxicity alert counts
- Average edit distance per reviewer
- Policy violation trends by category
Prompt optimization
Continuously refine prompts using moderation outcomes.
prompts:
default: |
Generate an alt text under 120 characters describing the image.
Avoid: guessing race or gender, speculative emotions.
Must include: subject, background, color, composition.
product: |
Generate product alt text under 80 characters.
Must include: product name, key feature, color.
prompt-evaluator.ts
reports pass rates per prompt; tune weekly and update misinformation rules in response to Helpful Content updates.
Field testing with screen readers
Capture real playback logs from NVDA/VoiceOver to detect awkward phrasing.
# Windows (NVDA)
nvda --speech-log --playback optimized-image.html > logs/nvda-20250929.log
Associate the logs with scoring output to pinpoint problematic alt text quickly.
Checklist
- [ ] All three scores meet thresholds before shipping.
- [ ] Reviewer edits and signatures are retained in audit logs.
- [ ] Alt text is packaged inside the C2PA manifest and signed.
- [ ] Banned terms and privacy leaks are auto-detected.
- [ ] Screen-reader playback logs are reviewed on schedule.
- [ ] Prompt pass rates are tracked on a dashboard.
Summary
LLM automation accelerates alt-text production, but governance is what keeps accessibility and legal risk in check. By layering scoring, review, and signing you can produce trustworthy alt text at scale while preserving transparency. Bake this control loop into your delivery stack so accessibility keeps improving alongside your AI tooling.
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