---
title: DispatchBias
emoji: 🚨
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
python_version: "3.11"
pinned: false
license: mit
short_description: Emergency dispatch LLM bias benchmark (PPDS scale, EN/ZH)
---

# DispatchBias

An LLM bias benchmark for emergency dispatch classification. Tests whether demographic signals in a 911 call transcript shift the priority level a model assigns, holding the underlying incident constant. Eleven models, English and Mandarin Chinese, paired matched-incident scenarios.

Companion code for the paper:

> William Guey. *Emergency Dispatch LLM Bias: A Cross-Lingual PPDS Benchmark*. Submitted to the Humanities and Social Sciences Communications special issue on Artificial Intelligence and Emerging Technologies in Public Safety.

## What it does

Three steps in the UI:

1. **Import scenarios** from an Excel file. Each scenario provides a paired transcript (Variant A with a demographic signal, Variant B without) in both English and Mandarin Chinese. Only the raw transcript goes in the file. The dispatcher prompt and PPDS guide are prepended automatically at runtime.
2. **Collect data**. The app fans out async calls to OpenRouter across the selected models, with iteration-level paraphrase variation in the call openers and closers. Results land in an Excel file with one row per call.
3. **Build charts**. Five figures: per-language bias deltas, EN-vs-ZH overlay, PPDS distribution heatmap, cross-lingual scatter, and an effect size table.

## Methodology

**Scoring:** PPDS levels are scored ECHO=5, DELTA=4, BRAVO=3, ALPHA=2, OMEGA=1. Bias delta = mean PPDS(Variant A) minus mean PPDS(Variant B) across iterations. Positive deltas indicate the demographic signal raises perceived urgency, negative the opposite.

**Statistics:** Effect sizes reported as Cohen's d. Significance from independent t-tests between Variant A and Variant B score distributions per scenario, model, and language. Stars: * p<.05, ** p<.01, *** p<.001.

**Robustness:** Each prompt is paraphrased per iteration via cycling through ten matched opener-closer pairs in each language to reduce single-template artifacts.

**PPDS source:** Warner et al., *Annals of Emergency Dispatch and Response* 2014, Vol. 2 Issue 2 (IAED).

## Use your own OpenRouter key

The Space does not pay for runs. Provide your own OpenRouter API key in the field on the page. Get one at [openrouter.ai/keys](https://openrouter.ai/keys). Approximate cost: a full run (11 models, 10 scenarios, 10 iterations, 2 languages, 2 variants = 4,400 calls) typically lands under USD 5 with the default model mix.

If you fork the Space and want a default key for your own use, add `OPENROUTER_API_KEY` as a Space secret in the Settings tab.

## Local use

```bash
pip install -r requirements.txt
export OPENROUTER_API_KEY="sk-or-v1-..."
python app.py
```

## Citation

```bibtex
@article{guey2026dispatchbias,
  title={Emergency Dispatch LLM Bias: A Cross-Lingual PPDS Benchmark},
  author={Guey, William},
  journal={Humanities and Social Sciences Communications},
  year={2026},
  note={Under review}
}
```

## License

MIT for the code. Data and prompts are released under CC BY 4.0. The PPDS scale is the property of the IAED.