Quick Answer
Yes. As of 2026, according to Airbnb’s community, Airbnb’s AI system handles initial review disputes quickly, filtering out violations such as retaliation or irrelevance. Often denying appeals instantly without human checks, frustrating hosts. But nowhere does Airbnb officially mention AI. They said they still rely on team reviews to assess authenticity or policy violations. Submit detailed evidence to beat the rigid bot.
Many Airbnb hosts are seeing the same pattern: they submit detailed review-removal requests with appropriate screenshots, timelines, and clear evidence. They still get denied within minutes. But why?
As I work directly with hosts on review disputes at STR Assistance, this concern comes up often. The issue isn’t just denied cases. It also directly affects the host’s listing performance (ranking, superhost status) and reputation. You might overlook one bad review, but Airbnb doesn’t.
As an Airbnb review specialist who has handled many disputes involving false claims, policy violations, and retaliatory reviews, I have observed a clear pattern. Some responses feel standardized, and some decisions come too fast to reflect the evidence submitted. That doesn’t confirm full automation, but it makes hosts question how the system works.
Let’s break it down clearly. See how review removal works, where AI may play a role, what Airbnb has actually said, and how hosts can improve their chances of a fair review. Give you straight answers based on my real cases.
How Airbnb’s Review Removal Process Works?
Airbnb doesn’t remove reviews just because they’re negative or unfair. A review is only taken down if it clearly breaks Airbnb’s official content policies. Here, hosts can’t delete reviews themselves; for that, they need to submit a removal request with proof through Airbnb support. Before giving you a verdict on whether Airbnb uses AI to decide review removal or not, it is important to understand the design of the Airbnb system. Have a look at each way that Airbnb uses:
Two-Way Review System
Airbnb uses a mutual review structure, which uses a double-blind two-way system where both the host and the guest leave reviews after a stay. They can also see each other’s honest feedback reviews until both are submitted or the review 14-day window closes. This helps stop both sides from writing revenge reviews while the review is still being written.
Blind Review Period
There’s a limited window where reviews remain hidden. Once both sides submit or the deadline passes, reviews go public simultaneously. Once the window closes, there’s no chance to place a review. After you submit feedback, you can edit it within 14 days. This design protects honesty but also makes disputes harder after publication.
Official Review Policy Guidelines
Airbnb allows reviews to be removed only under specific conditions and with evidence requirements. Disliking a review оr believing it’s unfair isn’t enough. The review must clearly violate policy and require a request tо be submitted tо the Airbnb Help Center. If a review remains, you have 30 days to post a public reply and share your side.
Categories Eligible for Removal
Reviews are typically removed if they fall into these areas:
- Hate speech or discrimination
- Irrelevant content unrelated to the stay
- Extortion or retaliation
- Conflicts of interest (like competitor reviews)
- Privacy violations
Does Airbnb Use AI to Moderate Reviews?
Airbnb hasn’t confirmed full automation, but based on real cases, it likely uses a mix of AI and human review. AI automation may help flag fake reviews or bad actor behavior at scale, while more complex cases are reviewed later by the Airbnb team. Let’s look at a few signs and the Airbnb review decision process, which make some people think automation may be involved.
Signs AI May Be Involved in Review Removal
From what I’ve seen while working on many review disputes, a few clear patterns lead people to suspect automation is involved in how Airbnb reviews are handled.
- Instant Automated Responses: Some rejections are instant, coming in just minutes. Even when you upload screenshots and detailed explanations, the reply comes back very fast. That speed feels unusual for a fully manual review.
- Standardized Rejection Messages: Many hosts receive replies that are very similar, even though their cases are different. The language reads as copied from elsewhere, and it doesn’t always address the evidence provided.
- Speed Of Decision: It usually takes time to understand complex disputes. When a decision is made too quickly, it makes people wonder whether the case was really considered carefully or was just run through a system.
- Keyword-Based Enforcement Patterns: Appeals that use clear policy terms, such as “hate speech” or “retaliation,” seem to be more effective. Cases with more detail are often rejected. This points to some filtering based on keywords or patterns.
- Policy-Based Language Detection: Some outcomes seem to depend a lot on how the appeal is written. It has a better chance of moving forward if the wording fits official policy categories. This occurs frequently in automated moderation systems.
- Limited Human Interaction: Hosts sometimes say that frontline support can’t remove reviews themselves and has to ask another team within the company to do so. The process feels less personal and more system-driven because of this separation.
- NLP-Style Moderation Patterns: Based on behaviour, it appears sentiment and wording are processed automatically, especially for issues such as harassment, fake reviews, or comments intended to retaliate.
Is AI Making Final Decisions?
Airbnb likely uses automation to screen review requests first, but final decisions are usually made by human reviewers. Clear, fact-based appeals aligned with policies have the best chance of success.
The line between automation and human review isn’t fully clear. Airbnb hasn’t officially said that review removals are fully automated. Community Managers confirm that all disputes are reviewed by Airbnb team members.
Hosts report that some decisions feel instant, suggesting that requests may first go through a system that screens or filters them. If denied quickly, the appeal may undergo another automated review before a human reviews it.
Because of this, it’s smart to structure appeals carefully. Focus оn clear facts, match your points tо Airbnb’s written policies, and avoid emotional arguments. This approach increases the chance that a real person will review your case thoughtfully.
What Hosts Are Reporting About AI Review Denials?
Many hosts reported that they feel the review moderation system is inflexible and sometimes looks automated. Quick rejections and generic replies lead some to believe the system may favor guests and doesn’t always review cases thoroughly. As an Airbnb co-host, here’s my point of view about host reporting:
Instant Review Removal Rejections
One big reason hosts question the system is the speed. Many submit a detailed removal request and get a rejection within minutes. Even when the review appears to violate policy, the decision comes quickly.
- Denials within minutes: Hosts often report near-instant rejections after sending a request. This makes the process feel rushed and confusing.
- Evidence that does not appear to be addressed: Many hosts upload screenshots, timelines, and clear policy points. But the reply still feels generic. It often doesn’t reflect the details they shared.
- Repeated appeals are rejected quickly: Some hosts try again with more proof. But the second reply often comes just as fast. When that happens, many begin to wonder whether the case was fully reviewed.
Common Host Concerns
When hosts share their experiences, a few recurring concerns arise. Those common concerns are:
- Feels automated: As I mentioned already that hosts report it feels automated. People start to wonder when they receive a rejection quickly, within the blink of an eye, after submitting. The question arises, does it come from automated systems? How did it turn down so quickly?
- Standardized responses: Replies often sound similar across different cases. Hosts compare messages and notice generic wording that doesn’t reflect their specific situation. Responses feel repetitive and template-based across various hosts and disputes. This uncertainty also affects confidence when hosts try to respond to a guest review on Airbnb, especially when the moderation process feels system-driven.
- Lack of contextual review: Some cases are complicated, but the replies feel too simple. Many hosts feel the system didn’t fully look at the details or understand the full situation.
- Perception of AI-based decisions: Due to these experiences, hosts are beginning to suspect AI involvement. The process feels unclear, like a “black box,” even though there’s no clear proof.
Airbnb’s Official Response
From what I’ve experienced, Airbnb’s message has been pretty consistent. They say review disputes are handled by real people, not fully by AI. According to them, human team members review cases and make the final call.
When I’ve looked into fast decisions, Airbnb usually attributes them to better training and more efficient support teams. In simple terms, they say speed doesn’t always mean automation.
Airbnb hasn’t clearly explained how everything works behind the scenes. Because of that, many hosts, including some I’ve worked with, still wonder how much automation is actually involved.
Can AI Detect Retaliatory Reviews?
Yes, AI can spot possible negative reviews, but it’s not perfect. Airbnb and Google use AI to flag reviews based on behavior, language, and timing.
But AI misses context. A review might appear normal, but become retaliatory if there is a late-checkout dispute or a denied refund. So, AI can flag potential issues, but where only a human can understand the full story.
How to Increase the Chances of a Human Review?
After handling many Airbnb review disputes, one thing is clear to me that, working with the system and not working against it. Airbnb has both automated filters and human reviewers. If you want your case seen by a real person, you must follow their process or workflow:
Step 1: Submit Only One Automated Request
First of all, I recognise that the host makes the same mistake, which is sending multiple tickets or repeated report requests. But the truth is that it doesn’t help them. Airbnb flags duplicate listings and, in some cases, automatically closes them. So, avoid multiple submissions in the system.
Instead, submit one request through the review dispute form and wait for their response before following up.
Step 2: Use Exact Review Policy Language
Human reviewers always follow checklists. Most оf the time, your appeal will be rejected іf іt doesn’t follow the correct policy. So make sure you follow Airbnb’s Review Policy and your review appeal writing fits into the right category:
- Irrelevant Content: Review talks about traffic, the weather, оr anything else that has nothing tо dо with the stay. It’s denied.
- Biased/Extortive: Guest threatens tо write a bad review іf they don’t get their money back.
- Illegal/Harmful: Reviews that include profanity, hate speech, or discrimination. That declined.
For clarity, if you review about the flight delays instead of the stay, your review violates the ‘Irrelevant Content’ rule. So rejection happens.
Step 3: Keep It Short and Policy-Focused
Long, emotional appeals don’t work here. Tо avoid being skimmed by agents, stick tо facts:
- Describe the violation and the policy that іt violates.
- Show proof, such as screenshots, messages, or dates.
- Make іt quick and easy tо gо over.
There should be nо filler оr stories, just facts. By citing the exact policy section, you make it simple for a reviewer to approve removal without guessing.
Step 4: Escalate Through Customer Support
If the first request is denied, escalate. Always try to:
- Include the conversation ID, e.g., “Following up оn Case ID #1234567.”
- Ask for a Trust & Safety review, e.g., “Please escalate this tо a supervisor оr Trust & Safety member for manual review.”
Note: First-level agents often can’t remove reviews, so escalation is necessary.
Step 5: Contact Community Managers (If Applicable)
To make things clear, use a host circle or community manager if you have one.
- Only tag @AirbnbHelp оn social media when it’s clear that they’ve been violated.
- Regional managers can sometimes get help directly from internal support. Use іt only for cases that have been denied and clearly break the rules.
How STR Assistance Helps You Remove Airbnb Negative Reviews?
Do You Need to help
Remove Review?
In our day-to-day operations, we work with hosts like you through STR Assistance. We understand the frustration when a negative review appears on your listing and appreciate your effort in documenting everything, yet it is still denied.
Our team successfully removes reviews at approximately an 80% success rate when the case qualifies under our Airbnb review removal service.
When issues like this arise, we review the case carefully and determine whether it violates Airbnb’s rules or not. After that, submit an appeal that follows their policy exactly. We make sure evidence is clear, your claim is strong, and the request is easy for a human reviewer to understand.
As a short-term rental virtual assistant company, we manage many host listings every day. Recently, a new host joined us, and a sudden review-related issue came up. We handled it successfully, and the host was very happy with the outcome.
We can also help you with review removal or any other support you need. Also, if you have questions or need assistance, just book a free consultation, and our team will take care of the rest.
Bottom Lines
AI is the future, and everyone should utilize it, and Airbnb is smart enough to understand that. They might be used to simplify processes, but Airbnb has not yet clarified this. Actual reality likely sits somewhere in the middle. That’s why some appeals work, and others don’t.
Removal is not impossible, but it is not simple either. But many reviews can be removed appropriately by submitting your appeal. Whatever automation plays as a small role or a major one, it disappears when you know what to do. If you want someone to review your case honestly, I’m here to help.
