There is an amusing and slightly acerbic acronym that has stuck with me from my days working at a computer helpdesk for an international oil firm: PICNIC. Short for “problem in chair, not in computer”, my colleagues used it as code whenever an employee rocked up at our helpdesk with a complaint or problem that was due to human clumsiness rather than malfunctioning hardware. “Did you check that the printer was plugged into the power socket?”
Nevertheless, says Artificial Intelligence (AI) researcher Robert Elliott Smith, our blind faith in computers and the algorithms that run them is misguided. Based on his 30 years experience working with AI, the aptly titled Rage Inside the Machine takes the reader on a historical tour of computing to show how today’s technology is both less amoral and more prejudiced than we give it credit for.
Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All, written by Robert Elliott Smith, published by Bloomsbury Business (a Bloomsbury Publishing imprint) in June 2019 (hardback, 344 pages)
At their silicon hearts, computers are just big number crunchers. This has led to the tacit assumption that computers are rational machines that cannot possibly be biased, as opposed to humans. But this, says Smith, is a mistake. The theories and findings that gave rise to today’s algorithms go back several centuries and are products of their times, and this historical context is often ignored in contemporary discussions. A large part of Rage Inside the Machine, therefore, is a trip down memory lane.
The first historical vignette goes back all the way to 1290 when Christian scholar Ramon Llull tried to make a mechanical device that would give irrefutable proof that Christianity was the one true faith. Goofy as this may now sound, it did lead him to write about the mathematical subdiscipline of combinatorics, which in turn influenced scholars and philosophers centuries down the line. Combinatorics is quite simply the study of how many possible combinations you can make with a given number of component parts. What it reveals about reality is that extremely complex problems – ones that are easy to describe but hard to solve – are actually surprisingly common. The travelling salesman problem is probably the most well-known example of a problem where the number of possible solutions rapidly balloons. Thus, to provide us with answers, algorithms simplify real-world processes and are by their very nature reductionist.
With that firmly in mind, Smith proceeds to look at the historical antecedents of various facets underpinning AI, with the occasional foray into equations and Venn diagrams. He thus discusses the history behind probability theory, which is the mathematical modelling that uses statistics to analyse complex data. And he shows how Darwin’s theory of evolution by natural selection was rapidly appropriated and applied to social contexts. When combined with Friedrich Gauss’s concept of the bell curve (a graph that shows the dispersion of data either side of an average value), it was used as justification for eugenic practices aimed at the betterment of the human race by eliminating statistical outliers.
“[…] to provide us with answers, algorithms simplify real-world processes and are by their very nature reductionist.”
One of the most notorious tools that came out of this form of social Darwinism, which is still with us today, is the intelligence quotient (IQ) test. It has been used to prop up racism and sexism for decades. Less well-known is that other statistical tools had equally less salubrious origins, with links to both eugenics and mental asylums (the name Karl Pearson might ring a bell from your statistics classes).
The current fears that AI will soon make large swathes of humanity unemployable (also see CGP Grey’s excellent video Humans Need Not Apply) is an echo of what happened when the Industrial Revolution replaced the cottage industry with factories. Conversely, the notion that you can increase efficiency by dividing labour influenced how humans did complex computations before technology could help out – it led to groups of skilled people in computing factories breaking down the task into bite-sized chunks.
“The current fears that AI will soon make large swathes of humanity unemployable […] is an echo of what happened when the Industrial Revolution replaced the cottage industry with factories”
Other important figures that feature in Smith’s story are Alan Turing, Claude Shannon, and Noam Chomsky. Turing, and the test named after him, gave rise to the idea that the brain is just a computer. Shannon’s information theory underlies all electronic communication today. And Chomsky studied human language, particularly its syntax, and his contributions are still relevant to the current struggle of algorithms to really understand human language with all its subtleties. Ironically, much discussion on AI is muddled by the language we use to describe what algorithms are doing, resulting in wishful mnemonics: the naming of computational phenomena with words denoting human characteristics and capabilities. “Does Google’s AlphaGo programme really intuitively decide on its next move when playing Go?”, asks Smith.
Throughout his book, Smith links the historical material back to current concerns around AI. One of the take-away messages that he repeatedly hammers home is that the assumptions and simplifications we have built into our algorithms are wedded to historical prejudices and baggage. And by their very nature as relentless optimisers, algorithms will reinforce these and feed them back to us, as examples of racist and misogynist AI bloopers show.
Still, as the book progresses, I increasingly felt Smith went a bit off-script. I think the book’s subtitle initially put me on a wrong footing and led to me expecting more social commentary and less history. There were certain points in the book where I wondered “what does this have to do with current concerns about social networks?” One example is when he writes of his work for aerospace company McDonnell Douglas. Here he trained genetic algorithms, which mimic evolution to find better solutions, to learn the fighter jet maneuvres of top-gun pilots. Though, to his credit, those same genetic algorithms can help us understand how social network architecture leads to the self-reinforcing filter bubbles that have become a grave concern (but see the critique Are Filter Bubbles Real?).
“While Silicon Valley is awash in dreams of the coming Singularity […] Smith sees a far more immediate problem in the unholy trinity of scientism, computation, and commercialism.”
Together with books such as The AI Delusion and Rebooting AI, Smith clearly falls in Camp Cautious. While Silicon Valley is awash in dreams of the coming Singularity, when AI will eclipse human intelligence, Smith argues that beyond computers having become more numerous, powerful, and connected, not much has changed. I would add that the maxim “garbage in, garbage out” still stands firmly. Rather than the future existential threat of AI that some fear, Smith sees a far more immediate problem in what he calls the unholy trinity of scientism, computation, and commercialism. We obliviously trust the powerful algorithms employed by large firms such as Facebook that have penetrated every nook and cranny of our everyday lives. And it is easy to forget they have but one objective: maximize profit. And that, argues Smith, is far more dangerous to humankind than nightmarish visions of the robot apocalypse.
So, how can we stop the internet making bigots of us all? Smith is not outspokenly prescriptive, though his work on evolutionary algorithms suggests we can create a different kind of beast, a breed of diversity-preserving algorithms rather than the relentless optimizers underlying current online social networks. Instead, the goal of this book is foremost to educate readers, to arm them with a better understanding of how algorithms work by simplifying reality, and to raise awareness of how their inner workings betray the past prejudices that are still baked into them. To that end, Smith presents a very pleasant and accessible mix of revealing history, personal anecdotes, and sharp observations.
Disclosure: The publisher provided a review copy of this book. The opinion expressed here is my own, however.
Other recommended books mentioned in this review:
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, Now take another good look around you. Where is the internet that we were promised?
“The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy“, written by Matthew Hindman, published by Princeton University Press in October 2018 (hardback, 256 pages)
In response to this question, associate professor of media and public affairs Matthew Hindman is telling his readers not to hold their breath. Despite much belief and fervent wishing to the contrary, he strongly argues that the internet is not a level playing field. A handful of multinational firms have attained a virtual monopoly on digital audiences and online revenue (a virtual virtual monopoly?) With The Internet Trap, Hindman explores how we got here and what the impacts are on business, news, and politics.
Amidst all the books that I review on biology and related sciences, this book is perhaps an unusual choice. I hope my pen name speaks for itself. One of my favourite bloggers, Mark Manson, eloquently summarised my feelings on the impact of the internet with his post Everything Is Fucked and I’m Pretty Sure It’s the Internet’s Fault.
“There is serious money to be made in trying to monopolise that most precious of commodities: your time”
When I read about Hindman’s book, I was wondering what new views it would add. After all, in recent years there has been a veritable outpouring of distress and concern. Authors worry that the algorithms running the internet are isolating us in filter bubbles, exposing us only to views we already agree with (see The Filter Bubble: What The Internet Is Hiding From You, but also the forthcoming critique Are Filter Bubbles Real?), are turning us into easily offended bigots (see Hate Inc.: Why Today’s Media Makes Us Despise One Another and Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All), are eroding intelligent thought (see The Shallows: How the Internet is Changing the Way We Think, Read and Remember and World Without Mind: The Existential Threat of Big Tech), and are overwhelming us with too much of everything (see The Internet Trap: Five Costs of Living Online). Others decry the invasive character of both social media (see Terms of Service: Social Media and the Price of Constant Connection) and new internet business models (see What’s Yours Is Mine: Against the Sharing Economy). In short, many authors are very concerned about what the internet is doing to us, the poor users. And this is where this book makes a novel and interesting contribution.
At its heart, this book all about what is called the attention economy. There is serious money to be made in trying to monopolise that most precious of commodities: your time. The epigraph to chapter 2, quoting former Facebook employee Jeff Hammerbacher, reads:
“The best minds of my generation are thinking about how to make people click ads. That sucks.”
The main thrust of Hindman’s argument is that the ability of firms to attract users and retain their attention is what determines their survival online. And bigger firms can throw more of everything (money, manpower, and infrastructure) at achieving this aim. His descriptions of the data centres and other hardware that power online giants like Google should give pause to anyone who thinks that the internet makes things “free” or that infrastructure has become redundant just because information can be sent digitally. I found his point that Google’s facilities are “industrial mills, digital smelters refining not ore but information” a powerful comparison. The same economies of scale that apply to conventional industry are at play here.
Thus, through relentless software development and endless comparative testing of user experiences (so-called A/B testing), large firms such as Facebook and Google have edged out the competition. Smaller firms simply cannot compete and have progressively lost more and more of their online audience. The other big lesson to draw is that small differences (for example minuscule differences in a page’s loading time) matter when it comes to retaining your audience and compound over time. As Hindman points out, when that happens, they rapidly cease to be small differences. And, the author says, let us not forget switching costs: all of us are resistant to change. Once we have learned to use a certain website or software, we become locked-in and less likely and willing to try something new. Most of us will recognise the frustration of having to learn to use new software or upgrade to the latest version of our preferred operating system. (Hell, I stuck with Windows XP ten years past its use-by date.)
“Google’s facilities are “industrial mills, digital smelters refining not ore but information””
In the process, the big internet firms have become fiendishly effective at retaining our attention, at creating an-as-frictionless-as-possible online experience. To keep us mindlessly scrolling and clicking has been elevated to an art form and has become their raison d’être (see also The Attention Merchants: The Epic Scramble to Get Inside Our Heads). It has gotten to the point where internet addiction is now a serious problem (see also Irresistible: Why You Are Addicted to Technology and How to Set Yourself Free). Something that, I think, is not sufficiently recognised, though calls for resistance have been issued (see for example Stand out of our Light: Freedom and Resistance in the Attention Economy and How To Do Nothing: Resisting the Attention Economy). Maybe computers should come with a health warning? Or you may at least want to reconsider putting a smartphone or tablet in the hands of a young child who has yet to learn restraint.
The middle part of this book goes beyond merely verbally making these points. Hindman spends a chapter building a formal economic model of online content production, another chapter analysing a large dataset of web traffic that reveals some remarkable patterns, and two chapters on local news websites. This is where the book gets a bit more nitty-gritty, though without losing readability – technical details have been relegated to the appendix. The analysis of local news websites and the recommendations Hindman gives to strengthen local journalism are US-centric due to the source data used, but they will no doubt apply wider. I found these chapters “only” reasonably interesting, but that was really because the first few chapters made such convincing points and outshine this part of the book.
“[…] big digital firms have become fiendishly effective at retaining our attention, at creating an-as-frictionless-as-possible online experience”
In my opinion, The Internet Trap is a smart and opinionated argument that, as Hindman points out a few times, goes against much established internet scholarship. It runs counter to frequently aired opinions, even those of industry-insiders such as aforementioned quote by Google’s Larry Page that the “competition is only a click away” (see also 21 Digital Myths: Reality Distortion Antidote). Encouragingly, Hindman is not afraid to openly admit his own past mistakes in that context, such as his claim from The Myth of Digital Democracy that “the internet is reducing the cost structure of media firms and content producers: it lowers the cost of distribution”. Any author who will go on record to admit he was wrong goes up a few notches in my estimation. If, like me, you are sometimes concerned with the concentration of online power in the hands of just a few large monopolies, how this affects us all, or wonder how we got here, this book is highly recommended and sure to provide food for thought.
Disclosure: The publisher provided a review copy of this book. The opinion expressed here is my own, however.
Other recommended books mentioned in this review:
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