Minimizing data breaches and privacy compliance are now top strategic and operational priorities for organizations given increasingly onerous data privacy regulatory requirements.
Consider the facts:
Machine learning technologies are not new. Technologies such as document capture, pattern recognition, and knowledge management are widely used to automate the digitization of documents.
With the advent of big data and cloud computing, machine learning is gaining mainstream adoption. Referred to as deep learning, a more advanced form of machine learning, is designed to process and analyze "high volume, high velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
Applications of deep learning such as fraud detection and recommendation engines deliver significant commercial benefits by empowering organizations such as banks and e-commerce providers such as Amazon and Netflix to gain granular and contextual insight into customer sentiments and buying preferences.
While these advances in machine learning technologies do benefit consumers, they may also potentially compromise their privacy rights. For example, profiling based on consumer’s social media likes and preferences, while delivering value in the form of more targeted advertising, may expose personally identifiable information by combining such information with other metadata such as GPS information.
A recent decision by the US Supreme Court in Riley recognized the potentially adverse consequences of profiling based on the collection of metadata: “An Internet search and browsing history, for example, could reveal an individual’s private interests or concerns”. Moreover, GDPR recognizes the importance of protecting privacy rights relating to “any form of automated processing of personal data consisting of data to evaluate personal preferences, interests, behavior, location, and movements.”