On Privacy & ethics
Today law assumes that privacy can be reduced to your legal name, SSN, email, phone number, purchase history and profile created of you by the organization that served you. In fact much of the protection offered to consumers today by acts such as the CCPA, VCDPA and soon to come Colorado, Connecticut and Utah may adopt similar assumptions about data and privacy. While these laws come a long way to provide consumers with some control of their identity, they miss the privacy implications that exist in other forms of data that are aggregated by Machine Learning (ML) algorithms about you.
Data privacy laws may not adequately address emerging technologies, such as artificial intelligence, machine learning, and the internet of things, which may pose new and unique privacy risks. AI algorithms often rely on large amounts of personal data to make decisions, such as recommending products or services, personalizing content, or even making hiring decisions. However, the use of personal data by AI algorithms can result in the creation of biased or unfair outcomes, which can lead to discrimination, harm, and violations of individuals' privacy rights.
For example, AI algorithms may draw on historical data that reflects existing social biases, such as racial or gender biases, leading to discriminatory outcomes. Additionally, AI systems may be opaque and difficult to understand, making it hard for individuals to know what personal data is being used and how decisions are being made.
A perfect example of how this aggregate layer can have a personal privacy implication is the 2012 Forbes article “How Target figured out a teen girl was pregnant before her father did”. Target data science figured out that when woman purchase a specific sequence of products they are likely to be pregnant. Furthermore Target was able to point to the exact trimester an expecting mother was in, based on purchase history. Today purchase history has been defined as a protected form of data by the CCPA and other legislative work, but this is not a national adopted definition. However companies are still able to leverage purchase history in addition to many other data types to make these implicit decision about who you might be and they are getting really good at it.
Because of rapid technological advancements, lack of public awareness, political factors, resource constraints, national and international variations in data privacy, law lags behind customer sentiment. Its here where a competitive advantage exist for the right organization to adopt a new paradigm of business to customer data extraction.