Closing the Gap: Overcoming Data Bias with the NEW Power of Synthetic Data (01/19)
In the rapidly advancing landscape of artificial intelligence (AI), securing quality training data has become a paramount concern. The axiom “To build a good AI model, you need good training data” holds truer ever. The results produced by AI models are heavily influenced by the quality and characteristics of the training data. One significant challenge that arises in the process is data bias.
Table of Contents:
1. Understanding Data Bias
2. Addressing Data Bias with Synthetic Data
Understanding Data Bias
Data bias occurs when the collected data fails to adequately represent the entire target population or phenomenon. This can manifest in various ways: Unavailability or difficulty in obtaining certain real-world data. Data generated predominantly by specific groups. Discriminatory elements from past datasets created by certain demographics. Training an AI model with biased data can lead to skewed outcomes, reflecting the inherent biases present in the training data.
About Data Bias Further: link here
Addressing Data Bias with Synthetic Data
One effective strategy to mitigate bias of data is through the use of synthetic data. Synthetic data refers to either data generated by applying sampling techniques to real source data or entirely new data created through the interaction of models and processes. This type of data helps ensure diversity and representativeness, addressing biases or missing patterns in real-world environments. It serves as a means to overcome limitations in existing data, enabling models to generalize well across diverse situations.
For instance, in training AI models for autonomous vehicles, scenarios like roadkill may be rare or absent in real datasets. By generating synthetic data for such cases, the model can be trained to handle potential risks in actual hazardous situations, thus enhancing its performance.
Synthetic data emerges as a powerful solution to tackle data bias challenges, enhancing the overall performance of AI models. Its application spans across various industries, offering an effective means to address biases and improve model generalization in diverse scenarios.
If you’re curious about various methods for generating synthetic data, please click the link below