AI Moderation and Community Guidelines

The Moderation Challenge

🔄 Quick Recall: In the previous lesson, you built a content and engagement system — with batch content calendars, AI-generated discussion prompts, and weekly rituals. Now let’s address the other side of community management: protecting the culture you’re building from the behaviors that can destroy it overnight.

A single toxic incident can undo months of community building. One unaddressed troll, one public meltdown, one comment thread that spirals into personal attacks — and your best members quietly leave, often without explanation.

AI moderation helps you catch problems at scale. But moderation is where AI’s limitations matter most: context, sarcasm, cultural nuance, and the difference between passionate disagreement and personal attack require human judgment.

Building Your Moderation System

Design a hybrid AI-human moderation system for my community.

Platform: [Discord/Slack/Circle/other]
Community size: [X] members
Average posts per day: approximately [X]
Current moderation: [describe — just me? Team? Volunteer mods?]

Build a tiered moderation system:

TIER 1 — AUTO-ACTION (AI handles immediately):
- Spam detection (links, repeated messages, suspicious accounts)
- Prohibited content (slurs, explicit content, doxxing)
- Auto-remove and log for review

TIER 2 — FLAGGED FOR REVIEW (AI flags, human decides):
- Potentially toxic language (confidence 60-90%)
- Heated discussions approaching personal attacks
- Reports from community members
- Queue with context and AI's assessment

TIER 3 — HUMAN MONITORING (no AI trigger):
- Subtle tone shifts, persistent negativity
- Clique formation, member exclusion
- Burnout signals from power users
- Cultural shifts detected through sentiment trends

For each tier, specify:
- Tools or bot configurations needed
- Response time target
- Who handles it (me, mod team, AI)
- Escalation criteria

Moderation Tiering in Practice

Content	AI Confidence	Action	Response Time
Spam link from new account	99%	Auto-remove	Instant
Racial slur	98%	Auto-remove + ban	Instant
“Your opinion is garbage”	75%	Flag for human review	<2 hours
Passive-aggressive comment	40%	Monitor, no action	Observe pattern
Heated but respectful debate	20%	No flag	None needed

✅ Quick Check: Why is the 60-90% confidence range the most important for community managers to personally review? Because this “gray zone” contains the posts that determine your community’s actual culture. Below 60%, the content is almost certainly fine. Above 90%, it’s almost certainly a violation. But the gray zone contains sarcasm mistaken for hostility, passionate disagreement that pushes boundaries, cultural expressions AI doesn’t understand, and legitimate criticism delivered harshly. How you moderate this zone defines what kind of community you’re building.

Writing Effective Guidelines

Write community guidelines for my community.

Community purpose: [from Lesson 2]
Community culture: [describe the vibe you want — supportive,
professional, casual, competitive, etc.]
Platform: [Discord/Slack/Circle/other]

Structure:

PART 1 — OUR VALUES (what we celebrate):
Write 3-5 positive behavioral values as "We..." statements
Examples: "We share failures openly — they teach more than successes"
"We ask questions before making assumptions about intent"

PART 2 — COMMUNITY STANDARDS (what we expect):
5-7 specific behavioral expectations with concrete examples
of both acceptable and unacceptable behavior.
No vague statements like "be respectful" — every rule needs
a specific, visible example.

PART 3 — ENFORCEMENT (what happens when standards are violated):
Clear escalation path with specific consequences at each level.
Include: who makes moderation decisions, how to appeal,
and what constitutes immediate removal.

Tone: warm but clear. Not corporate legalese, not casual handwave.

Enforcement Escalation Path

Help me design a clear enforcement escalation path.

My community has the following violation categories:
1. MINOR: Off-topic posts, mild self-promotion, low-quality posts
2. MODERATE: Personal attacks, persistent negativity, harassment-adjacent
3. SEVERE: Hate speech, doxxing, threats, illegal content

Design an escalation for each category:

MINOR violations:
Step 1: [what happens]
Step 2: [if repeated within X time]
Step 3: [if persistent]

MODERATE violations:
Step 1: [immediate action]
Step 2: [follow-up]
Step 3: [escalation]

SEVERE violations:
Action: [immediate]

Include: who is notified, what gets documented, how the member
is informed, and whether the action is public or private.

Severity	First Offense	Second Offense	Third Offense
Minor	Friendly reminder (public or private)	Private warning with reference to guidelines	24-hour mute
Moderate	Private warning + content removed	7-day suspension + formal notice	Permanent ban
Severe	Immediate ban + content removed	N/A	N/A

Training AI on Your Community’s Norms

Help me create a community-specific moderation guide for AI tools.

In my community, the following are ACCEPTABLE even though
generic AI might flag them:
- [Industry jargon that sounds aggressive out of context]
- [Friendly teasing between regular members]
- [Passionate disagreement about technical topics]
- [Specific terms or phrases common in our community]

The following are NOT ACCEPTABLE even though they might seem mild:
- [Subtle exclusionary language specific to our context]
- [Backhanded compliments common in our space]
- [Specific behaviors that undermine our community culture]

Create a "context document" I can reference when configuring
moderation tools — a cheat sheet of what's uniquely OK and
uniquely not-OK in this specific community.

✅ Quick Check: Why does every community need a custom moderation context, not just generic rules? Because community culture is specific. A coding community where members say “your code is terrible, here’s why” is direct and helpful. The same phrase in a creative writing community is destructive. A gaming community where trash talk is bonding would be flagged as toxic by any generic moderator. Your context document teaches AI (and new moderators) the difference between your community’s normal and genuinely problematic behavior.

Key Takeaways

Hybrid AI-human moderation works in three tiers: auto-action for clear violations (spam, slurs), human review for gray-zone content (60-90% confidence), and human monitoring for subtle culture shifts
False positives from AI moderation frustrate members — tune your system with community-specific allowlists and confidence thresholds
Guidelines should lead with values (what you celebrate) before rules (what you prohibit) — members who resonate with values self-moderate
Every rule needs a concrete example of both acceptable and unacceptable behavior — “be respectful” is unenforceable
Private outreach to struggling members resolves 80%+ of tone issues — public enforcement should be the last resort, not the first

Up Next: You’ll learn to handle the hardest community management challenges — active conflicts between members, crisis situations, and reputation threats — with AI-assisted response frameworks.