--- title: Will AI Annotate Itself? The Model-Assisted Labelling Reality Check description: "LLMs are now labelling their own training data. The efficiency gains are real, but so is a critical failure mode: anchoring bias." image: https://sumedhhabbu.in/hubfs/Cover%20Image%20-%20ChatGPT.png --- [Skip to content](https://sumedhhabbu.in/blog/will-ai-annotate-itself-the-model-assisted-labelling-reality-check#main-content) [![1777448426466~2](https://sumedhhabbu.in/hubfs/1777448426466~2.png)](https://sumedhhabbu.in) - [Home Page](https://sumedhhabbu.in) - [About Me](https://sumedhhabbu.in/about-me) - [My Experiences](https://sumedhhabbu.in/en/experience) - [Get In Touch](https://sumedhhabbu.in/get-in-touch) - [Sumedh's Blogs](https://sumedhhabbu.in/blog) - [Register for Medh AI Meetup](https://sumedhhabbu.in/en-in/register-for-a-medh-ai-circle-meetup) # *Sumedh Habbu* English - United States This is a search field with an auto-suggest feature attached. - There are no suggestions because the search field is empty. # Will AI Annotate Itself? The Model-Assisted Labelling Reality Check [Sumedh V. Habbu](https://sumedhhabbu.in/blog/author/sumedh-v-habbu) Sep 29, 2026, 10:36:56 PM ![Newsletter cover titled “Will AI annotate itself? The model-assisted labelling reality check.” A human reviewing AI-generated labels sits beside a humanoid AI assistant, with a visual workflow showing raw data, LLM pre-labels, human review, training data, and a model connected in a feedback loop.](https://sumedhhabbu.in/hubfs/Cover%20Image%20-%20ChatGPT.png) ### Contents A little while ago, my colleagues and I were discussing how these days, humans don't review AI suggestions, they simply confirm them. Model-assisted labelling, where an LLM pre-labels and a human verifies, is the industry's efficiency bet for 2026. Prima facie, this looks like an obvious efficiency win: let the model pre-label thousands or millions of examples, have humans review the outputs, and reserve expensive human attention for the difficult cases. However, the problem is that "human reviewed" does not necessarily mean "human independently judged". In fact, the human in the loop can become the human following the loop. This behavior is becoming a load-bearing assumption in most SFT and RLHF pipelines right now. *![Figure 1 - ChatGPT](https://sumedhhabbu.in/hs-fs/hubfs/Figure%201%20-%20ChatGPT.png?width=1000&height=500&name=Figure%201%20-%20ChatGPT.png)* #### **What the Research Actually Shows** [A 2025 paper from researchers Hope Schroeder, Deb Roy, and Jad Kabbara at the MIT Center for Constructive Communication](https://aclanthology.org/2025.findings-acl.1323/) put this question to the test. Their preregistered experiment involved 350 annotators and 7,000 annotations across four conditions, two models, and two datasets. Showing annotators LLM-generated suggestions did not make them faster. It did, however, increase their confidence and significantly shift their labels toward whatever the model proposed. When those AI-assisted labels were then used to evaluate model performance, reported performance went up, not because the model got better, but because the ground truth had quietly absorbed the model's own tendencies. *![Figure 2 - ChatGPT](https://sumedhhabbu.in/hs-fs/hubfs/Figure%202%20-%20ChatGPT.png?width=1000&height=667&name=Figure%202%20-%20ChatGPT.png)* The evidence got stronger in 2026 with a study titled["ChatGPT, why can't anyone afford a house? On the Effects of LLM pre-annotation on Annotator Subjectivity" by Emilie Francis and team](https://aclanthology.org/2026.nlperspectives-1.10/). These researchers found that LLM pre-annotation can cause convergence in human annotations and an erosion of human subjectivity. This distinction matters because while higher agreement sounds like better data, this same agreement can also mean that everyone has started seeing the data through the same lens. #### **The Anchor Risk: What Determines the Outcome?** Not every task shows this failure mode at the same intensity. Three variables decide which outcome you get: - Whether verification is mandatory or optional - How ambiguous the task is - Whether the model sits upstream (pre-labeling) or downstream (auditing) of the human judgment Anchoring risk is highest when high task ambiguity collides with optional verification. If your pipeline can't tell you whether a human genuinely disagreed with a pre-label or just clicked through it, agreement rates are measuring compliance, not quality. ![Figure 2 - Claude](https://sumedhhabbu.in/hs-fs/hubfs/Figure%202%20-%20Claude.png?width=1000&height=750&name=Figure%202%20-%20Claude.png) The industry is already building around this problem. In October 2025, [Uber AI Solutions published "Requirement Adherence: Boosting Data Labeling Quality Using LLMs,"](https://www.uber.com/in/en/blog/requirement-adherence-boosting-data-labeling-quality-using-llms/) which offered a genuinely useful reframe. Instead of using an LLM upstream to generate the label, they built a system that uses the LLM downstream to audit human-submitted labels against the client's requirements before submission. The model can flag inconsistencies, identify missing requirements, surface questionable examples, and prioritize review, while humans still own the judgment. Catching errors rather than introducing them resulted in an 80% reduction in required manual audits. Same technology, opposite role in the workflow, very different risk profile. *![Figure 3 - ChatGPT](https://sumedhhabbu.in/hs-fs/hubfs/Figure%203%20-%20ChatGPT.png?width=1000&height=667&name=Figure%203%20-%20ChatGPT.png)* #### **The Inconsistency Inheritance** There is another uncomfortable problem here: LLMs are not consistently reliable annotators either. [A 2025 study evaluating GPT-4 across 27 annotation tasks](https://ojs.aaai.org/index.php/ICWSM/article/view/35883) found that on a third of those tasks, precision or recall fell below 0.5. Automated annotations diverged from human judgment in numerous scenarios, even after prompt optimization. That inconsistency is exactly what gets inherited when review turns into confirmation. So, will AI annotate itself? To some extent, it already is. This creates an increasingly automated data-generation loop:  ![Figure 4 - ChatGPT](https://sumedhhabbu.in/hs-fs/hubfs/Figure%204%20-%20ChatGPT.png?width=1000&height=335&name=Figure%204%20-%20ChatGPT.png) While this loop can be extremely powerful, it can also quietly reinforce its own mistakes. If every human sees the model's proposed label before making a decision, we should not treat their agreement with that label as independent validation. Some samples should be independently annotated without seeing the model's answer, and disagreement between humans and models should be measured, not automatically resolved in favor of consensus. If the AI gets to suggest the answer, and the human gets to approve it, we need to be honest about what that dataset represents. It may no longer represent purely human judgment, but it may represent human judgment after exposure to machine judgment, and that is a very different kind of dataset. For teams running AI training or evaluation pipelines today, how are you distinguishing a reviewer who independently validated a label from one who simply agreed with it? [Opinion](https://sumedhhabbu.in/blog/tag/opinion) [Artificial Intelligence](https://sumedhhabbu.in/blog/tag/artificial-intelligence) [AI](https://sumedhhabbu.in/blog/tag/ai) [Data Annotation](https://sumedhhabbu.in/blog/tag/data-annotation) [Data Labelling](https://sumedhhabbu.in/blog/tag/data-labelling) [LLM](https://sumedhhabbu.in/blog/tag/llm) [SFT](https://sumedhhabbu.in/blog/tag/sft) [AI Models](https://sumedhhabbu.in/blog/tag/ai-models) [RLHF](https://sumedhhabbu.in/blog/tag/rlhf) [Previous Regulating AI: Two Years Later, Are Our Ambitions Still Safe?](https://sumedhhabbu.in/blog/regulating-ai-two-years-later-are-our-ambitions-still-safe) ## Leave a Comment Sumedh Habbu © 2026 All rights reserved. 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