What We’re Reading

Designer Anton Sten ponders the future role of digital designers in a world of more and more Alexas, Siris, and other non-visual interfaces. His conclusion is that much more of our work will be about designing for what goes wrong:

As technology offers us more and more options and possibilities, our work as UX-designers will grow to include even more edge-cases. As our acceptance of friction with these services continues to decrease, our work will increasingly need to include more ‘what if’ scenarios.

I agree, and I’m excited about this. Instead of etching buttons and controls for flows that we wholly control, I see our work evolving into the anticipation of scenarios that spin out of machine-generated content and interaction.

How will we handle the weird, the odd, the unexpected, and the wrong? These are exciting challenges, and they mean designing the experience and expectations around the interaction that the machines themselves create. Among other things, we have to help systems be smart enough to know when they’re not smart enough.

So happy and proud for my studiomate Lori Richmond, a marvelous illustrator who’s also become an impressive runner over the last several months. Only a year after taking up running, Lori was just featured in Runner’s World—for her inspired illustration project.

Here’s the concept: after every training run, Lori draws or paints a scene she saw. And she executes it in exactly the time it took her to finish that run.

Short runs get quick impressions:

…while long runs get tons of attention and detail:

Lori’s been posting all of these at her @loririchmonddraws Instagram account. And Runner’s World took notice, which is super-fun recognition for a new runner who’s already logged four half marathons. (Her goal is to run a half marathon in each of New York’s five boroughs.)

“I got kicked out of gym in the 6th grade because I was SO un-athletic,” Lori posted in our studio Slack channel. “The art kids will always come back for you!!”

At An Event Apart today, Brad Frost announced the release of “The Style Guide Guide,” a boilerplate template for building style guides for design systems. It’s a terrific starter kit, cribbing the information architecture we’ve found to be most successful in our work on big enterprise design systems.

The Style Guide Guide imports and displays design-pattern HTML from a separate pattern library. Anytime there’s a change in the underlying code of the patterns, the style guide picks it up—always up to date. From there, The Style Guide Guide mixes in your documentation, usage guidelines, and design principles. Because those are entered in Markdown, it’s easy for a whole team to contribute documentation and guidelines, with a very low technical barrier to entry

Brad, Dan, Ian, and I have been using The Style Guide Guide alongside Pattern Lab in our last few design-system projects. It’s proven to be a highly collaborative environment for creating and sharing a design system. (Stay tuned, Brad promises a blog post to walk you through the integration with Pattern Lab, very cool.)

Albert Wenger writes that concerns about “black box” algorithms are overwrought. (See here and here for more about these concerns.) It’s okay, Wenger says, if we can’t follow or audit the logic of the machines, even in life-and-death contexts like healthcare or policing. We often have that same lack of insight into the way humans make decisions, he says—and so perhaps we can adapt our current error prevention to the machines:

It all comes down to understanding failure modes and guarding against them.

For instance, human doctors make wrong diagnoses. One way we guard against that is by getting a second opinion. Turns out we have used the same technique in complex software systems. Get multiple systems to compute something and act only if their outputs agree. This approach is immediately and easily applicable to neural networks.

Other failure modes include hidden biases and malicious attacks (manipulation). Again these are no different than for humans and for existing software systems. And we have developed mechanisms for avoiding and/or detecting these issues, such as statistical analysis across systems.

Ben Thompson reacts to Google’s latest effort to bury fake news and hate speech. In particular, he throws a flag on Google’s plan to favor “authoritative” sources—and especially on the fact that Google will almost certainly not reveal what grants a site this privileged status.

Google is going to be making decisions about who is authoritative and who is not, which is another way of saying that Google is going to be making decisions about what is true and what is not, and that demands more transparency, not less.

For better or worse, of course, Google is our de facto truth machine. Most of the world turns to its search engine to answer a question. That’s what makes this whole situation so thorny: as the world’s primary source for facts, Google must be more discerning than it is now. And yet the act of being more discerning amplifies its influence even more.

Perhaps the most unanticipated outcome of the unfettered nature of the Internet is that the sheer volume of information didn’t disperse influence, but rather concentrated it to a far greater degree than ever before, not to those companies that handle distribution (because distribution is free) but to those few that handle discovery.

Jorge Arango conjures Gall’s Law, the 40-year-old dictum of systems design that remains as relevant as ever:

“A complex system that works is invariably found to have evolved from a simple system that worked. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over with a working simple system.”  — John Gall

Every ambitious project launches amid a thicket of fears and grand hopes. The worst thing you can do is try to design for all those assumed outcomes (let alone the edge cases). Start with a sturdy but simple system and build from there as you learn. As Jorge writes, that’s the appeal (and necessity) of the MVP:

When the product is real and can be tested, it can (and should) evolve towards something more complex. But baking complexity into the first release is a costly mistake. (Note I didn’t say it “can be”. It’s guaranteed.)

“I’m not going to be the guy who ruined About.com,” About.com CEO Neil Vogel told Business Insider. “It’s already ruined, so this is all upside here.”

Vogel was referring to his plans to retire the About.com brand next week, on May 2. About.com is one of the most venerable Internet properties out there, over two decades old and still one of the top 100 by traffic. The content will live on, but across several different verticals, none of which will carry the About.com name. About.com is dead; long live About.com.

Shutting down that brand might have the ring of failure, but it turns out it’s a pretty remarkable turnaround story. I’ve been lucky enough to see that turnaround up close.

A few years ago, Google’s algorithm started treating the general-interest site as a content farm, and the site’s search ranking plummeted. At the same time, advertisers were backing out, preferring more targeted sites over About.com (WebMD, for example, instead of About.com’s Health section). Fortunes were not looking good.

In early 2016, Big Medium teamed up with About.com to create new vertical brands out of About.com content. We crafted the brands, designed the sites, and helped revamp the company’s design process. Over the past year, we designed three verticals and advised on the branding for a fourth. These verticals took About.com’s enormous library of how-to content, dusted it off, and wrapped it in premium, branded sites.

The first one was Verywell, a health vertical:

Health is our most valuable, most-trafficked, biggest vertical, so we came up with an idea. Our content is very much in the style of like WebMD or Everyday Health. But we thought those sites, we just didn’t think they have served a market need. We thought that we could make a beautiful, kinder, gentler health site. You go to these some of other sites with a headache, you think you have a brain tumor. You come to us with a headache, we’re going to make your headache feel better and explain why you had a headache and make it better. That was the thesis.

So we took our 100,000 pieces of health content of About.com, threw 50,000 in the garbage because they were old. We didn’t like them. The other 50 [thousand] were read by our writers. If it was medical information; it was read by a doctor. We had 30,000 pieces of content read by physicians, edited, cleaned up. Built a brand-new site from scratch, a new taxonomy for our content, put it on the site.

We did that. We built this beautiful new site from scratch, everything from scratch.

Together we created the new brand, cleaned up the information architecture, and importantly got rid of a ton of cheap advertising. With fewer ads per page and a new premium brand, traffic skyrocketed and revenue soared.

I think we had 8 million uniques when we started a month, I think we have 17 million uniques now to Very Well. So we’ve pretty much doubled in size in 12 months. We’re by far the fastest-growing thing in the health space. I think we’re No. 4 or 5 on comScore on health because our bet was right …

We knew that this would work. Then we launched something in the summer. Ran a very similar playbook on our personal-finance content called The Balance, which has pretty much doubled in traffic since we launched it this summer. We launched something called Lifewire in November, which is our evergreen-content tech site — how to fix my router, how to unbrick my iPhone. We launched three weeks ago, about a month ago something called The Spruce, which is the third-biggest home site on the internet, only behind HGTV and the Hearst Brands. We had such scale on About, that we’re launching these new brands into the world that are new to the space with no legacy issues, look like start ups, but all of a sudden, like we’re top 10 in comScore because we’re coming with such scale. The market’s like, "What? Where did you guys come from?"

It was a treat to work with the whole crew at About.com. There’s a lot of experience under that roof, and it’s been amazing to help release so much pent-up potential.

Vogel says that the About.com name will finally be retired next week to be replaced with a new brand name.

Over at freelance.tv, my pal and collaborator Dan Mall shares the goods on what it takes to be a world-class indie designer. Dan is not only one of the most talented designers I know, he’s also one of the most generous, openly sharing his hard-earned wisdom of making it work in this industry.

Here’s Dan on the early days of starting his design collaborative SuperFriendly:

I figured out what I was really good at, I figured out what I was good at that I didn’t want to do. I figured out what I was bad at. I figured out what I was bad at that actaully clients were asking for, so I should get better at that stuff.…

The ability to be a generalist is really important for a freelancer. When you’re working by yourself, you’re the CEO but you’re also the janitor. You’ve gotta take care of the plants, too.…

There’s an interesting time in the life of a freelancer when you decide, “I want to team up with somebody, or collaborate with somebody, or hire somebody to do the jobs that I’m not particularly good at.”

I’m very honored to be one of those collaborators—and very happy that Dan is so freakin’ good at so many of the jobs that I’m not good at.

Ellen Huet, writing for Bloomberg, peeks in on the worklife of the people who backstop the bots by reviewing answers and frequently stepping in to provide their own. They are the “humans pretending to be robots pretending to be humans.”

Huet talked to people who filled this role at two services that automate calendar scheduling, X.ai and Clara, and I t doesn’t sound like the world’s most fulfilling work:

Calvin said he sometimes sat in front of a computer for 12 hours a day, clicking and highlighting phrases. “It was either really boring or incredibly frustrating,” he said. “It was a weird combination of the exact same thing over and over again and really frustrating single cases of a person demanding something we couldn’t provide.”

[…]

As another former X.ai trainer put it, he wasn’t worried about his job being replaced by a bot. It was so boring he was actually looking forward to not having to do it anymore.

I’m confident that putting people in the bot role is the right way to prototype bot services with very small trial audiences. It lets you hone your understanding of what people actually want and build a good set of training data as well as the voice and tone of the service. But it’s also clear that this kind of work—focusing relentlessly and mind-numbingly on the same narrow micro-interaction—is not meant for long-term job roles.

This is why people are trying to automate this stuff in the first place. The risk is that, during the transition, the tedium of modeling this automation will fall heavily and narrowly on a small group who wind up working for the bots, rather than the reverse. How might we avoid making this the future of work?

BuzzFeed’s Mat Honan takes a world-weary view of Facebook’s unsurprisingly boosterish presentation of new technologies at the company’s F8 show. In particular, he’s disappointed the company didn’t do more to acknowledge the potential for abuse in this new tech:

The problem with connecting everyone on the planet is that a lot of people are assholes. The issue with giving just anyone the ability to live broadcast to a billion people is that someone will use it to shoot up a school. You have to plan for these things. You have to build for the reality we live in, not the one we hope to create. …

Executive after executive took the F8 stage to show off how these effects will manifest themselves in the real world. Deborah Liu, who runs Facebook’s monetization efforts, encouraged the audience to “imagine all the possibilities” as she ran through demos of a café where people could leave Yelp-style ratings tacked up in the air and discoverable with a phone, or a birthday message she generated on top of an image of her daughter, while noting that with digital effects, “I can make her birthday even more meaningful.”

And yet the dark human history of forever makes it certain that people will also use these same tools to attack and abuse and harass and lie. They will leave bogus reviews of restaurants to which they’ve never been, attacking pizzerias for pedophilia. If anyone can create a mask, some people will inevitably create ones that are hateful. …

But Facebook made no nods to this during its keynote — and realistically maybe it’s naive to expect the company to do so. But it would be reassuring to know that Facebook is at least thinking about the world as it is, that it is planning for humans to be humans in all their brutish ways. A simple “we’re already considering ways people can and will abuse these tools and you can trust us to stay on top of that” would go a long way.

I like that. Simply acknowledging potential problems—and stating your resolve in solving them—is a way to make your values clear and to start to bake them into the product and organization, too.

At MIT Technology Review, Tom Simonite writes about Facebook’s efforts to make its automated assistant M answer pretty much any request that comes its way, not matter how obscure. And for a very small group of beta testers, the bot actually works, delivering results so good you’d swear you were talking to a human being. Because you are.

M is so smart because it cheats. It works like Siri in that when you tap out a message to M, algorithms try to figure out what you want. When they can’t, though, M doesn’t fall back on searching the Web or saying “I’m sorry, I don’t understand the question.” Instead, a human being invisibly takes over, responding to your request as if the algorithms were still at the helm. (Facebook declined to say how many of those workers it has, or to make M available to try.)

That design is too expensive to scale to the 1.2 billion people who use Facebook Messenger, so Facebook offered M to a few thousand users in 2015 as a kind of semi-public R&D project. Entwining human workers and algorithms was intended to reveal how people would react to an omniscient virtual assistant, and to provide data that would let the algorithms learn to take over the work of their human “trainers.”

This is the way I’ve been prototyping chatbots, too: start with simple human-to-human interactions.

I’m a big fan of this kind of prototype that put people where the pipes will eventually go. In a way, Uber is a similar prototype for self-driving cars: until the robots get the go-ahead to drive on their own, we’ll put a human in the driver’s seat and automate the rest of the experience (calling a car, giving directions, paying the tab).

When you’re trying out new interactions for untested or emerging technologies, the best MVP is often no tech at all. Powering a bot with people instead of artificial intelligence gets you early info about what people want, how they respond, and the kind of language to use. It proves out the demand of the service, hints at the shape it should take, and offers training data to give to the bots down the road.

Eventually the AI steps in. At Facebook, they’re still trying to use all that data to train the bots well enough so that they can take over. Simonite shares some of the techniques the M team is using, with mixed results. Even though machine-learning breakthroughs are coming fast and furious, the holy grail of broad and instant natural-language understanding is still tantalizingly out of reach. “Sometimes we say this is three years, or five years,“ M’s leader Laurent Landowski told Simonite. ”But maybe it’s 10 years or more.”

Writing for The Verge, Casey Newton reports that publishers are abandoning Facebook’s Instant Articles format:

Two years after it launched, a platform that aspired to build a more stable path forward for journalism appears to be declining in relevance. At the same time that Instant Articles were being designed, Facebook was beginning work on the projects that would ultimately undermine it. Starting in 2015, the company’s algorithms began favoring video over other content types, diminishing the reach of Instant Articles in the feed. The following year, Facebook’s News Feed deprioritized article links in favor of posts from friends and family. The arrival this month of ephemeral stories on top of the News Feed further de-emphasized the links on which many publishers have come to depend.

In discussions with Facebook executives, former employees, publishers, and industry observers, a portrait emerges of a product that never lived up to the expectations of the social media giant, or media companies. After scrambling to rebuild their workflows around Instant Articles, large publishers were left with a system that failed to grow audiences or revenues.

Building a business on top of someone else’s platform offers little control or visibility—and ties your fortunes to their priorities, not your own. Newton writes that many publishers are instead throwing in with Google’s AMP platform, which feels like a frying-pan-to-fire maneuver.

David Weinberger considers what it means that machines now construct their own models for understanding data, quite divorced from our own (more simplistic) models. “The nature of computer-based justification is not at all like human justification. It is alien,” Weinberger writes. "But ‘alien’ doesn’t mean ‘wrong.’ When it comes to understanding how things are, the machines may be closer to the truth than we humans ever could be.”

The complexity of this alien logic often makes it completely opaque to humans—even those who program it. If we can’t understand the basis of machine-delivered “truths,” Weinberger suggests, they become categorically different from what we’ve always considered to be “knowledge”:

Clearly our computers have surpassed us in their power to discriminate, find patterns, and draw conclusions. That’s one reason we use them. Rather than reducing phenomena to fit a relatively simple model, we can now let our computers make models as big as they need to. But this also seems to mean that what we know depends upon the output of machines the functioning of which we cannot follow, explain, or understand. … If knowing has always entailed being able to explain and justify our true beliefs — Plato’s notion, which has persisted for over two thousand years — what are we to make of a new type of knowledge, in which that task of justification is not just difficult or daunting but impossible? …

One reaction to this could be to back off from relying upon computer models that are unintelligible to us so that knowledge continues to work the way that it has since Plato. This would mean foreswearing some types of knowledge. We foreswear some types of knowledge already: The courts forbid some evidence because allowing it would give police an incentive for gathering it illegally. Likewise, most research institutions require proposed projects to go through an institutional review board to forestall otherwise worthy programs that might harm the wellbeing of their test subjects.

This is super-intriguing: what are the circumstances where the stakes are so high that we simply can’t allow ourselves to trust the conclusions of our machines, not matter how confident we may be in the algorithm? When it comes to “forbidden” areas of machine-learning models, Weinberger points out credit agencies are already forbidden from tying certain predictive models to credit scores. If the machines decide that certain races, religions or ethnicities are prone to lower or higher credit scores, for example, credit agencies are legally forbidden from acting on that info.

The reason this is a dangerous area is because the machines’ conclusions are only as valuable as the training data we feed to them. And that training data depends on the perspective (and bias) of the folks who collect it:

For example, a system that was trained to evaluate the risks posed by individuals up for bail let hardened white criminals out while keeping in jail African Americans with less of a criminal record. The system was learning from the biases of the humans whose decisions were part of the data. The system the CIA uses to identify targets for drone strikes initially suggested a well-known Al Jazeera journalist because the system was trained on a tiny set of known terrorists. Human oversight is obviously still required, especially when we’re talking about drone strikes instead of categorizing cucumbers.

We’re still in the early days of what this oversight and machine-human partnership might look like, but we’re going to have to learn fast. Machine learning has suddenly become inexpensive and accessible to a whole range of organizations and uses, and we see it everywhere. This revolution has revealed the complexity of everyday systems at the same time that it’s let us cut right through them through the capacity and speed of modern computing—even if we don’t understand how we got there.

Where once we saw simple laws operating on relatively predictable data, we are now becoming acutely aware of the overwhelming complexity of even the simplest of situations. Where once the regularity of the movement of the heavenly bodies was our paradigm, and life’s constant unpredictable events were anomalies — mere “accidents,” a fine Aristotelian concept that differentiates them from a thing’s “essential” properties — now the contingency of all that happens is becoming our paradigmatic example.

This is bringing us to locate knowledge outside of our heads. We can only know what we know because we are deeply in league with alien tools of our own devising. Our mental stuff is not enough.

Frank Chimero mulls the beauty of the plain and the normal in design. I like the implicit humility Frank suggests in designs that root their beauty in the quiet satisfaction of their function—not “an overly accentuated, hyper-specific identity”:

I am for a design that’s like vanilla ice cream: simple and sweet, plain without being austere. It should be a base for more indulgent experiences on the occasions they are needed, like adding chocolate chips and cookie dough. Yet these special occassions are rare. A good vanilla ice cream is usually enough. I don’t wish to be dogmatic—every approach has its place, but sometimes plainness needs defending in a world starved for attention and wildly focused on individuality. Here is a reminder: the surest way forward is usually a plain approach done with close attention to detail. You can refine the normal into the sophisticated by pursuing clarity and consistency. Attentiveness turns the normal artful.

Designer Yury Vetrov collected this wide-ranging set of algorithm-driven design projects. Spin through for a glimpse at the emerging role of machine learning in everyday digital design.

Examples include automated web designs from The Grid CMS and Wix, as well as the machine-generated page layouts at Vox and Flipboard. There are also bot-built logos, type pairings, image generators, content-aware photo croppers, and more.

Lots to see and learn here about how designers will collaborate with our robot overlords.

Tim Wu writing for Wired:

Consider, for example, the “innovation” known as Gas Station TV—that is, the televisions embedded in gasoline pumps that blast advertising and other pseudo-programming at the captive pumper. There is no escape: as the CEO of Gas Station TV puts it, “We like to say you’re tied to that screen with an 8-foot rubber hose for about five minutes.” It is an invention that singlehandedly may have created a new case for the electric car.

Attention theft happens anywhere you find your time and attention taken without consent. The most egregious examples are found where, like at the gas station, we are captive audiences. In that genre are things like the new, targeted advertising screens found in hospital waiting rooms (broadcasting things like “The Newborn Channel” for expecting parents); the airlines that play full-volume advertising from a screen right in front of your face; the advertising-screens in office elevators; or that universally unloved invention known as “Taxi TV.”

What to do about ad screens that are imposed on us in these captive scenarios? Wu suggests towns and cities have managed this problem before:

In the 1940s cities banned noisy advertising trucks bearing loudspeakers; the case against advertising screens and sound-trucks is basically the same. It is a small thing cities and towns can do to make our age of bombardment a bit more bearable.

At MIT Technology Review, Will Knight writes about the unknowable logic of our most sophisticated algorithms. We are creating machines that we don’t fully understand. Deep Patient is one example, a system that analyzes hundreds of thousands of medical records looking for patterns:

Deep Patient is a bit puzzling. It appears to anticipate the onset of psychiatric disorders like schizophrenia surprisingly well. But since schizophrenia is notoriously difficult for physicians to predict, [project leader Joel] Dudley wondered how this was possible. He still doesn’t know. The new tool offers no clue as to how it does this. If something like Deep Patient is actually going to help doctors, it will ideally give them the rationale for its prediction, to reassure them that it is accurate and to justify, say, a change in the drugs someone is being prescribed. “We can build these models,” Dudley says ruefully, “but we don’t know how they work.”

As deep learning begins to drive decisions in some of the most intimate and impactful aspects of life and culture—policing, medicine, banking, military defense, even how our cars drive—what do we need to know about how they think?

As the technology advances, we might soon cross some threshold beyond which using AI requires a leap of faith. Sure, we humans can’t always truly explain our thought processes either—but we find ways to intuitively trust and gauge people. Will that also be possible with machines that think and make decisions differently from the way a human would? We’ve never before built machines that operate in ways their creators don’t understand. How well can we expect to communicate—and get along with—intelligent machines that could be unpredictable and inscrutable?

This is especially important when the machines come up with bad answers. How do we understand where they went wrong? Or to know how to help them learn from the mistake? Knight offers a few examples of how researchers are experimenting with this, and many come down to new ways of visualizing and presenting the logic flow.

This resonates strongly with a key belief I have: the design of data-driven interfaces has to get just as much attention as the underlying data science itself—perhaps even more. If we’re going to build systems smart enough to know when they’re not smart enough, we need to be especially clever about how those systems signal the confidence of their answers and how they arrived at them. That’s the stuff of truly useful human-machine partnerships, and it’s a design problem I find myself working on more and more these days.

One hitch: we humans aren’t always so great at explaining our thinking or biases, either. What makes us think that we can train machines to do it any better?

Just as many aspects of human behavior are impossible to explain in detail, perhaps it won’t be possible for AI to explain everything it does. “Even if somebody can give you a reasonable-sounding explanation [for his or her actions], it probably is incomplete, and the same could very well be true for AI,” says Clune, of the University of Wyoming. “It might just be part of the nature of intelligence that only part of it is exposed to rational explanation. Some of it is just instinctual, or subconscious, or inscrutable.”

Brad Frost tackles the challenge of building a design system that works across different tech frameworks. It’s a nasty and common conundrum.

If your design system or pattern library puts down roots too deeply into any one JavaScript framework—Angular or React, for example—or a specific templating engine, then you’ve created a barrier for teams using a different tech stack. (And you’ve likewise created a barrier for future change, when product teams want to shift to a new and improved tech stack.)

Brad outlines the approach that we’ve followed in the design system work we’ve done together: the core design system should focus on the rendered interface—the HTML, CSS, and presentational JavaScript. It should otherwise be “tech-agnostic” on implementation. Easier said than done, of course:

Of course there’s a lot of work that goes into getting that HTML, CSS, and presentational JavaScript onto a page. That’s why teams reach for different backend languages, templating languages, and JavaScript frameworks. Which is where things get complicated. The evolution of JavaScript has especially made things thorny. since it’s gone from simple UI manipulation (a la jQuery) to full-fledged application frameworks (a la React, Angular, Ember, et al). It can be tough to find to find the seams of where the canonical design system ends and where the tech-specific version begins.

Brad suggests that development teams then build implementation-specific versions of the components that match the recommended rendered output. So you might have a React layer, an Angular layer, and so on. But those implementation details are all carefully segregated from the recommended markup.

The design system itself doesn’t care how you build it as long as the end result comes out the right way. Of course, developers do care how it’s built, and one promise of design systems is to deliver efficiencies there. So organizations should make it a goal for teams to share those platform-specific implementations, Brad writes:

This architecture provides a clear path for getting the tech-agnostic, canonical design system into real working software that uses specific technologies. Because it doesn’t bet the farm on any one technology, the system is able to adapt to inevitable changes to tools, technologies, and trends (hence the placeholder for the “new hotness”). Moreover, product teams that share a tech stack can share efforts in maintaining the tech-specific version of the design system.