NeuralTrust | Platform for Agent Security.

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

Dataset vs Prompt probes

This category is the only one with two catalog builders:

Builder	Probe family	Default evaluator
`ContentBiasObjectiveScenarioBuilder`	LLM-generated `*Probe` (cognitive)	`BiasComparisonEvaluator` + `BiasComparisonContext`
`ContentBiasDatasetScenarioBuilder`	`*DatasetProbe` (stereotypical, YAML under `datasets/static_objectives/`)	`TrueFalseEvaluator`

Each stereotypical subcategory also has a *PromptProbe (for example GenderStereotypicalBiasPromptProbe) you can instantiate yourself. Dataset probes inherit StaticDatasetProbe params: sampling, use_jailbreaks, max_jailbreak_per_objective, translate_into_language. See StaticDatasetProbe. Catalog scenarios attach framework tags (EU AI Act Art. 10, OWASP AITG-RES-02, MITRE ATLAS AML.T0020, ISO/IEC 42001). builder.get_scenario() returns a Scenario (probe + eval); run_scenarios() is the client/remote helper, not a builder method.

Usage Example

Testing for Cognitive Bias

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

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

Getting Started

Core Concepts

Connect your app

Create tests

Evaluate results

Content Bias

Purpose

Bias Categories

Cognitive Biases

Stereotypical Biases

How It Works

Cognitive Bias Testing

Stereotypical Bias Testing

Dataset vs Prompt probes

Usage Example

Testing for Cognitive Bias

Testing for Stereotypical Bias

When to Use

​Purpose

​Bias Categories

​Cognitive Biases

​Stereotypical Biases

​How It Works

​Cognitive Bias Testing

​Stereotypical Bias Testing

​Dataset vs Prompt probes

​Usage Example

​Testing for Cognitive Bias

​Testing for Stereotypical Bias

​When to Use

Purpose

Bias Categories

Cognitive Biases

Stereotypical Biases

How It Works

Cognitive Bias Testing

Stereotypical Bias Testing

Dataset vs Prompt probes

Usage Example

Testing for Cognitive Bias

Testing for Stereotypical Bias

When to Use