Data annotation & labeling blog.

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Data annotation

Data annotation

Data annotation is one of the important parts of machine learning, especially in AI example. To know more about this and get better knowledge on Data Annotation, we need to know ALL the basics! So, we will be putting up a blog on "Data Annotation" [AIN-103] with A - Z coverage which will help you in understanding different parts related to Annotation of data.

Embodied AI Data Collection for Robotics

Embodied AI Data Collection for Robotics

Embodied AI in robotics is a direction in which AI is combined with the physical body of a robot, enabling it to learn and act in the real world. Unlike traditional models that work only with visual or textual data, embodied AI perceives the environment through sensors, cameras, LiDAR, or

Attribution of Functions for AI-Based Business Intelligence Models

Attribution of Functions for AI-Based Business Intelligence Models

Traditional business intelligence tools struggle to decipher why models prioritize specific data patterns over others. This is where modern interpretation techniques become essential for reliable AI. Sophisticated analysis techniques reveal how specific variables influence outcomes. Global impact metrics quantify the overall role of each data point in learning, while instance-level

Counterfactual Explanation Generation for AI Transparency

Counterfactual Explanation Generation for AI Transparency

A counterfactual explanation serves not only to identify the factors that influenced the model's decision but also to demonstrate what minimal changes in the input data could lead to a different outcome. For example, in the case of a loan rejection, a counterfactual explanation might indicate that "

Best Data Annotation Tools 2025: Complete Platform Guide

Best Data Annotation Tools 2025: Complete Platform Guide

In 2025, annotation software is an inseparable part of any machine learning project. Raw materials, such as photographs, video streams, and spatial datasets, require detailed labels that train algorithms to recognize patterns. Effective labeling directly determines how well artificial intelligence performs tasks in the real world. The higher the quality

Bias Detection and Mitigation in Training Data

Bias Detection and Mitigation in Training Data

Any data collected from systems can reflect historical, social, or technical biases. If these biases remain in the training set, the model can reproduce or even amplify them in predictions, recommendations, or decisions, which can have serious consequences. Just like historical data, algorithmic decisions or human judgments that enter the

Outsourcing vs In-House Annotation: Complete Cost-Benefit Analysis

Outsourcing vs In-House Annotation: Complete Cost-Benefit Analysis

In modern AI projects, the efficiency of data annotation directly determines the speed and quality of model development. Therefore, the question is whether to build an internal team of annotators or outsource the work to an external provider. The primary difference between the two approaches is most often reflected in

Explaining AI for Regulatory Compliance

Explaining AI for Regulatory Compliance

AI systems deployed today cannot fully explain their decision-making processes. This is a problem as global regulations tighten. The EU AI law, which governs how organizations develop and deploy AI, requires clarity and auditability for moderate- and high-risk systems. As AI becomes increasingly complex, industry leaders acknowledge the lack of