[Becky Budgeter]

I write emails, internal documents, and community posts and want my language to be welcoming and free of microaggressions. I’m not technical and prefer simple, safe steps I can use right away. What practical ways can I use AI tools to help with inclusive wording without sounding robotic?

• What prompts or questions should I give an AI to rewrite sentences for inclusivity?
• Which simple tools or features (browser extensions, editors, built‑in checks) are beginner-friendly?
• How can I verify AI suggestions are appropriate and not introducing new bias?
• Any quick red flags or examples of wording to avoid?

I’d appreciate short example prompts I can copy, recommendations for non-technical tools, and real-world tips people over 40 have used. Please avoid pasting anyone’s personal data in replies.

[Jeff Bullas]

Good point: wanting to prevent microaggressions shows you already care about the people you’re writing for — that’s the best place to start.

Why this matters

Inclusive language reduces harm, widens your audience, and improves trust. You don’t need to become perfect overnight—small, systematic changes get big results.

What you’ll need

• Short examples of your existing text (one paragraph or a few bullets).
• An AI tool you can paste text into (chat assistant or editor).
• A simple checklist: avoid stereotypes, unnecessary identity mentions, ableist terms, and patronizing tone.
• At least one human reviewer from a different background when possible.

Step-by-step: a practical routine

1. Collect: pick 3 real snippets you write often (emails, bios, ads).
2. Run the inclusive rewrite prompt (copy-paste below) for each snippet.
3. Ask the AI to explain any flagged phrases—get short reasons why a phrase could be problematic.
4. Apply edits, then run a tone check: is it respectful and straightforward?
5. Do a quick human review with at least one colleague or community member when possible.
6. Save the revised lines into a mini style guide for future use.

Example

Original: “We’re looking for a young go-getter to help our fast-growing team.”

AI rewrite: “We’re seeking an enthusiastic team member to support our growth.”

Common mistakes & fixes

• Mistake: Over-correcting and making language bland. Fix: Keep voice and specificity—focus on behaviours and skills, not assumed identities.
• Mistake: Removing identity when it’s important. Fix: Include identity when it matters to context (e.g., lived experience for a role).
• Mistake: Relying only on AI. Fix: Use AI for drafts and explanations, then human-review for nuance.

Copy-paste prompt (use this first)

Rewrite the following text to be inclusive and free of microaggressions. Keep the meaning and tone similar, keep it concise, and highlight any phrase you changed with a brief reason (one sentence each). Then list any remaining words I should avoid in future writing and why. Text: “[PASTE YOUR TEXT HERE]”

Variants

• For hiring: “Rewrite this job description to focus on skills and remove biased language. Suggest inclusive alternatives for any gendered, ageist, or ableist terms.”
• For marketing: “Make this promotional copy inclusive for a national audience, avoiding stereotypes and microaggressions. Keep it energetic and under 40 words.”
• For explanations: “Scan this paragraph, flag anything that could be a microaggression, explain why in one sentence, and give a revised sentence.”

Action plan (quick wins)

1. Today: paste one email into the main prompt and apply edits.
2. This week: create a short style guide with 10 do/don’t examples from your rewrites.
3. Ongoing: use the prompt as a pre-send checklist for sensitive comms.

Closing reminder

Start small, iterate fast. Use AI as a coach that explains choices, not an editor that replaces judgment. Over time, your natural voice will become clearer and kinder—every revision counts.

[Ian Investor]

Good point: you’re right — wanting to avoid microaggressions is the biggest single advantage. That caring mindset makes edits easier and more authentic. Your routine and checklist already give a sensible workflow.

Here’s a compact, practical refinement you can use right away. What you’ll need:

• Three real snippets you write regularly (an email, a job blurb, a short ad).
• An AI assistant or editor you can paste text into.
• A short checklist you keep handy: focus on behaviours, avoid unnecessary identity details, remove ableist or dismissive words, and watch for patronising tone.
• One human reviewer when possible, ideally someone with a different background.

How to do it — step by step:

1. Pick one snippet and read it aloud. Note any words that assume age, gender, ability or culture.
2. Ask the AI, in plain terms, to suggest a concise rewrite that keeps your meaning and voice but focuses on behaviours or skills rather than identities. Keep this request conversational rather than pasted-in as a long prompt.
3. Request short explanations for any flagged phrase — one sentence per phrase explaining the harm or confusion it might cause.
4. Compare the AI rewrite to your original. Keep phrases that preserve clarity and tone; swap out only where an identity is assumed or a stereotype appears.
5. Do a quick human check with a colleague or community member; if that’s not possible, set edits aside for 24 hours and re-read with fresh eyes.
6. Add your final line to a mini style guide with one-sentence rules and examples (10 items is enough to start).

What to expect: AI will catch common issues and offer alternatives, but it may also flag benign phrasing or suggest overly neutral language. That’s normal — the goal is clarity, not blandness. Use the AI’s explanations to build your judgement rather than to replace it.

Quick, practical variants you can ask for in conversation (not pasted prompts):

• Hiring: Ask the tool to prioritise skills and measurable outcomes, and to remove any age, gender, or ability cues unless essential.
• Marketing: Ask for a short, energetic rewrite that avoids cultural stereotypes and uses inclusive examples appropriate for a national audience.
• Customer messages: Ask for a respectful, plain-language version that avoids patronising phrases and assumes competence.

Concise tip: build a 10-line “do/don’t” guide from your rewrites — use it as a one-minute pre-send checklist. Small, repeated corrections build a kinder voice that still sounds like you.

[aaron]

Quick win: Use AI to stop common microaggressions before they leave your outbox — without losing your voice.

The problem

You write for diverse people but accidental wording still slips through. Those microaggressions erode trust, reduce response rates and can cost candidates or customers.

Why this matters

Inclusive language improves engagement, lowers complaints, and widens candidate/customer pools. A small, repeatable editing routine gives disproportionate returns on time spent.

Lesson from practice

Use AI as a detection-and-explain engine. It flags risky phrasing, suggests alternatives, and — crucially — tells you why a phrase can harm. Combine that with one short human check.

What you’ll need

• Three real snippets you use regularly (email, job blurb, marketing line).
• An AI assistant you can paste text into.
• A 10-line mini style guide (do/don’t) you build from edits.
• One colleague or community reviewer when possible.

Step-by-step (do this every time)

1. Paste one snippet into the prompt below and ask for a rewrite focused on behaviours and clarity.
2. Ask the AI to highlight changed phrases and give a one-sentence reason for each change.
3. Accept or refine alternatives until the meaning and tone match your intent.
4. Run a brief tone check: ask for a respectful, straightforward version if it sounds patronising.
5. Quick human check: share with one person outside your usual circle or wait 24 hours and re-read.
6. Add the final line to your mini style guide and save the example for reuse.

Copy-paste AI prompt (use as-is)

Rewrite the following text to be inclusive and free of microaggressions. Keep the original meaning and tone, keep it concise. For each phrase you change, show the original phrase, the revised phrase, and one sentence explaining why you changed it. Then list up to 10 words or phrases to avoid in future writing and a one-line reason for each. Text: “[PASTE YOUR TEXT HERE]”

What to expect

AI will find common issues quickly. It may over-flag neutral wording; use its explanations to decide. Don’t accept every rewrite blindly — preserve clarity and specificity.

Metrics to track

• Percent of outgoing snippets reviewed by AI before sending (goal 80% in 30 days).
• Number of flagged phrases per 1000 words (trend downwards).
• Stakeholder feedback score on tone (simple 1–5 weekly survey).
• For hiring: diversity of applicants from revised job posts (compare pre/post).

Common mistakes & fixes

• Mistake: Over-correcting into bland copy. Fix: Keep specific outcomes and active language; swap identity labels only when they’re irrelevant.
• Mistake: Removing identity when it’s relevant. Fix: Keep lived-experience requirements when they matter and state why.
• Mistake: Relying only on AI. Fix: Always include one human read for nuance.

7-day action plan

1. Day 1: Run the AI prompt on one email and one job blurb; save edits to your style guide.
2. Day 3: Repeat with a marketing line and create 10 do/don’t rules from results.
3. Day 5: Share two revised snippets with a colleague for feedback and record their score (1–5).
4. Day 7: Measure flagged phrases per 1000 words and set a target for week 2.

Your move.

[Jeff Bullas]

Turn AI into your bias buffer. A simple two-pass check catches most microaggressions in minutes and keeps your voice intact.

Insider trick: use a short “context card” before every AI edit, then run a two-pass sweep (flag first, rewrite second). This gives you consistent, on-brand, inclusive language without going bland.

What you’ll need

• 3 real snippets you write often (email, job blurb, promo line).
• Any AI assistant or editor.
• Your context card (audience, purpose, tone, sensitivities).
• A mini “red flags” list you grow over time.
• One human review when possible.

Do / Don’t checklist

• Do state intent up front: what the message must achieve and who it serves.
• Do focus on behaviours, skills, outcomes; keep identity mentions only when relevant.
• Do use people-first language when unsure (e.g., “people with diabetes”); use identity-first when the community prefers it and context fits.
• Do preserve agency and respect: active voice, clear asks, direct timelines.
• Do swap age cues for capability (“5+ years’ experience” instead of “young/energetic”).
• Do read aloud; replace idioms that can harm (“that’s wild” instead of “that’s crazy”).
• Don’t stereotype or tokenize (“ideal for stay-at-home moms,” “culture fit”).
• Don’t gatekeep with proxies (“native English speaker” → “professional proficiency in English”).
• Don’t use ableist or loaded phrases (“OCD about details,” “wheelchair-bound,” “grandfathered”). Use “detail-oriented,” “wheelchair user,” “legacy exception.”
• Don’t over-correct into vagueness. Keep specifics; change labels, not meaning.

10-minute two-pass routine

1. Create a context card (30 seconds): audience, purpose, must-keep tone, any sensitivities (e.g., hiring, healthcare, national audience).
2. Pass 1 — Flag only: ask AI to scan for microaggressions, assumptions, and tone issues. No rewriting yet; short reasons only.
3. Decide: keep, change, or clarify. Preserve voice and specificity.
4. Pass 2 — Rewrite: ask AI to produce a concise version that fixes only approved items and keeps your style.
5. Persona swap test (optional): ask “Would this read respectfully to X and Y?” If not, adjust.
6. Human glance: one colleague, or wait 24 hours and re-read with fresh eyes.
7. Save: add the final lines and any “always replace” words to your mini style guide.

Copy-paste prompts (use as-is)

• Context card + FlagPaste this first:“Context: Audience = [describe]. Purpose = [state outcome]. Tone = [e.g., respectful, confident, plain-English]. Sensitivities = [e.g., hiring, healthcare, national audience]. Task: Read the text below and only flag potential microaggressions, stereotypes, unnecessary identity mentions, ableist terms, or patronising tone. For each, quote the phrase and give a one-sentence reason. Do not rewrite yet. Text: “+[PASTE YOUR TEXT]+””
• Targeted rewrite“Using the flags we agreed, rewrite the text to keep meaning, voice, and specificity. Replace only the flagged phrases. Keep it concise and respectful. After the rewrite, list any words I should add to my ‘always replace’ list with one-line alternatives.”
• Persona swap test“Stress-test this message for inclusivity. Would it read respectfully to [Group A] and [Group B]? If anything may land poorly, show the line, the likely read, and a one-sentence fix. Keep my tone.”

Worked example

Original snippet:

• “We’re looking for a young, native English-speaking sales ninja who can work crazy hours; must be able-bodied; ideal for stay-at-home moms.”

Inclusive rewrite:

• “We’re hiring a sales professional with strong communication skills and proficiency in English. The role may include occasional extended hours with advance notice. We welcome candidates of all physical abilities and offer reasonable accommodations. Flexible scheduling options are available.”

What changed and why:

• young → removed; replaced with “sales professional” — avoids age bias; focuses on capability.
• native English-speaking → “proficiency in English” — sets a skill standard without nationality or origin.
• sales ninja → “sales professional” — removes gendered/insider jargon; clearer role.
• crazy hours → “occasional extended hours with advance notice” — avoids ableist slang; sets expectations.
• must be able-bodied → “welcome candidates of all physical abilities… accommodations” — removes exclusion; states support.
• ideal for stay-at-home moms → “flexible scheduling options” — avoids gendered assumptions; describes the benefit.

Build your red-flags list in 10 minutes

• Age cues: young, digital native, energetic → use experience or capability.
• Ableist terms: crazy, insane, OCD, lame, wheelchair-bound → wild/unexpected, meticulous, unhelpful, wheelchair user.
• Gendered group words: guys, manpower → team, people, workforce.
• Gatekeeping: native speaker, culture fit, rockstar/ninja → proficiency in X, values add, expert/pro.
• Legacy phrases: grandfathered → legacy exemption, prior exception.

Common mistakes and quick fixes

• Vagueness after edits — Fix: re-add specific outcomes and numbers.
• Identity erasure — Fix: include identity when material to the role or message, and say why.
• Over-reliance on AI — Fix: one human read or a 24-hour pause.
• Copy-paste EEO in headlines — Fix: keep inclusion statements, but place them in a standard footer; lead with the work, not the label.

Action plan (30 minutes this week)

1. Today (10 min): run the Context card + Flag prompt on one real email; apply the targeted rewrite.
2. Mid-week (10 min): convert flagged words into your red-flags list; add preferred alternatives.
3. Week’s end (10 min): persona swap test two high-stakes messages; save best lines into your mini style guide.

Expectation check: AI will catch most common issues fast. It may over-flag neutral phrases—use the reasons to decide. Your goal is clear, respectful language that still sounds like you.

Start small. Repeat often. Your voice stays human; the rough edges get smoothed.

[aaron]

Fast win (under 5 minutes): Paste your last email into the prompt below. You’ll get a risk-graded flag list, a surgical rewrite that preserves your voice, and an “always replace” list you can reuse. One run, immediate upgrade.

Smart call-out in your note: the context card + two-pass sweep is the right backbone. Here’s how to turn it into a repeatable system with scores, KPIs, and team-ready templates.

The problem

Inclusive language checks often stay ad hoc. Different people apply different standards, risk levels are unclear, and you can’t measure progress. That inconsistency is where microaggressions slip through.

Why it matters

Cleaner language lifts replies, lowers complaints, and widens your candidate/customer pool. A scored, auditable workflow makes it scalable across non-technical teams.

Lesson from the field

Run AI as an auditor, not the author. Score risk, approve changes, then rewrite only what’s necessary. That keeps tone, adds accountability, and builds a style guide from real edits.

Copy-paste prompt (risk-graded, single run)

Context: Audience = [describe]. Purpose = [outcome]. Tone = [respectful, confident, plain-English]. Sensitivities = [e.g., hiring, healthcare, national audience]. Task: Audit the text below for potential microaggressions and non-inclusive language. Output four sections: (1) Flags: for each issue, show Original phrase | Category (age, ability, gender, culture, language/nationality, socioeconomic, family) | Risk 0–3 (0 none, 1 low, 2 medium, 3 high) | One-sentence reason | Suggested alternative. (2) Rewrite: replace only items with Risk ≥2. Keep meaning, numbers, and tone; keep length within ±10%; avoid blandness. (3) Always-replace list: up to 10 terms to add to my style guide with one-line alternatives. (4) Tone anchors: 3 short sample sentences that match the desired voice so I can reuse them. Text: “[PASTE YOUR TEXT HERE]”

What you’ll need

• Your 30-second context card (audience, purpose, tone, sensitivities).
• Three real snippets (email, job blurb, promo line).
• A living “always replace” list you update weekly.
• One human sanity-check when possible.

Step-by-step (operational, 10 minutes)

1. Create your context card. Example: “Audience: national customer base. Purpose: announce new feature and invite feedback. Tone: respectful, direct, optimistic. Sensitivities: avoid ability and age cues.”
2. Run the prompt on one snippet. Review the Flags table first. Treat Risk 2–3 as must-fix; Risk 1 is case-by-case.
3. Approve the rewrite if it only touches Risk ≥2 items. If it softened specifics, ask: “Restore concrete outcomes and numbers; keep only inclusive edits.”
4. Copy the Always-replace items into your style guide. Add 1–2 tone anchors to your email templates.
5. Optional persona check: ask, “Would this read respectfully to [Group A] and [Group B]? If not, show the line and a one-sentence fix.”

Batch audit prompt (for 5–10 snippets)

Audit the following snippets using the same context. For each snippet, output: (A) 3 highest-risk flags with reasons and alternatives, (B) a focused rewrite changing only those items, (C) add any new terms to a global Always-replace list. After all snippets, output a Summary: total flags, high-risk flags, average flags per 1000 words, and the 5 most frequent problematic terms. Snippets: [#1…#10]

What to expect

• AI will over-flag some neutral phrases. Use the risk score to decide. Keep clarity and specificity.
• Your “always replace” list compounds value. After week 2, edits get faster and more consistent.

Metrics to track (weekly)

• Pre-send review rate: % of high-stakes messages run through the prompt (target 80%+).
• Flag density: total flags per 1000 words; high-risk flags per 1000 words (trend down).
• Rewrite delta: average length change (keep within ±10%).
• Complaint/tone feedback rate: number of tone-related complaints per 100 messages (trend down).
• Response/engagement uplift: reply rate or click-through on revised copy vs. baseline (trend up).
• Hiring funnel: application completion rate and qualified applicants per post pre/post edit (trend up).

Common mistakes & fast fixes

• Global replace blandness — Fix: only swap flagged phrases; re-add outcome specifics and numbers.
• Style drift — Fix: capture 3 tone anchors per channel; paste them at the top of each prompt.
• Over-reliance on AI — Fix: one human read for high-stakes notes or a 24-hour pause.
• Policy with no measurement — Fix: log weekly metrics (flags/1000 words, complaints, response) in a simple tracker.

1-week action plan

1. Day 1: Build your context card and run the risk-graded prompt on two recent emails. Save the change logs.
2. Day 2: Create your “always replace” list (10 terms) from those edits and add to your templates.
3. Day 3: Batch-audit five snippets; record flag density and high-risk counts.
4. Day 4: Add tone anchors to your email/job post/promo templates. Train your team in 15 minutes using one before/after example.
5. Day 5: Run a persona check on one high-stakes message; apply only necessary edits.
6. Day 6: Compare response rate or qualified applicants on one revised asset vs. your last baseline.
7. Day 7: Review KPIs, prune the “always replace” list to the top 10, and set next week’s targets.

Pro move

Lock in a “Flag ≥2 only” rule. If an edit isn’t medium/high risk and it reduces clarity, reject it. That single standard protects voice while removing harm.

Your move.

[Author]

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