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Annotation Services

Harnessing the power of AI with advanced data annotation techniques

According to a study by a leading management consulting company, AI will drive a 14% increase in global GDP by the end of this decade. However, much of AI’s success depends on accurate data classification and annotation that forms the ML model. Correct labelling is essential for training the AI engine. Against that backdrop, the data labelling market is expected to create a global market of USD 12.75 bn by 2030.

AI and data annotation platforms are playing an equally important role for large and small businesses alike. However, research shows that 53% of tech businesses are unable to leverage big data processing, and 59% see immature data management as a challenge.

The article discusses the importance of human-in-the-loop annotation, ethical concerns, and data privacy, and strengthening the potential of AI.


Human-in-the-loop data annotation

Data annotation techniques involve careful planning, adherence to best practices, and attention to detail. This requires annotators to have domain expertise and thoroughly understand annotation guidelines. Comprehensive annotation guidelines and standards ensure consistent results and extraction of the right insights.

In the Human-in-the-Loop (HITL) annotation methodology, human annotators work in conjunction with machine learning algorithms. This combines human expertise and judgment with the scalability and efficiency of machine learning. The process comprises the following steps:


Baseline annotation

Human annotators set up a baseline by annotating and manually labelling the data. This baseline acts as training data for machine learning algorithms and automated data annotation platforms.


ML assistance

Machine learning algorithms on data annotation platforms help human annotators label the remaining data. ML algorithms support this process by suggesting priority samples or pre-labelling the data.


Review and correction

ML-driven annotations undergo human review to correct any errors and inconsistencies. Humans provide feedback for algorithms and eventually help improve the annotation guidelines.


Higher quality through iterations

There are several iterations of algorithmic assistance and correction by humans leading up to the desired quality of annotation. The HITL model continuously refines the annotations and enhances the performance of the systems.

By incorporating human feedback in the loop, businesses can address complex tasks that require contextual understanding, subjective judgment, and domain expertise. HITL combines the strengths and unique capabilities of automated systems and high-quality human annotators at scale.


Ethical concerns and data privacy in advanced data annotation

Leverage Humanware and Software for High-Quality Training Datasets

Leverage Humanware and Software for High-Quality Training Datasets

Regulatory and legal frameworks govern ethical and data privacy concerns around advanced data annotation. This protects individual privacy rights and ensures responsible data usage. The ethical implications encompass consent, freedom from bias, respect for privacy, fairness, and equality.

The data annotation techniques and processes must be transparent about data collection, usage, and sharing practices, and obtain consent from the users before capturing their information.


Strengthening the potential of AI with data readiness

In the race to adopt AI, underprepared businesses may lose out to competitors who have built a stronger foundation. If a business does not master data annotation techniques and fundamentals, it may build AI models that yield low-quality results and violate the data collection and user consent requirements.

Standardised and harmonised data with well-integrated repositories, platforms, and transparent data pipelines are essential for a mature AI model that works as per the company’s expectations. Other differentiating factors that put businesses ahead of the competition include:

  • The speed with which one can integrate data into AI models
  • Integration of structured internal data that contains customer information across the business units in AI initiatives
  • Integration with external data (open source or purchased data)
  • The use of unstructured data, including texts, call logs, etc.
  • Generating synthetic data to train AI models where there is insufficient natural data
  • A modular data architecture to accommodate the new AI use cases
  • Automate processes such as data labelling and quality control
  • Ability to scale the whole process quickly

Achieving this means either hiring the right talent in-house or partnering with the right AI and data annotation experts. In fact, research by Infosys suggests that 33% of businesses want to partner with a third party to access the right talent pool that has expertise in data annotation techniques.


How can Infosys BPM help enrich AI tools with data?

Infosys BPM helps enterprises build high-quality training data at scale using a HITL service model, saving time and resources and focusing on refining the AI model. The model leverages the power of human intelligence (humanware) and software-enabled automation to produce high-quality AI-model training datasets.

Read more about the data annotation platform at Infosys BPM.