Writer and filmmaker Astra Taylor explains fauxtomation as "the process that renders invisible human labour to maintain the illusion that machines and systems are smarter than they are." (Faux means false or fake in French).

Taylor gives an example of standing in line to collect food and the person in front of her expressing amazement at how the app knew his order was done 20 minutes early. The server replies, "I sent you a message."

With the rise in incredible abilities driven by AI chatbots and tools, it's tempting to believe that AI can run anything. But, in many situations, claims of AI magic and sophisticated automation disguise work done by people—often outsourced and poorly paid.

An example of fauxtomation is content moderators, whose job is to review and filter objectionable content from the web when the claim is that sophisticated algorithms and AI are doing the work. Other examples are tagging or searching images, transcribing audio, responding to customer queries, making decisions, or, ironically, training machine learning models. In another twist, ordering on the app or touchscreen at McDonalds or the self-checkout at a supermarket, you, as a customer, become the labour of a server or waiter.

Some have argued that automation polarises work into vast quantities of small, menial tasks feeding the machines on one end and sophisticated, high-skilled work in designing and maintaining the machines at the other.

Amazon has long offered its Mechanical Turk service, which crowdsources assignments with a distributed workforce—they called it artificial, artificial intelligence. The original Mechanical Turk was a hoax from the 1700s where a concealed human chess player fooled onlookers into believing a machine was playing.

As a design and UX professional, I've used the Wizard of Oz prototype technique. Named after the infamous wizard secretly controlling impressive machines from behind a curtain, the idea is to give the appearance of a working software prototype by discreetly controlling what people see based on their actions.

AI promises instant automation, but that's rarely the case. As you work to automate and remove inefficiencies, it's natural that systems should be supported by people.  Human-augmented work may be a more appropriate term. In many domains, especially where safety is at stake, such as in self-driving cars, reviewing legal documents, or automated background checks, oversight by people may remain critical.

Alongside the AI revolution, a quiet revolution nearly as significant for our everyday experience is the tying together of different tools for automation. Tools like Zapier, Google Sheets, Notion, and the growth of APIs allow us to automate workflows across systems, saving massive amounts of time and effort.

While the vision of machines reducing our labour persists, history shows that they often drive increased productivity instead of reducing work. This echoes Jevon's paradox, where fuel efficiency gains usually result in increased fuel usage rather than less.

I saw an excellent talk by machine learning and AI expert James Smith, in which, before you try machine learning ("I hear you have Magic Pixie dust. Can I have some?"), he would ask, "Have you tried 50 IF statements?" These programmed rules will be more maintainable, less complex, and result in responses that are easy to understand.

I learned about fauxtomation from Tom Humberstone's remarkable visual essay What Luddites Can Teach us About Resisting an Automated Future in the MIT Technology Review. Fauxtomation is similar in concept to Potemkin AI, a term from researcher Jathan Sadowski.

You can read Astra Taylor's article, The Automation Charade, or watch her discuss fauxtomation at the AI Now 2018 symposium.

Sketchplanations continues to be done by me, by hand. If you'd like to support me, I'm improving my Patreon, which makes such a difference in helping creators like me keep creating. Thank you.

Here's a fauxtomation sketch by ChatGPT in case you're interested =)

As well as fauxtomation prints, you can get AI department prints

Fauxtomation

The Automation Paradox is that the better our machines get, the more we struggle when they fail.

When heading out for a hike in the woods, it's tempting to skip the map and compass and rely on our phones and apps for navigation. Yet when we encounter no signal or lose power, we can find ourselves in a sticky situation. Or perhaps, like me, you've come to rely on popping your destination into the satnav or Google Maps whenever you get in the car and have nearly forgotten the ability to navigate without it.

These situations illustrate the paradox of automation, where the more sophisticated and automated our machines and technologies become, the more bewildered we find ourselves when they inevitably fail. Or: the smarter the machines get, the dumber we might get.

In his book Messy, Tim Harford suggests three strands to the paradox as our machines get more sophisticated:

Automation covers up our mistakes, hiding our incompetence, meaning we may not learn to correct ourselves—consider autocorrect cleaning up our typos as we go.

When we rely on automation, we get less practice for our skills, so even highly skilled individuals may find their expertise diminishing—perhaps you've found yourself using your phone calculator for a trivial calculation.

When the easy scenarios are taken care of, failures may occur in complex or unpredictable ways that we may find especially difficult to recover from—like a subtle but persistent failure in the steering of a passenger plane, recovering from a skid on an icy road, or when you're deeply lost in the wilderness.

More sophisticated technology can even make it useless or more dangerous when it fails. Older cars used to be reparable forever. Now, if your vehicle fails, it's likely to need plugging in at the dealership to figure out what's up. How many electronic devices are thrown away because somewhere inside, some tiny loose connection or component makes the whole thing worthless? Were pilots and flight crews better prepared and able to improvise before the autopilot became ubiquitous?

I often wish our devices would fail more like an escalator or an electric toothbrush. If an escalator fails, you can still walk up it. If your electric toothbrush dies, you can still use it to brush your teeth. These could be called Technology-Enhanced Products, perhaps. But when most of our devices die, they're often rendered worthless.

Automation and sophisticated machines help me so much. I did use Grammarly to help check this post. I use Google Maps nearly every time I put in my destination to home, and I often use a calculator to check my maths. But I do pay attention to grammar corrections, bring a paper map when I can, and keep trying to do the maths in my head.

But as Tim Harford explains it, we still face "the paradox of automation: the better the machines get, the more bewildered we are when the machines fail."

The Dunning-Kruger effect (send me proof of purchase of Big Ideas Little Pictures, and I'll send the sketch to you)

Tolkein-style landscape inspired by the excellent Lord of Maps.

The Automation Paradox

Jevon's paradox is that improvements in fuel efficiency tend to increase fuel use, not decrease it as you might expect.

In 1865, a chap named William Stanley Jevons noted that while there had been a number of improvements in coal efficiency in steam engines and the like, the effect was to increase consumption instead of reduce it. This observation became known as Jevon's paradox.

While it’s not a surefire outcome, there are plenty of parallels like more efficient batteries leading to more overall battery use, washing machines leading to doing more washes rather than less time spent washing, or the ability to recycle some plastics leading to more overall plastic consumption.

In her book More Work for Mother, Ruth Cowan addresses the ironies of household technology and how improvements in the efficiency of household devices often led to more work, not less.

This is sometimes known as the rebound effect.

Jevon’s Paradox

Big data + Machine learning + Cloud = Informed, intelligent decisions. As a very minor example of this I have been enjoying the Dark Sky app.

The big data equation

Bill Buxton’s Long Nose of Innovation model suggests that for any “wow” moment of new technology—using the mouse on a personal computer, touchscreens, haptics or self-driving cars—there has typically been a long period of low-amplitude invention, refinement and augmentation, often for 20 years or more. Technologies such as the computer mouse need polishing, adjustment, optimization and often the development of an entire ecosystem to hit the big time.

The Long Nose of Innovation helps us understand the process and impact of innovation.

One intriguing consequence of the theory is that the technologies that will have a substantial impact in the coming five years are likely 15 years old already.

The next time you’re wowed by technology, consider the decades of small incremental improvements that have been made before it reached you.

Bill Buxton shares an article he wrote in BusinessWeek about the Long Nose of Innovation on his website (pdf). The theory is a near mirror image of Chris Anderson’s theory of The Long Tail, where sales of a small number of a very large selection, particularly in the digital realm with minimal storage costs, can turn the “top hits” and “blockbuster” models of business on its head. More about the long tail in Chris Anderson’s The Long Tail: Why the Future of Business is Selling Less of More (Hyperion, 2006).

This sketch, lovingly polished from the original, features in my book Big Ideas Little Pictures

AI sketches