The rise of machines: what is your data used for?

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"The Terminator", "The Matrix", "I, Robot".

These are all movies where machines become sentient and attempt to take over the world (or at least kill all humans). It's a popular plot because it speaks to our deep fears about technology. Will our devices and the data they collect be used against us as we move to Web3?

It's not just Hollywood paranoia. In recent years, we have seen increasing evidence that our data is being used in ways we never anticipated or anticipated. The Cambridge Analytica scandal showed how Facebook data was harvested and used to manipulate voters in the US presidential election.

Google has been fined for collecting data from children without parental consent. And facial recognition technology is used by law enforcement and businesses with little regulation or oversight.

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In this article, we will look at the dangers of unfettered data pipelines and how blockchain technology, especially as we move towards Web3, can potentially reduce the opacity of the black box algorithms.

The world runs on algorithms

We live in a time where algorithms are increasingly making decisions for us. They decide what we see on social media, what ads we might like, and who gets a loan and who doesn't.

Algorithms can be simple, like the one that decides in which order to display results in a search engine. Or they can be more complex, like those used by social media companies to decide which posts to show us in our news feeds.

Some of these algorithms are designed to be transparent. We know how Google's search algorithm works, for example. But many others are opaque, meaning we don't know how they work or what data they use to make decisions.

This lack of transparency is concerning for several reasons. On the one hand, it can lead to biased decisions. If an algorithm uses race or gender as a factor in its decision making, this bias will be reflected in the results.

Second, opaque algorithms can be manipulated. If we don't know how an algorithm works, we can't understand how to play it. This is why many companies keep their algorithms secret - they don't want people tampering with the system.

Finally, opaque algorithms are difficult to hold accountable. If an algorithm makes a mistake, it can be difficult to understand why or how to fix it. This lack of accountability is especially problematic when algorithms are used for important decisions, like whether or not someone gets a loan or a job.

The dangers of data pipelines

The problem with algorithms is that they are only as good as the data they use. If the data is biased, the algorithm will be biased. If the data is incomplete, the algorithm will make inaccurate predictions.

And often, the...