AI models are exploding. In virtually every industry, deep learning involves image processing, natural language processing, and recommendation systems. The ability of AI to use personal data that infringes on privacy rights is increasing, and so is concern.
Given the vast quantities of data, there’s always a risk of data leaks, and that machine learning will learn what should be private. In a 2017 survey of more than 5,000 people, Genpact found that 63% of respondents valued privacy more than a positive customer experience. They wanted businesses to avoid using AI in case it invaded their privacy. The concerns include how an AI system can access a consumer’s personal information, what kind of information it can access, and how significant a privacy infringement this could be.
However, the other side is that businesses can use AI to minimize the risk of privacy breaches by encrypting personal data, reducing human error, and detecting potential cybersecurity incidents.
Business leaders must consider the architecture that will build, train and deploy AI models and know how to collect and process large amounts of data. This requires assembling a good team of data engineers, ML engineers, and data scientists.
It is true–AI comes with risks, and AI platforms must be sophisticated. They will need to stay constantly abreast of news from the world of AI. No AI solution is perfect, and there’s a limit to its effectiveness, so it cannot be relied upon entirely. AI models should be constantly tested and retrained so that they are effective based on current data.