README.md

---
license: mit
language:
- en
base_model: answerdotai/ModernBERT-large
pipeline_tag: text-classification
tags:
- hate-speech-detection
- content-moderation
- fairness
- bias-mitigation
- aae
- dialect
metrics:
- recall
---

# Dialect-Fair Hate Classifier (ModernBERT-large)

A binary hate-speech classifier fine-tuned from [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large), explicitly debiased against **African-American English (AAE) false positives**.

Off-the-shelf toxicity/hate classifiers flag benign AAE text as hateful at **2x+** the rate of benign General-American English (Sap et al. 2019). This classifier targets and measures that gap directly: it catches **93% of hate** while keeping the benign-AAE false-positive rate within **~0.04** of the benign-GAE rate.

Author: **Guy Grigsby** (Aeryx-ai). License: MIT.

## Labels

`0 = not hate`, `1 = hate`. Output the softmax probability of class 1 and threshold it. The policy is **targeted hate** (slur-as-attack, protected-group harassment) — profanity, insults, dark themes, sexual content, and political opinion are deliberately **not** flagged.

## Operating frontier

Evaluated on HateCheck (recall, FP) and a **held-out dialect-balanced benign set** (FP on high-AAE vs low-AAE text, dialect measured with [TwitterAAE](https://github.com/slanglab/twitteraae)). Pick a threshold from the curve:

| threshold | recall | HateCheck FP | high-AAE benign FP | low-AAE benign FP | dialect FP gap |
|---|---|---|---|---|---|
| 0.50 | 0.934 | 0.013 | 0.082 | 0.040 | **0.042** |
| 0.80 | 0.906 | 0.007 | 0.055 | 0.025 | **0.030** |
| 0.90 | 0.872 | 0.006 | 0.041 | 0.015 | **0.027** |

The dialect FP gap is near-parity across the whole frontier (raw bias is ~5x lower than the ModernBERT-large baseline's 0.21 gap). Default to **0.50** for max recall; raise toward **0.80** for extra parity margin (e.g. corpus filtering).

## Usage

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Aeryx-ai/dialect-fair-hate-classifier")
model = AutoModelForSequenceClassification.from_pretrained("Aeryx-ai/dialect-fair-hate-classifier").eval()

def hate_prob(text):
    enc = tok(text, return_tensors="pt", truncation=True, max_length=256)
    with torch.no_grad():
        return torch.softmax(model(**enc).logits, -1)[0, 1].item()

is_hate = hate_prob("...") >= 0.5
```

## How it was debiased

1. **Bias-aware training sources** — Measuring Hate Speech (UC Berkeley), ToxiGen, DynaHate, Civil Comments (using `identity_attack` to separate group-hate from mere toxicity). The canonically AAE-biased Davidson/Founta sets are **excluded** as label sources.
2. **High-AAE-safe augmentation** — ~14k benign high-AAE examples (mined from social text, TwitterAAE-filtered) so the model cannot use dialect as a hate cue.
3. **FP-parity gate** — release was conditioned on benign high-AAE FP ≈ benign low-AAE FP on a held-out set (not just overall F1). The eval set is held out from the augmentation distribution.

Loss reweighting and DANN-style adversarial debiasing were tried; **data volume of high-AAE-safe text** was what actually closed the gap.

## Limitations

- **English only.** TwitterAAE is a Twitter-domain distant-supervision proxy for dialect, not ground truth, and is noisier on long-form/other-domain text.
- **Recall/parity tradeoff** is explicit in the frontier — higher thresholds trade recall for margin.
- **Not for high-stakes automated decisions** about individuals. Intended for content moderation triage and training-corpus filtering, with humans in the loop.
- Trained on a "targeted hate" policy; it will not flag profanity or controversy by design.

## Citation

```
@misc{grigsby2026dialectfair,
  title  = {A Dialect-Fair Hate Classifier},
  author = {Grigsby, Guy},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/Aeryx-ai/dialect-fair-hate-classifier}}
}
```

References: Sap et al. 2019 (Risk of Racial Bias in Hate Speech Detection); Blodgett et al. 2016 (TwitterAAE); Kennedy et al. (Measuring Hate Speech); Hartvigsen et al. (ToxiGen); Vidgen et al. 2021 (DynaHate).