Module 7: Bias & Fairness in AI Outputs - DRAFT

Why This Matters

AI models learn from large datasets that reflect historical and systemic biases. Without deliberate review, AI outputs can reproduce stereotypes, exclude perspectives, and produce inequitable outcomes—especially in client deliverables that influence decisions about people.

You are accountable for catching and correcting biased AI output before it leaves RBA, Inc.

Key Risks

Demographic stereotyping AI may default to assumptions about gender, race, age, disability, or socioeconomic status—for example, associating leadership roles with one gender or technical roles with one ethnic group.
Underrepresentation Outputs may omit or minimize perspectives from marginalized communities, producing recommendations that work for a “default” audience but fail others.
Homogenization AI tends to flatten diverse viewpoints into a single consensus perspective, erasing nuance and reinforcing dominant cultural norms.
Accessibility gaps AI-generated content may not be usable by people with disabilities—missing alt text for images, overly complex language, or formats incompatible with assistive technologies.

What to Watch For

These are practical signals that AI output may carry bias:

Gendered or racialized language in job descriptions, personas, or user stories.
Cultural assumptions that treat one group’s norms as universal (e.g., Western-centric examples, English-only references).
Age bias in recommendations—assuming technology comfort by generation, or defaulting to a narrow age range in personas.
Ableist framing that treats disability as deficit rather than difference, or ignores accessibility needs entirely.
Missing perspectives when AI summarizes stakeholder input or generates options—ask yourself whose voice is absent.

Your Review Checklist

Before using AI-generated content in deliverables or decisions:

I checked outputs for demographic assumptions and stereotyping.
I reviewed whether recommendations apply equitably across diverse groups.
I checked client-facing content for inclusive language and balanced representation.
I considered accessibility: plain language, alt text where needed, and compatibility with assistive technologies.
I questioned AI-generated personas, examples, or scenarios for hidden defaults.

When to Escalate

Report to your Manager or the AI Governance & Risk Management Lead when:

AI outputs could lead to discriminatory outcomes in hiring, resource allocation, or service delivery.
You notice recurring patterns of bias in a specific tool or model.
A client deliverable touches protected classes or regulated decisions (employment, lending, housing, healthcare).
You are unsure whether output reflects legitimate analysis or embedded bias.

Use the incident process for cases involving actual or suspected harm.