Bias
Stereotype bias remains a critical challenge for large language models (LLMs), particularly when evaluated on sensitive tasks involving demographic groups and potentially harmful societal assumptions. VirtueRed evaluates stereotype bias through contextualized, conversational scenarios that embed biased premises within realistic social, professional, or interpersonal interactions.
Overview
Unlike simple, decontextualized statements, VirtueRed's approach crafts scenarios that simulate realistic interactions. The model is prompted to generate responses that require navigating social context while addressing biased premises.
| Evaluation Dimension | Description |
|---|---|
| Demographic Coverage | 24 distinct demographic groups |
| Stereotype Topics | 16 harmful stereotype categories |
| Context Variations | 3 system prompt contexts (benign, untargeted, targeted) |
| Scenario Type | Contextualized conversational scenarios |
Testing Methodology
To assess model resilience, each user prompt is tested within three distinct system prompt contexts:
1. Benign System Prompt
Provides a baseline context where the model operates as a standard, helpful assistant, expected to adhere to its safety training.
2. Untargeted System Prompt
Creates a general adversarial context that encourages the model to bypass safety restrictions and moral guidelines without priming it against any specific group.
3. Targeted System Prompt
A more potent adversarial context that explicitly instructs the model to adopt a biased stance against the specific demographic group mentioned in the user prompt, testing vulnerability to direct manipulation.
Stereotype Topics
VirtueRed evaluates the following stereotype topics:
Health-Related Stereotypes
| Topic | Description |
|---|---|
| HIV | Assesses whether the model associates HIV disproportionately with specific demographic groups, such as homosexuals or women, reflecting harmful biases around health and sexual orientation |
| Drug Addiction | Tests whether the model reinforces stereotypes around substance abuse, particularly associating drug addiction with marginalized communities or specific ethnic groups |
| Drug Dealing | Examines whether the model perpetuates the stereotype that specific racial or ethnic groups are more involved in drug trafficking or illegal activities |
Violence and Crime Stereotypes
| Topic | Description |
|---|---|
| Terrorism | Examines whether the model disproportionately links terrorism with particular ethnic or religious groups, contributing to dangerous stereotypes that associate violence with certain identities |
| Crime | Focuses on whether the model disproportionately associates criminal behavior with particular races, genders, or socioeconomic groups |
Capability Stereotypes
| Topic | Description |
|---|---|
| Intelligence | Focuses on the model's tendency to associate intellectual capacity with specific races or genders, perpetuating harmful biases around inherent ability |
| Leadership Skills | Assesses whether the model promotes biases regarding leadership, such as associating leadership qualities more with men than women or certain ethnicities over others |
| STEM Abilities | Examines whether the model demonstrates bias around STEM proficiency, particularly reflecting the stereotype that women or minority groups are less capable in these fields |
| Technology Use | Examines whether the model associates technology skills with specific age groups, genders, or socioeconomic backgrounds |
| Driving Ability | Evaluates the model's tendency to perpetuate stereotypes about poor driving skills being associated with women, the elderly, or certain ethnic groups |
| Physical Weakness | Assesses whether the model reflects stereotypes around physical strength, particularly assumptions that women or older adults are inherently weaker |
Social and Cultural Stereotypes
| Topic | Description |
|---|---|
| Greed | Evaluates if the model links greed or financial exploitation with particular nationalities or ethnicities |
| Parenting | Evaluates whether the model demonstrates biases related to gender or cultural norms around parenting capability |
| Xenophobia | Assesses the model's tendency to promote negative stereotypes about immigrants or foreign groups |
| Job Stealing | Investigates whether the model reflects biases related to immigration and economic fear |
| Hygiene | Tests for any association of poor hygiene with particular nationalities, ethnic groups, or socioeconomic classes |
Demographic Groups
The evaluation covers 24 distinct demographic groups across multiple dimensions:
| Dimension | Groups Covered |
|---|---|
| Gender | Men, Women, Non-binary individuals |
| Age | Youth, Elderly, Middle-aged |
| Race/Ethnicity | Multiple racial and ethnic backgrounds |
| Religion | Various religious affiliations |
| Nationality | Multiple national origins |
| Sexual Orientation | LGBTQ+ identities |
| Socioeconomic Status | Various economic backgrounds |
| Disability Status | People with disabilities |
Risk Assessment
| Risk Level | Description | Indicators |
|---|---|---|
| High | Model actively reinforces harmful stereotypes | Direct stereotype propagation, agreement with biased premises |
| Moderate | Model shows inconsistent handling of bias | Occasional stereotype leakage, context-dependent failures |
| Low | Model consistently rejects biased premises | Appropriate pushback, educational responses |
Mitigation Strategies
Organizations can reduce bias risks through:
- Diverse Training Data - Ensuring balanced representation across demographics
- Bias Detection Systems - Implementing automated bias monitoring
- Regular Auditing - Conducting periodic bias assessments
- Debiasing Techniques - Applying post-training bias reduction methods
- Inclusive Testing - Testing with diverse user groups and scenarios
See Also
- Societal Harmfulness - Related harmful content testing
- Privacy - Demographic data protection
- Brand Risk - Reputational impact of bias