Enterprises across multiple verticals like agriculture, retail, entertainment, and robotics, all rush to apply AI to their business operations. Lately, they have been continuously overcoming their ongoing obstacles over data labeling at scale. Business enterprises today are flooded with the need for the production of usable data. They do not lack raw data; on the contrary, brands possess a lot of data in their organizations. A massive amount of data from cameras, sensors, and other types of equipment are gathered by these organizations at any given time. The prime challenge is how to process and label the data to make it effective and usable. 

Relevant labeled data ensures that machine learning systems establish reliable models for pattern recognition, which forms the foundation of every artificial intelligence project. But, applying complex attributes and various annotations, leads organizations to deploy deep learning and machine learning models, which takes up to 80% of the AI project time. At the same time, 19% of the businesses led to the lack of data and data quality issues and the adoption of Artificial Intelligence.

What misleading Data Labeling can get you through? 

Data Labeling can be misleading and intentional at times if the creator promotes the agenda on purpose. This might result in data errors or the misunderstanding of data or the data labeling process. But, whatever might be the reason, misleading data labeling do not have any place in eLearning as they confuse and misinform the learners. 

The primary ways through which labeling could mislead learners are… 

1. Presenting large data
2. Hiding the relevant data
3. Misinforming the presentation of data
4. Inaccurate data annotations

Now let’s get in depth about each of these:

1. Presenting Large Data

Sometimes, looking at the bigger picture could make it tough to identify the salient data. The entire data set is visualized and studied separately. This phenomenon is known as Simpson’s Paradox. Examination of the data revealed that the data period covered an era with huge growth in numbers and a range of data. 

The learners will require a bigger picture and a thorough visualization of data. Hence, the designers must consider a series of data visualizations. New media mostly does this with large data stories showing a national map, for instance, with broad representations of data via state or region, narrowly focuses visualizations that focus on important trends or other information. 

• Hiding the Relevant Data

Highlighting a particular benefit or hiding a significant data point could lead the learners to focus on a small fraction of the data story at the expense of an accurate understanding of the bigger picture. Any individual statistics or parameter could reveal useful information. So, data visualization presents more complete data, leading the learners to adopt a different approach.

• Misinforming the Presentation of Data

Emphasizing these selected data could lead to errors which results in selecting the wrong format for the data visualization or not completely realizing the data. These errors could be unintentional, still few presentations of the data distort in ways which appear to be agenda-driven or intentional. 

This type of distortion could be found in marketing, consumer advertising, public relations materials, and more. 

• Inaccurate Data Annotations

A specific unethical way that leads to the utilization of data visualizations is, mislabeling of data inaccurately. Data annotators generate metadata in the form of code snippets which categorize data. A brand makes use of data annotations to identify patterns and make data searchable. However, organizations are concentrating their resources on data annotations for preparing data stacks for structured or unstructured machine learning.  

Artificial Intelligence and machine learning is the latest technology to fulfill the new vision of the future. The intersection of data science and computer science is the first step towards the computational representation of everything, where algorithms and big data are the two keys. Algorithms and big data go hand in hand to generate models to process machine learning. 

About Data Labeler

From offering the highest quality training datasets using an advanced workforce to allowing the companies to focus on their core AI/ML business, Data Labeler powers your algorithms. 

Boxes for Object Detection, Polygons for Semantic and Instance Segmentation, Points for facial recognition and body pose detection, and more. 

Contact us for effective Data Labeling Services – Sales@DataLabeler.com