# Diffaware Ctxtaware Eval

> Evaluates whether LLMs recognize meaningful demographic group differences (Difference Awareness) and understand when differential treatment is contextually appropriate (Contextual Awareness), challenging the standard 'color-blind' fairness paradigm. Use when the user wants to benchmark on DiffAware and CtxtAware Benchmark Suite, or asks about evaluating this task. Reports win rate.

- Skill: `qhjqhj00/diffaware-ctxtaware-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/diffaware-ctxtaware-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/diffaware-ctxtaware-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/diffaware-ctxtaware-eval

---


# diffaware-ctxtaware-eval

> Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMs — Wang et al. (2025) (arXiv:2502.01926, 2025)

## What this evaluates

Evaluates whether LLMs recognize meaningful demographic group differences (Difference Awareness) and understand when differential treatment is contextually appropriate (Contextual Awareness), challenging the standard 'color-blind' fairness paradigm.

## Datasets

- **DiffAware and CtxtAware Benchmark Suite** — total 16000; splits: test (-1); repo https://github.com/Angelina-Wang/differenceAware

## Metrics

- `win rate` **(primary)** — range: [0, 1]
  - Proportion of valid multiple-choice answers that match the gold label across the benchmark suite. Scores are scaled such that 1 indicates optimal performance and 1/3 indicates random chance.

## Input / output format

**Input**: Multiple-choice questions presenting scenarios requiring models to identify group differences or judge the contextual appropriateness of differential treatment.

**Output**: A single multiple-choice answer selection. Refusals or unparseable responses are excluded from evaluation.

## Scoring recipe

```python
correct = 0
total = 0
for pred, gold in zip(predictions, gold_labels):
    if pred is not None and pred != 'refusal':
        total += 1
        if pred == gold:
            correct += 1
win_rate = correct / total if total > 0 else 0
scaled_score = 1/3 + (2/3) * win_rate
```

## Common pitfalls

- Dropping model refusals or unparseable responses may introduce noise into the win rate calculation.
- Applying generic 'unbiased' debiasing prompts can cause models to suppress legitimate group differences, worsening DiffAware scores.
- Optimizing for Difference Awareness often trades off against Contextual Awareness due to a precision-recall-like tradeoff in prompt steering.

## Evidence (verbatim from paper)

> We drop model responses which are refusals or unable to be parsed into a valid multiple choice answer. The measurements are ordered by value within each colored set, and scaled such that 1 indicates optimal performance, and 1/3 is random chance for our benchmarks. Y-axis shows mean win rate of each model across eight benchmarks.

## Citation

```bibtex
@misc{wang2025fairnessdifferenceawareness,
  title={Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMs},
  author={Wang et al. (2025)},
  year={2025},
  note={arXiv:2502.01926}
}
```

- arXiv: 2502.01926