Companies with big data pools can have great economic power. Today, that shortlist includes Google, Microsoft, Facebook, Amazon, Apple and Baidu.
I think we’re just beginning to understand the implications of data as an economic asset.
Steve Lohr (a journalists from The New York Times) had a recent conversation with Andrew Ng, a Stanford professor who worked at Google X, co-founder of Coursera and now chief scientist at Baidu. He asked him why Baidu, and he replied there were only a few places to go to be a leader in A.I. Superior software algorithms, he explained, may give you an advantage for months, but probably no more. Instead, Ng said, you look for companies with two things — lots of capital and lots of data. “No one can replicate your data,” he said. “It’s the defensible barrier, not algorithms.”
I asked myself the following question: Technology is moving beyond increasing the odds of making a sale, to being used in higher-stakes decisions like medical diagnosis, loan approvals, hiring and crime prevention. What are the societal implications of this?
steve Lohr argues that the new decisions that data science and AI tools are increasingly being used to make — or assist in making — are fundamentally different than marketing and advertising. In marketing and advertising, a decision that is better on average is plenty good enough. You’ve increased sales and made more money.
But the other decisions are practically and ethically very different. These are crucial decisions about individual people’s lives. For these kinds of decisions, issues of accuracy, fairness and discrimination come into play.
What we probably need is some sort of auditing tool; the technology has to be able to explain itself, to explain how a data-driven algorithm came to the decision or recommendation that it did. And it would important that a “human remains in the loop” for most of these kinds of decisions for the foreseeable future.