Artificial data scarcity: Is it outdated in the age of AI?

I have long argued that modern technologies can only be effective if governed by principle-based legislation.

- RAHUL MATTHAN

Prescriptive rules tend to be sclerotic, calcifying faster than the technology systems they seek to regulate. What we need instead are broad, durable principles that describe the outcomes we need, rather than the processes by which they are achieved. This ensures that the law’s objectives remain valid even after the technology it governs has evolved in a direction that no one could have anticipated.

The need for well-designed principles-based regulation is particularly acute in the era of artificial intelligence (AI). If there is one thing that is predictable about these computational systems, it is that they will evolve in unpredictable directions. This suggests that the only way to effectively regulate them would be to define the principles according to which they must operate.

But what should those principles be?

AI systems get better the more data they are trained on. Their performance is directly correlated with the volume of their training data: the more data they ingest, the more useful they are. Today's leading AI models have achieved their current levels of excellence only because they were created by processing volumes of data that would have been unimaginable even a decade ago. It is fair to say that our world today is defined by data abundance—and by computational models optimized to extract insights from such abundance.