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5 Challenges of Data Annotation for ML
and How to Handle Them

Building an intelligent or machine learning application that behaves like a real person requires large amounts of data. For such a program to make correct decisions and take appropriate action, it has to be trained for that job. This training has to be done using annotated data. When data is annotated, it is given a label. The label tells the machine what object is on an image specifically.

Data annotation, also known as labeling or tagging, is a way of adding metadata to raw data. It is the process of labeling or categorizing data for Machine Learning (ML) algorithms to be trained on. It can be done by hand in a laborious manual process or automated which is much more efficient and scalable.

Data labeling is a necessary step in many machine learning workflows because it's difficult for algorithms to infer meaning on their own. You have to feed the ML model annotated data for the latter to learn from it and make accurate predictions.

Data annotation is not a new thing in today's world as it has made its way to the forefront of ML development because of the increase in volume, variety, and velocity of data that needs to be annotated. It is crucial to annotate data for machine learning because this enables algorithms to make predictions on future data.

Data annotation is also necessary because it provides information to improve the quality, efficiency, and accuracy of machine learning algorithms. It is used in deep learning and machine learning tasks such as Image classification or sentiment analysis.

However, there are many challenges associated with data annotation that lots of individuals struggle with. The process of data labeling can be tedious, expensive, time-consuming, and often frustrating. It requires careful attention to detail to ensure that the annotations are accurate and can be interpreted correctly.

When taking over a data annotation project these are what you should keep in mind:

  • Data annotation can be time-consuming and tedious.
  • Data annotation is usually expensive due to the wages of annotators.
  • The quality of data annotations varies depending on the annotator's skill level and domain expertise.
  • Annotation tasks are error-prone due to variations in human perception between individuals, both across and within groups.
  • There are many different types of data annotation that require different skillsets, so it can be difficult to find an annotator with overlapping skills.

The above points illustrate why you should be aware of the ups and downs in data annotation. If you are currently working on a project that requires lots of data annotation and labeling, then you should read this article. This article will go over five common challenges when working with data annotation and how to overcome them.

Data Relevance and Quality

Data quality is often an issue as it might be expensive to extract and maintain relevant data. Ensuring that datasets are clean and high quality is one of the most effective ways for getting accurate results from ML algorithms.

When it comes to data quality, there are different points to be considered. It can be difficult to get standardized data. It's difficult to find common ground on what constitutes a good set of annotations because different datasets require different types of annotation.

Another issue to be considered is data reliability. Data annotation can be very unreliable for some datasets, as there is no way to know if the labeling is accurate or not. There could also be missing information which could potentially alter the standard of the data.

Data quality is a challenge because there are many different ways to label data incorrectly. When an error occurs in an annotation task, it can have a ripple effect and impact other downstream tasks that rely on the initial annotation for accuracy.

Accuracy of Labeled Data

When it comes to accuracy, It can be grueling for humans to accurately annotate a dataset without errors, especially when it comes to labeling certain features within an image.

Sometimes, It can also be difficult to identify all the relevant properties of an image to describe it accurately. The system may not understand what is being labeled or how it should be processed and interpreted by the algorithm.

Labeling data properly can be challenging for a company because the process requires a lot of work to process large volumes of unstructured data. Here, companies need to ensure that this data is processed consistently and accurately so that they do not risk their reputation.

Another challenge may be about getting biased results due to human error in labeling or coding of data. Human error can lead to bias in results because humans are capable of adding their own biases while defining labels for datasets.

Cost of Annotation

There are many ways to annotate data for machine learning. Annotation can be done either manually or automatically. Labeling your data manually can incur huge costs as it requires manual effort from expensive experts. Plus you will need to purchase the software you can annotate your data with. So, it’s the equipment, the software, and the human resource that are a must to acquire. Depending on how big your project is, the cost will vary.

Proper Handling of Confidential Data

Another challenge usually faced by organizations and companies includes the improper handling of sensitive data, which could lead to legal consequences with regard to consent and privacy rights as well as ethical issues such as confidentiality and anonymity.

Confidential information is often sensitive and private, therefore cannot be shared. Once you take over a project where the data your team is working on is confidential, not leaking it is your task as well.

Spending a Lot of Time on Annotation

Data annotation is usually very time-consuming, as the annotation done by humans can take weeks to complete if your project is a big one. Apart from that, It also takes a lot of time and effort to create annotation guidelines and tutorials for human annotators. Hence, making the right estimations in terms of time management is another challenge to consider.

How to handle these challenges

Creating a well-designed labeling protocol

The main goal of labeling data is to identify the context and meaning of the project in order to label it accordingly for the machine to make proper predictions.

Labeling datasets can be tricky because there are many aspects to consider when deciding how each dataset should be labeled. For example, if you are creating a classification problem, how do you decide which labels to use?

Designing good labels for trainable models turns out to be one of the key steps when trying to build robust machine learning systems. Well-designed labels lead to more accurate results when training models.

Data annotation protocols need to be well-designed so that they are consistent and easy for annotators to understand and apply. It’s important to give your annotators detailed instructions so that they know what you expect from them when labeling data points.

Automated annotation using crowdsourcing or paid workers

One of the most popular ways to make data annotation faster and better is by using good crowdsourcing. Crowdsourcing involves outsourcing tasks such as the actual annotation to a network of people to finish them more quickly and efficiently.

The use of good crowdsourcing platforms allows for large-scale and low-cost annotation efforts. This means anyone with an annotation project can hire a team to take over, complete the data annotation, and hand in the complete project. Other ways include automating tasks and sharing resources with other projects, institutions, and organizations.


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