Quick Concepts: Ground Truth Data Collection
What is ground truth data?
Ground truth data is empirical data based on conditions “on the ground.” The term is derived from meteorology and geology, where measurements and observations made on site are used to validate information received from remote sensors such as radar and aerial photography. In AI, ground truth data refers to data that is collected in real-world settings (e.g., recordings of natural conversations) or that is “known” to be true (e.g., when humans use their own knowledge to verify judgments made by AI). Ground truth data is used to train AI algorithms and improve their accuracy. It is particularly useful in helping AI systems interact more naturally with humans.
How is ground truth data collected?
Ground truth data capture occurs in a number of ways and comes in a variety of forms. It includes any data that is captured in context, preferably in a natural setting, such as images, audio and video recordings, direct observations and measurements, and simulations. This data is used to train new AI models or improve existing ones. Because any inaccuracies or gaps in the data could be amplified in AI models, ground truth data should be collected thoughtfully to ensure wide representation and avoid bias. When collecting ground truth data, teams must consider factors such as demographics, locations, settings, auditory/visual noise, natural vs. staged interactions, and methods of collection. In some cases, actors perform scripted scenarios in order to provide ground truth data for occurrences that are difficult to capture in the real world (e.g., fraud and other illegal activities). The actors’ speech, facial expressions, vocal intonation patterns, body language, and spatial orientation can then be used to train AI models.
How is ground truth data used, and why is it important?
Ground truth data is typically used to train AI models that interact with humans. It enables models to accurately interpret human inputs and generate appropriate responses. Use cases for ground truth data include chatbots, virtual assistants, facial recognition systems, virtual/augmented reality, and smart home products. Ground truth datasets provide the variety, contextual details, and nuances required to train interactive AI models to function optimally in the real world and provide a natural user experience.
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