The adage "garbage in, garbage out" holds exceptionally true in the realm of AI. The quality, diversity, and representativeness of data used to train an AI model directly influence its outputs and potential risks.
The artificial intelligence market is estimated to reach $407 billion by 2027. By 2030, it is set to reach $1.4 trillion, increasing by a CAGR of 38.1%,as per the report of BigID.
It’s not a huge surprise. Manufacturing, cybersecurity, clinical research, retail, education, marketing, transportation, and many other industries are benefiting from the use of AI in data practices and data processing.
AI and data privacy are intrinsically connected because of machine learning. As you know, machine learning is used to “teach” models using supervised or unsupervised learning.
You feed the model vast quantities of data, which it uses to learn. The more data you give it, the more it develops its own logic based on what it has learned. Then, you can use that learning in the form of generative AI or automation.
This vast quantity of data, or big data, is very important in this process. It has a considerable impact on machine learning in the form of the three Vs — volume, variety, and velocity.
Secondly, anonymity is a big part of collecting data. The most important consideration for personal information is whether it’s identifiable or not. Before using personal data for training AI models, anonymize or pseudonymize it to remove or replace identifiers that link the data to an individual. This can help protect privacy and reduce risks if the data is compromised.
Going forward, AI privacy is set to change and mature as technology evolves. New challenges will require continual adjustment of regulatory frameworks. Organizations can significantly reduce the risks associated with AI and build more trustworthy and reliable systems.
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