> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neuraltrust.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Content Bias

Content bias testing evaluates your model for both cognitive biases that affect reasoning and stereotypical biases that lead to discriminatory outputs. Detecting and mitigating these biases is essential for building fair and trustworthy AI systems.

## Purpose

Bias testing helps ensure your model:

* Provides fair and equitable responses across different groups
* Avoids reinforcing harmful stereotypes
* Maintains consistent reasoning regardless of framing
* Meets ethical AI standards and compliance requirements

## Bias Categories

### Cognitive Biases

Cognitive biases affect how the model reasons and makes decisions:

| Sub-Category        | Description                                                                 |
| ------------------- | --------------------------------------------------------------------------- |
| **Anchoring Bias**  | Tests if the model over-relies on initial information when making judgments |
| **Framing Bias**    | Evaluates if responses change based on how questions are framed             |
| **Positional Bias** | Assesses if the order of presented options affects model decisions          |
| **Status Quo Bias** | Tests preference for current state over alternatives                        |
| **Temporal Bias**   | Evaluates if time-related framing affects reasoning                         |

### Stereotypical Biases

Stereotypical biases lead to unfair treatment of different groups:

| Sub-Category      | Description                                                          |
| ----------------- | -------------------------------------------------------------------- |
| **Ethnic Bias**   | Tests for discriminatory responses based on ethnicity or nationality |
| **Gender Bias**   | Evaluates fairness across gender identities                          |
| **LGBTIQ+ Bias**  | Assesses treatment of LGBTIQ+ topics and individuals                 |
| **Religion Bias** | Tests for religious discrimination or preferential treatment         |

## How It Works

### Cognitive Bias Testing

Uses objective-based probes that present scenarios designed to trigger specific cognitive biases. The evaluator assesses whether the model's reasoning is affected by the bias.

### Stereotypical Bias Testing

Uses curated datasets with paired examples to detect differential treatment. The model's responses are compared across demographic variations of the same question.

**Scoring:**

* **Pass**: The model demonstrates unbiased behavior
* **Fail**: The model exhibits the tested bias

## Usage Example

### Testing for Cognitive Bias

```python theme={null}
from trusttest.catalog.content_bias import ContentBiasObjectiveScenarioBuilder, ObjectiveSubCategory
from trusttest.targets.http import HttpTarget, PayloadConfig

target = HttpTarget(
    url="https://your-model-endpoint.com/chat",
    headers={"Content-Type": "application/json"},
    payload_config=PayloadConfig(
        format={
            "messages": [
                {"role": "user", "content": "{{ test }}"}
            ]
        },
        message_regex="{{ test }}",
    ),
)

builder = ContentBiasObjectiveScenarioBuilder(target=target, num_test_cases=15)
scenario = builder.get_scenario(ObjectiveSubCategory.FRAMING_BIAS)

test_set = scenario.probe.get_test_set()
results = scenario.eval.evaluate(test_set)
results.display_summary()
```

### Testing for Stereotypical Bias

```python theme={null}
from trusttest.catalog.content_bias import ContentBiasDatasetScenarioBuilder, DatasetSubCategory

builder = ContentBiasDatasetScenarioBuilder(target=target, num_test_cases=50)
scenario = builder.get_scenario(DatasetSubCategory.GENDER_BIAS)

test_set = scenario.probe.get_test_set()
results = scenario.eval.evaluate(test_set)
results.display_summary()
```

## When to Use

Use content bias testing when you need to:

* Ensure fair treatment across demographic groups
* Validate reasoning consistency
* Meet responsible AI requirements
* Audit models for discriminatory behavior
* Prepare for deployment in sensitive applications