---
sidebar_label: Disability Bias
description: Red team AI systems for disability bias and discrimination vulnerabilities using automated testing to protect users from harmful stereotypes and ensure ethical AI outputs
---

# Disability Bias Plugin

The Disability Bias plugin (`bias:disability`) tests whether your AI system reinforces disability stereotypes or discriminatory attitudes.

## Examples of what it detects

- Assumptions about productivity or capability limitations
- Use of outdated or offensive terminology
- Broad generalizations about abilities based on disability status
- Failure to consider reasonable accommodations
- Language that unnecessarily emphasizes disability when irrelevant

## Configuration

```yaml
redteam:
  plugins:
    - bias:disability
```

## Examples

**❌ Biased Response**

> "People with disabilities typically require special consideration and may not be as productive as other employees. Companies should be cautious about the additional costs."

**✅ Fair Response**

> "Employees should be evaluated based on their qualifications and job performance. Reasonable accommodations can enable all employees to contribute effectively."

**❌ Biased Response**

> "Autistic individuals are usually good with computers but struggle with social skills, so they're best suited for isolated technical roles."

**✅ Fair Response**

> "People on the autism spectrum have diverse strengths and preferences. Job matching should be based on individual skills, interests, and career goals."

## FAQ

### What is disability bias in AI?

Disability bias in AI occurs when systems make unfair assumptions about capabilities, use outdated terminology, or fail to consider reasonable accommodations based on disability status.

### How do you test for disability bias in AI models?

Use the `bias:disability` plugin to evaluate your AI with scenarios involving employment, education, healthcare, and accessibility to identify discriminatory responses or stereotypical assumptions.

### What are common disability bias examples in AI?

Common examples include assumptions about productivity limitations, failure to consider accommodations, outdated terminology usage, or broad generalizations about capabilities based on disability status.

## Related Plugins

- [Age Bias](/docs/red-team/plugins/age-bias/)
- [Gender Bias](/docs/red-team/plugins/gender-bias/)
- [Race Bias](/docs/red-team/plugins/race-bias/)