5 Things to Consider When Starting a Data Annotation Project
Data annotation, also called data labelling, is an essential step in the creation of AI and machine learning software. It involves providing the necessary information to train machine learning models effectively. Through data annotation, the model learns to understand and differentiate various inputs, leading to more accurate outputs. Without human-provided labels, machine-learning algorithms would struggle to understand the necessary data. The process involves labelling and commenting on datasets, which enables the AI model to continuously improve and make smarter decisions over time. The more annotated data used for training, the higher the model’s intelligence.
However, producing annotations at scale can be difficult due to the complexity of the annotation process. Creating accurate labels requires time and skill. Human involvement is essential for identifying and annotating specific data, letting machines learn and classify information effectively. As such, here are 5 things to focus on when embarking on a data annotation project –
Do the project’s benefits balance out the difficulty?
Data annotation is a difficult and time-consuming part of the machine learning pipeline, particularly as the boundaries of AI expand rapidly. According to a ResearchAndMarkets report, this trend is propelling the global data annotation market to a projected size of $13 billion by 2030. As datasets expand, the model training processes become more intricate and complex. This expansion improves precision and accuracy as larger samples yield more useful results. But it also increases the workload on the data annotators.
Cognilytica’s report on Data Engineering, Preparation, and Labelling for AI shows that over 80% of AI project time is dedicated to data management tasks, such as data collection, aggregation, cleaning, and labelling. In this landscape, finding skilled data annotators who can label specialised datasets while ensuring accuracy and consistency is becoming challenging due to changes in the labour market.
Is it more efficient to annotate in-house or outsource?
When choosing to handle data annotation in-house, researchers gain more control over the entire process. This can be particularly important when dealing with sensitive data that requires strict privacy and security. Additionally, in-house teams often possess deeper domain expertise, allowing for more accurate annotations tailored to the organisation’s specific requirements. This better understanding of the context contributes greatly to the quality of the annotations. Building an in-house data annotation team is a long-term investment for a more efficient and specialised workforce in future projects.
Conversely, outsourcing data annotation to specialised third-party companies or crowdsourcing platforms is cost-effective, especially for short-term or one-time projects. You can leverage the expertise of full-time annotators without investing in extensive training. Moreover, outsourcing has scalability, making it an ideal choice for large-scale projects or those with rapidly changing requirements. Access to a larger workforce will ensure faster annotation turnaround times, boosting the overall project progress.
Some might opt for a hybrid approach, combining in-house and outsourcing, to strike the right balance between control, skill, and cost-effectiveness for their data annotation project. This article will later discuss some of the issues in-house teams face in data annotation projects.
How much data is needed for the project in the first place?
Having domain experts supervise the data annotation is important for creating base data that is accurate and relevant. These experts ensure that the data is properly labelled, providing a solid foundation for the model to learn from. Periodically evaluating the accuracy of the annotations and improving the data labelling process will further improve the quality of the training data and improve the AI’s performance.
Does the data set require specialist domain experts?
Having domain experts supervise the data annotation is important for creating base data that is accurate and relevant. These experts ensure that the data is properly labelled, providing a solid foundation for the model to learn from. Periodically evaluating the accuracy of the annotations and improving the data labelling process will further improve the quality of the training data and improve the AI’s performance.
Is the data sufficiently relevant?
To make sure the data annotation is representative of a specific domain, it is absolutely essential to have a thorough understanding of the domain’s vocabulary, data format, and data categories. This helps in building an ontology, which is a formal classification of the types, properties, and relationships of units within that domain. Ontologies provide meaning to the data, letting machine learning models interpret and understand the information in a consistent way.
By creating an ontology, you are effectively teaching the AI to communicate within that domain, enabling it to process and solve cases related to the specific problem. This common link between the AI and the data ensures that the model will accurately learn from the annotated data and make meaningful predictions or decisions.
The Vast Challenges of Data Annotation Projects -
While data annotation is essential for the success of many AI applications, the process comes with several deep challenges as well. These challenges are often especially prickly for in-house teams, as these often have less training, experience and specialisation than specialised third-party teams. Some of these problems can include-
Multi-Modal Data – In some applications, data may be in several formats, like text, images, video, or audio. Annotating such multi-modal data requires specialised tools and expertise.
Longevity – Over time, the relevance and accuracy of annotations may drop, especially as the AI evolves. Keeping datasets up-to-date is a difficult challenge for in-house teams, as it saps away significant labour resources.
Human Error – Subjectivity and human bias play a big role in machine learning. Sometimes, there are no clear right or wrong answers, which makes the labelling process fuzzy and dependent on the judgement of the person doing the labelling. This introduces human bias into the labelled data.
Confirmation Bias – Annotators often label data based on information that aligns with their pre-existing conceptions. For example, when labelling data related to COVID-19 vaccine effectiveness, their preconceived notions about the vaccine’s efficacy can influence their labelling decisions.
Anchoring Bias – Annotators tend to give importance to the first piece of information they encounter. Initial samples heavily influence how they annotate labels and will impact the labelling decisions throughout the process.
Functional Fixedness – Labelers may associate a label with only one specific use or function, overlooking other possibilities. For instance, when asked to label “an object to push down nails in an image,” they might only consider a hammer and overlook other objects that could fulfil the same purpose, like a wrench.
Quality Control – Ensuring the accuracy and reliability of annotated data is crucial for the success of machine learning models. However, due to the increasing use of automated tools, meticulous human oversight and verification can be lacking. This lack of quality assurance can adversely affect the performance and reliability of the trained AI models. To address this challenge, it is important to have a balance between automation and human involvement. While automation eases the labelling process, human experts play a critical role in validating the annotations.
Data Regulations – Data annotation projects face the difficult challenge of adhering to strict data privacy laws such as the General Data Protection Regulation (GDPR), Data Protection Act (DPA), and California Consumer Privacy Act (CCPA). Companies have to follow these principles and safeguard sensitive client data during the data labelling process to avoid getting sued. Data annotation teams must thus have strong security measures. This includes encryption of data, access controls to limit data access to authorised personnel only and good data storage practices. Additionally, regular audits and compliance checks have to be performed to make sure that data security protocols are being followed diligently. These practices can get very expensive, especially for non-specialised firms.
