Content
Introduction
This technique aims to promote the use of language that includes and respects people of all backgrounds, identities, and abilities. AI-generated text can inadvertently exclude or marginalize groups through insensitive wording. By guiding the AI to use inclusive terms and avoid stereotypes, you ensure equitable, welcoming communication for diverse audiences.
Why It's Important
- Inclusivity: Language shapes perceptions. Inclusive wording validates a broader range of identities, helping everyone feel recognized and respected.
- Avoiding Harm: Biased language can perpetuate stereotypes and discriminatory narratives. Thoughtful phrasing helps dismantle rather than reinforce such patterns.
- Building Trust: When AI consistently produces inclusive language, users see a commitment to equity, boosting confidence in the technology’s outputs.
- Global Audience: Cultural and linguistic nuances vary worldwide. Encouraging inclusive language ensures relevance and sensitivity across different regions.
How to Use
In your prompts, explicitly request the AI to use inclusive, bias-free language. For instance: 'Draft a product description that is welcoming to people of all ages, genders, and cultural backgrounds—avoid assuming specific attributes.' Encourage thorough review of the final text to detect any subtle biases (e.g., referencing only one demographic by default).
Default Prompt: Write a job description for a software developer position.
Updated Prompt: Write a job description for a software developer position. Ensure that the language used is inclusive and respectful to all individuals, avoiding terms or phrases that may marginalize or exclude specific groups. Aim for neutrality in pronouns and job qualifications, and highlight the company’s commitment to diversity.
Key Considerations
- Awareness: Keep current with evolving terminology, particularly around gender, disability, and cultural identity. Language that was acceptable in the past may now be outdated or insensitive.
- Context: Inclusive language varies by region or industry. In multinational contexts, clarify which terms are culturally neutral or widely acceptable.
- Continuous Improvement: AI models learn from existing data, which can contain biased language. Periodically check for subtle shifts in generated text, and retrain or refine instructions as needed.
- Feedback Loop: Encourage readers or users to report any language they find exclusive or offensive. Human feedback remains crucial for continuous refinement of inclusive language practices.
