· Data Annotations · 2 min read
The Hidden Cost of Poor Data Labelling
Bad labels breed bad models. We look at why data annotation is often the single biggest point of failure in AI projects and how to fix it.
It is a cliché in the AI world to say “Garbage in, Garbage out”, but few teams truly treat data labelling with the respect it deserves. We often see teams spending weeks optimising hyperparameters (learning rate, batch size) while ignoring the fact that 15% of their “Ground Truth” data is actually wrong.
The Multiplier Effect of Error
If a human teacher gives a student the wrong answers, the student doesn’t just fail that question; they get confused about the underlying logic. Neural networks are the same. A single mislabelled example in a training set can have a disproportionately large impact on the model’s decision boundary.
Research has shown that improving label quality is often 10x more effective than doubling the dataset size. If your model is stalling at 85% accuracy, adding more noisy data won’t help. Fixing the labels will.
Building a Quality Assurance (QA) Loop
You cannot rely on a single annotator. Humans get tired, bored, and bias creeps in. To fix this, you need a “Consensus” workflow:
- Overlapping: Have 3 different people label the same image.
- Consensus: If 2 say “Cat” and 1 says “Dog”, the label is “Cat”.
- Arbitration: If all 3 disagree, send it to a “Super Annotator” (a senior expert) to decide.
The Alps Agility Approach
We treat annotation as an engineering discipline, not a gig-economy task. We define strict “Gold Standard” guidelines before a single image is touched. If the guidelines aren’t clear enough for a human to agree 100% of the time, they certainly aren’t clear enough for a computer.
Is your data holding you back? Contact our Data Ops team to audit your training data quality.
