Insight 2017: From fake news to cashless economy, problems designers should be trying to fix

Thanks to the efforts of Steve Jobs (including this quote), Jony Ive and unnamed countless others, design has never been a larger part of the public lexicon, or of corporate culture, than it was in 2016. Nearly every consumer company spends endless time and resources on creating well-designed products and services. But at the same time, as a designer, it’s hard to shake the feeling that we need to be doing more. More than creating a better, blacker iPhone (really, Apple?); more than dreaming up addictive new ways to catch Pokemon; more than designing yet another oh-so-fashionably-retro mid-century style lounge chair.

I don’t think I’m the only one who feels that way, so here’s a list of some of the most important problems that designers should be trying to solve in 2017 (and by designers, I mean all types — graphic, product, UX, researchers, content creatives — the whole lot).

Distinguishing between Fake and Real News

“A lie can run around the world while the truth is still putting its boots on.” The late Terry Pratchett said this about fake news long before 2016, but this is an issue that exploded into prominence recently thanks almost entirely to the US Elections. Skip for a moment the question of the sources, their motivations, and the questionable morality of East European teenagers writing complete fabrications to generate clicks and ad revenue. After all, the internet has always been the great equaliser for people to get their opinions heard. The design question here is how difficult it’s become to tell the difference between a rant from someone with an opinion and a computer, and a well-researched, reliable newspaper story.

 Insight 2017: From fake news to cashless economy, problems designers should be trying to fix

The fake news issue exploded into prominence recently thanks almost entirely to the US Elections. Reuters

One problem is that web-design has matured to a point where it’s almost effortless to create a slick, professional looking website from a template (which was certainly not the case even 10 years ago. Anyone remember Maddox — author of the self-proclaimed best page in the universe? Nobody would ever confuse his site for the NYTimes). And while many fake news sites have large obnoxious ad banners that betray their shady origins, the sad truth is that plenty of genuine newspapers struggle to eke out an existence, resorting to very similar tactics (which are particularly prevalent on several Indian media websites).

Another problem is how the distribution of news has changed — ie the emergence of social media, and Facebook’s ubiquity. Our timelines, generated by Facebook’s algorithms, make it even easier for fake news stories to sit right alongside genuine articles. Sensationalist ‘clickbait’ headlines have become a matter of routine, and since those algorithms measure ‘successful’ articles by their engagement, it’s not surprising in hindsight that the articles that spread farthest are the ones free to use wild headlines with no regard for accuracy.


So what’s the solution? There’s no easy answer, but it’s encouraging to see the steps being taken to combat fake news by Google and Facebook (who, as the default source of information for most people, bear the largest responsibility in making sure it’s accurate). Facebook recently introduced ‘Disputed posts’ — a feature allowing users to flag any suspected fake content for fact checking. Google, meanwhile, began adding ‘fact-checked’ icons to their verified news articles, and corrected a grievous mistake in their search results that had a white supremacist propaganda site show up as the first result for the search question ‘Did the Holocaust Happen?’.

These efforts are only the beginning, and for designers, the most pressing questions will be how we empower people to verify and dispute any suspect information, while still allowing for genuine content to be easily shared. Should engagement and potential virality be the only way that we determine the worth of content, or is there an intrinsic importance to be accurate too?

Break the (Affirmation) Bubble

Another side effect of the way we now primarily get our news from social media is the affirmation bias that it creates. The so-called ‘filter bubble’ is created because the posts that we see on any form of social media are far more likely to echo our viewpoints than to contradict them. This once again comes down to the algorithms used by Facebook and others — we’re more likely to ‘engage’ and share posts we agree with, than anything that might feel more contradictory and uncomfortable.

FILE PHOTO: People holding mobile phones are silhouetted against a backdrop projected with the Twitter logo in this illustration picture taken September 27, 2013. REUTERS/Kacper Pempel/Illustration/File Photo - RTX2QA9X

We’re more likely to ‘engage’ and share posts we agree with, than anything that might feel more contradictory and uncomfortable. Reuters

At first glance, this seems like another aspect of the fake news issue but there are some important differences. For one thing, the filter bubble would be an issue wherever there are artificial boundaries on the information we consume, regardless of whether the specifics were genuine or not. Another key difference is that affirmation bubbles predate the internet itself, and in some ways, can seem almost inevitable. As humans, we tend to associate with people similar to us in some way, whether socially, economically or otherwise, and this usually breeds homogeneity of thought. That’s dangerous though; when everyone around you seems to have the same opinion, you start to think that it’s the only possible view, and automatically marginalise or outright dismiss any contradictory information.

Again, this is far from a new phenomenon, but the scale and reach of social media, coupled with algorithms that curate without us being aware of it, constantly showing us reinforcing viewpoints, can lead to groups of highly polarised people with little empathy for the ‘other side’. The two opposing voter banks during the US election exemplified this; closer to home, similar patterns played out in the drama of India’s ‘demonetisation’.

Fortunately, just because bias is a part of human nature doesn’t mean we can’t combat it using technology and good design. In this (sometimes uncomfortably self-aware) post, BJ May outlines how he personally broke out of his filter bubble, and shares a roadmap to do the same for anyone who wishes to better understand people in the world dissimilar to them. A group of computer scientists have more good news — people may be more open to alternative viewpoints than we think, including the ones most vocal about them; they just need exposure to new ideas.

That’s the challenge facing designers — how can we serve up new ideas to people who aren’t actively looking? How can we go beyond the placid reinforcement of similar groups and encourage the often uncomfortable process of self-discovery? How can we design to create empathy?

Cashless is King

Our demonetisation drama began less than two months ago, but it already seems like forever. No matter your take on the exercise — hard-won triumph, utter farce, or (as I personally believe) a worthy idea with appalling execution, it seems clear that having better design inputs could only have helped the process.

Start with the new Rs 2000 note itself, which has been called an unmitigated design disaster; visual merits aside, the decision to create a note with dimensions that weren’t compatible with existing ATM standards, particularly given the context of needing them distributed urgently, has to stand as one of the biggest design fails of the year, if not the decade — a textbook example of shallow design without considering the real world ramifications.

People wait in lines to deposit and withdraw money. Reuters

People wait in lines to deposit and withdraw money. Reuters

Several aspects of the overall demonetisation rollout exhibited a similar lack of consideration for the finer details which would have had any service designer cringing as policies and information shifted on an almost daily, seemingly random basis. Still, what’s done is done, so it’s time to shift focus to our (glorious? / terrifying?) cashless future.

There’s been enough written about some of the key points of the cashless economy, both positive (convenience, accountable histories, and far less overhead) and negative (vulnerability to hackers and potentially Orwellian surveillance levels), that we can skip over those and concentrate on some of the more interesting service design opportunities.

One major question is the viability of going cashless when there are still significant parts of the population who are unbanked, have little cellular coverage, and/or low literacy. There are certainly alternative solutions possible, from localised, limited use interim currencies, to pictorial representations to convey meaning, but any answer requires a cognitive shift on the part of the people planning the projects.


Two other ‘extreme’ demographics might have similar troubles adjusting to a cashless future. Children, who aren’t usually part of the regular banking system, will need viable replacements for pocket money or for the school cafeteria (though parents may also welcome the chance to monitor their children’s spending more closely). Elderly people will have different challenges, navigating yet another complex electronic interaction that can be confusing and hard to trust.

One of the most significant changes might be cultural. Moving from the physical act of peeling out notes one by one to an entirely digital process could not only transform common rituals like tipping or charitable donations, but also encourage people to spend more freely, making it easier to lose track. Making digital money feel more tangible again is a fascinating problem, and there are potentially interesting solutions out there too.

Country for Old Men (and Women)

Ageing populations have long been a key driver of design in several countries, particularly in Europe and Japan, but it’s an issue that we haven’t really had to consider. When the average life expectancy in India is only 66 years (as opposed to 78 in the USA, 79 in Europe, and an astounding 83 in Japan), why would we, right? There are the cultural differences too — it’s far more common for Indian parents to live with their children than in many other countries, and the traditional joint or semi-joint families have always acted as a built-in form of aged care.

A villager goes through the process of a fingerprint scanner during Unique Identification (UID) database system in the Pathancheru village, in Medak district of the southern Indian state of Andhra Pradesh April 27, 2010. As India gears up to build the largest biometric database in the world with the aim of providing most of its 1.2 billion citizens UID, perhaps the biggest challenge is smudged fingerprints. The UID Authority of India will issue the first UIDs linked to a person's demographic and biometric information between August and February, and issue about 600 million such IDs over the next five years to help verify citizens quickly and cheaply. It will be a boon for companies and government agencies alike. Picture taken April 27, 2010. To match feature INDIA-IDENTITY/ REUTERS/Krishnendu Halder (INDIA) - RTR2D8W7

A woman goes through the process of a fingerprint scanner. Reuters

We’re about to see that change dramatically over the next few decades though. Projections say that India’s over-60 population will more than double from 8 percent in 2010, to 19 percent by 2050 (for context, that’s more than 312 million people over 60, just under the entire current population of the US). This coupled with trends in migration and nuclear families effectively means that a lot of elderly people will be forced to live more independently than their parents did. It’s a sobering thought, but on the bright side, it also means huge opportunities for design.

Many of these problems have been solved in other parts of the world, but still need significant work to adapt to an Indian context. For example, Japan’s answers have traditionally relied on the liberal use of technology (and robots because, Japan!) but when designing for a much poorer population, expensive gadgets will only go so far. Similarly, even basic mobility can be an issue for people using walkers and wheelchairs, thanks to a lack of laws and standards on accessibility (which in itself is pretty shocking — come on people, it’s 2016).

In other spheres like interface design too, there’s room to improve the experience of older demographics, by making the text more readable, or using interactions that need less dexterity. It’s a pressing need as daily life moves online, be it grocery shopping or paying taxes.

The great part about designing with empathy for these factors from the start is that improving the experience for a segment with special needs (like the Oxo Good Grips kitchen range, originally developed for arthritic users) usually benefits everyone in the long run.

Improve Human-Robot Relations

Designing to create empathy looks critical in another major aspect of 2017 — the continuing development of Artificial Intelligence. To be clear, this isn’t just about the ‘super-intelligent AI’ that Hollywood has been bracing us for (though clearly, if we could instill a sense of empathy into our future robot overlords, then that can only be a good thing, right?).

While we’re waiting for the revolution though, AI is going to continue spreading further and further into everyday life, whether we’re ready for it or not. The challenge for designers will be to ensure that human psychology and behaviour is an integral part of creating these systems, so they can understand users better and anticipate their implicit needs as well as the explicit ones.

A visitor takes a selfie with Baidu's robot Xiaodu at the 2015 Baidu World Conference in Beijing, China, September 8, 2015. Xiaodu, an artificial intelligent robot developed by Baidu, has access to the company's search engine database and can respond to voice commands, Baidu says.REUTERS/Kim Kyung-Hoon - RTX1RLKS

While we’re waiting for the revolution though, AI is going to continue spreading further and further into everyday life. Reuters

We need to be able to trust our AI assistants, and that trust is going to be strained as they increasingly take on real-world tasks which come with an intrinsic amount of uncertainty and potential for things to go wrong. Research shows that right now we tend to be far less forgiving of mistakes made by a machine than we would for human error, so how do we build a sustainable trust between humans and potentially fallible machines?

One aspect could be in our actual interactions. Screens and buttons have been part of our cultural understanding of computers for decades, and come with an expectation of a relatively mechanical device. But we normally use speech only with living things (even when they don’t necessarily understand us perfectly, like pets) and that relates to a mental state that’s far more forgiving. We can anthropomorphise nearly anything given the chance (Jarvis, the disembodied AI in 2008’s Iron Man, was clearly as distinctive a character as Tony Stark himself), and speaking to our AI assistants is already making us consider them differently from the computer software that came before.

Another goal could be a concerted effort to build more transparency into autonomous systems, so that even in case of error, a user could still see the underlying decisions, and understand the circumstances, much as a human being might own up to their mistake and retain your trust. We’ll also need transparency because AIs perceive and evaluate the world in a very different way from us. Understanding their point of view will be critical in order to teach them the things we want and filter out the human biases we don’t want to pass on. Just as importantly, this understanding will be crucial to potentially learn from their perspective, as the Go players who competed against Google’s AlphaGo did, coming to think about the game in new ways entirely.

We’re some way off from having trusty robot sidekicks (or being a robot’s trusty sidekick) but if we can design AI the right way, we may get a lot closer to a sustainable symbiotic relationship.

Design things that matter

The world always seems to have more problems to solve than people to solve them, but by focusing on the impact of our work and tackling the messy, uncertain issues that nobody but designers can address, we can make things a bit better for everyone. And then next year we can start planning how to colonise Mars!

Updated Date: Jan 01, 2017 08:25:41 IST