What’s the most important thing when it comes to machine learning? The short answer is data annotation. If you get this critical function wrong, your model won’t be able to learn the patterns it needs to.
But not every task is as simple as slapping on a label. Some tasks need a human to make a judgment call. And that’s where we see subjectivity creep in. How serious is this?
It makes things trickier when it comes to consistent labeling, but it’s relatively simple to deal with. In this post, we’ll look at this topic in greater detail.
What Is Data Annotation Task Subjectivity?
Some things are easy to classify, others not. Think back a few years to that white and gold dress that had everyone talking. Did you see it as black and blue? It was divisive because two friends could look at the same picture and see different colors.
It was interesting because it shows we can’t always trust our eyes. Now, if we apply that example to data annotation, we can see how subjectivity can creep in.
People might also argue about whether something is a shrub or a tree. Or a color is pink or purple.
Here are some other examples where objectivity in data annotation becomes hard:
- Sentiment Analysis: Is the tone of this text positive, neutral, or negative?
- Content Moderation: Is this post crossing the line, or is it just fine?
- Intent Classification: Is the comment sarcastic or sincere?
- Relevance Scoring: How useful is this search result, really?
In all of these cases, different people might interpret things differently.
Why Subjectivity Is Tricky in Data Annotation
This matters because it can lead to:
- Inconsistent Labels: One person might find something funny, while another sees it as offensive. How will your client see it and are you willing to roll the dice?
- Ambiguous Data: Sometimes, the data itself is fuzzy. A blurry image or vague text leaves room for different interpretations.
- Personal Biases: Everyone brings their own background to the table, which can influence how they label things. For example, a person from South Africa will talk about “robots” meaning traffic lights.
- Model Confusion: If the data is all over the place, AI struggles to learn and deliver accurate results.
Managing Subjectivity
Now let’s look at ways to make things a little clearer.
Clear Guidelines Are Key
You start by explaining the project to the annotator.
They need to know what to do and why. You should provide a lot of examples, especially when it comes to trickier concepts like sarcasm. The more specific you are, the better the results you can expect.
Multiple Annotators, Multiple Opinions
You should never leave it all up to one person. You can have a few annotators label the same data and see if they come up with the same thing. If not, you’ll be able to fix disagreements quickly.
You can resolve these disagreements with a majority vote or by talking things through. Approaching things this way improves consistency and builds a better understanding of the data.
Diversity Brings Value
It’s fairly easy to hire remote workers from different backgrounds. It’s a good idea to bring people from the same area as your target audience. You can then see how the labels play out with people from diverse backgrounds. Therefore, you’re less likely to offend anyone.
Combine AI and Human Efforts
Here you can use a technique called active learning. This is where you let AI tell you what data it wants you to label. The benefits include:
- Flags unclear cases for human review.
- Lightens the load on annotators.
- Lets human feedback improve AI models.
Consistent Training Helps
You should teach your annotators how you want them to label the data by providing examples. Likewise, you can then compare the results from each and ask why they chose the labels they did. In need, you can tweak the guidelines.
Uncertainty Isn’t the End
Not everything needs a clear-cut label. If something’s ambiguous, let annotators flag it as “uncertain.” This can guide the team or AI in reviewing it later.
It’s better for them to flag it for further review than label it incorrectly.
A Real-Life Example of Handling Subjectivity
Let’s look at how we might apply these principles in real life.
Sentiment Analysis
Say, for example, that you’re creating a movie recommendation app. You might have to review several movie critiques to properly label the data. What happens when you get a mixed review like, “Great acting, but the plot was dull.”
Is that positive, negative, or neutral? Different people could see it different ways. You’d have to vote on it.
Tools That Make It Easier to Deal With Subjectivity
The right data annotation tech tools can help you deal with this issue.
- Annotation Platforms: Tools like Labelbox or Prodigy make collaboration easier and help spot inconsistent labels. Just check data annotation reviews before signing up.
- Quality Assurance Features: Some platforms flag labeling issues for quick fixes.
- Active Learning Frameworks: Frameworks like PyTorch or TensorFlow help blend human effort with AI for more efficient annotation.
If push comes to shove, you can partner with a reputable data annotation company. They’ll have ways to deal with these issues. If you choose the right company, you’ll reap the benefits of data annotation.
What’s Next When it Comes to Subjectivity?
Subjectivity won’t go away, but things are getting better:
- Smarter Pre-Labeling: AI will get better at handling subjective labels, reducing the need for human input.
- Dynamic Guidelines: Platforms will evolve rules based on annotators’ disagreements.
- Context-Aware Tools: Annotators will have more context (like user history) to help them make better decisions.
- Ethical Practices: There’ll be a bigger focus on reducing bias and making the annotation process fairer.
Final Thoughts
Humans tend to be subjective. But when you know that, you can plan accordingly. Start with clean guidelines for your team and encourage them to flag ambiguous data. You can always vote on the right labels before moving forward.
When you handle it right, subjectivity isn’t a problem. It’s an opportunity to build smarter, more reliable AI.