December 26, 2019
The Promise in Everyday Chaos: Our Expert Interview With David Weinberger
Rachel Salaman
©©Alberto Mingueza
Chaos is not usually a positive state to be in. But, for author and technology guru David Weinberger, today's unpredictable world provides new and exciting opportunities.
His new book, "Everyday Chaos: Technology, Complexity, and How We're Thriving in a New World of Possibility," aims to dispel our fears about the future in the Internet Age, and offers some practical advice on how to thrive, too.
Weinberger has been watching technological change for more than two decades. In 1999, he co-authored the "Cluetrain Manifesto," a treatise on the transformative power of the internet.
There's no doubt that the exponential growth of technology in our daily lives has been a mixed blessing. Weinberger is the first to admit that it has left many problems in its wake. Machine learning is a good example.
The Merriam-Webster dictionary defines machine learning as, "The process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model."
This innovation has so much promise, but it needs to be handled with great care and attention. The data that feeds machine learning is not always neutral, and this can have unpleasant consequences.
In our interview, Weinberger uses the example of recruitment.
"If you feed in employment information – because you're trying to train a machine-learning system to be able to sort through job applications, to pick out who should be interviewed by a human – it's very likely that that machine-learning system is going to "learn" that being a woman does not correlate very well with being a senior manager. Because that's the historic bias that the data represents," he explains.
"It can be very hard to notice that bias, and find all the factors that might reflect that bias in the data, to get rid of that bias. So machine learning represents a genuine danger of not only reproducing, but actually amplifying, existing biases. That's a real issue, for sure."
But this doesn't mean that we should give up on the immense potential of machine learning. The key is for humans to realize that they are in control of technology, not the other way around.
"For example, it's common now… to make sure that a diverse and representative set of people are involved in every phase of the development of the machine-learning system, including thinking through the data that's being collected and where there might be reflections of hidden bias, but also in the sort of outcomes that are desired," Weinberger says.
"The human side of this needs to thoroughly surround the design, development and deployment of machine-learning systems."
This idea is reflected in another theme in his book, "interoperability" – or working together across platforms – which he calls, "the very heart of the internet itself."
When companies share ideas and capabilities, and individuals weigh in too, everyone can benefit. That's the true glory of our connected world, Weinberger believes.
"Rather than thinking that you are always in a zero-sum game (in which it's either you or your competitors), when it makes sense – it doesn't always, but when it does – engage with your competitors in order to make more things possible," he suggests.
Likewise, "Rather than thinking about your customers as consumers of your product, [try] recognizing that they are full partners in the success of your product, listening to them, enabling them to add value to your product by adding features, transforming the way it works."
Interoperability gives us the opportunity to work together, for the better.
In short, Weinberger believes that the chaos of our age should not make us despair and back away. We should view the huge, unpredictable changes taking place "as an opportunity to make more possibility."
We just need to stay open and alert in order to reap the rewards.
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