Definition
AI data collection is the process of gathering raw data—text, audio, images, video, or structured records—used to train, validate, and test machine learning models. It ensures that models have representative examples of the real-world problem.
Purpose
The purpose is to build datasets that allow algorithms to learn patterns effectively. Reliable data collection reduces bias and improves model accuracy across different environments and populations.
Importance
- Quality of collected data directly affects model outcomes.
- Poor collection can lead to biased or unusable models.
- Diverse sources improve generalizability and reduce unfairness.
- Must follow ethical and legal standards (e.g., GDPR, HIPAA).
How It Works
- Define the type of data needed based on project goals.
- Identify sources (sensors, APIs, surveys, recordings, etc.).
- Collect data with proper consent and privacy protections.
- Store data with metadata for traceability and context.
- Prepare data for later annotation, cleaning, or training.
Examples (Real World)
- ImageNet: large-scale image dataset for computer vision research.
- Google Street View: data collected for maps and visual AI.
- Mozilla Common Voice: open dataset of speech recordings for ASR.
References / Further Reading
- Datasheets for Datasets — Gebru et al., ACM FAccT.
- Data Preparation for AI Systems — NIST.
- ISO/IEC TR 20547-5: Big Data Reference Architecture — ISO.