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AI IS THE FUTURE BUT PRIVACY SHOULD NOT BE THE PAST

With the expansion of internet use over the past few decades, our online activities have grown with it. We leave traces of ourselves almost everywhere, through the devices we use, the websites we visit, sensors in household items, smart appliances, and even biometric cameras in public spaces.

Companies and organizations are learning more and more about us. They can know what we like or dislike, where we go, what we buy, and how we spend our time. In some cases, they may even know things about our behavior that we have never consciously thought about ourselves.

While this may sound frightening, King and Meinhardt (2024) argue that the expansion of artificial intelligence (AI) is likely to increase the demand for even more data. This makes sense when we think about how AI systems are developed. Data is needed to train, test, and evaluate these systems, meaning that data is becoming an increasingly important resource in the development of AI.

But this raises an important question: how much data is actually enough?

The growing collection of personal data for AI systems creates important risks for individual privacy. As AI systems become more capable, the concern is no longer only about the information we intentionally provide. It is also about the information that can be collected about us, combined with other sources, and used to make inferences about who we are.

King and Meinhardt (2024) highlight how the collection of “consumer behavioral data” has moved beyond simply understanding consumer behavior. It is increasingly being used for individual profiling and inference-making. In other words, organizations are not only asking, “What did this person do?” They can also start asking, “What can we infer about this person from what they did?”

This becomes even more difficult because much of the data collection happening around us is not always visible. People are often advised not to share sensitive personal information with AI systems, but avoiding data collection altogether is becoming increasingly difficult as we become more dependent on digital technologies and third-party services.

And this is where I think the conversation around data needs to change.

As someone interested in data and analytics, I understand why organizations want more data. Data can help us understand what happened, why it happened, and potentially what might happen next. But more data does not automatically mean better analytics. Sometimes, the real question is not “How much data can we collect?”, but “What data do we actually need?”

King and Meinhardt (2024) argue that current approaches to privacy are not enough to deal with the growing race to acquire data for AI. Individuals are often expected to protect themselves by reading privacy policies, opting out of data collection, or requesting that their information be deleted. But this puts a significant responsibility on individuals who may not fully understand how their data is being collected, combined, or used.

One of the approaches they propose is moving from a default opt-out model towards a default opt-in model. In simple terms, the starting point should be that personal data is not collected unless the individual actively agrees to it. They also call for greater transparency and accountability across the AI data supply chain, as well as stronger mechanisms that give people more control over their data.

I think this is an important conversation, especially as AI becomes more integrated into everyday life.

Data is incredibly valuable. It can help businesses make better decisions, improve services, identify patterns, and build systems that solve real problems. But the value of data should not mean that collecting as much of it as possible becomes the default.

Perhaps the better question is not whether AI needs more data, but how much data is enough, what data is necessary, and who gets to decide how that data is used.

Good analytics is not simply about having more data. It is about having the right data, for the right purpose, and using it responsibly.

Reference

King, J., & Meinhardt, C. (2024). Rethinking Privacy in the AI Era: Policy Provocations for a Data-Centric World.

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