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Friction typically has a damaging connotation in consumer expertise design, nevertheless it truly has many advantages. Its best-known use case is mitigating unintended penalties in high-risk eventualities, but it has a brand new place within the age of synthetic intelligence. Including strategic friction to interfaces can result in profound effectivity beneficial properties in machine studying algorithms. This makes studying to leverage it a precedence for any purposes hoping to stay aggressive.

A typical assumption in consumer expertise design is much less friction makes apps extra pleasant. However in observe, the comfortable path isn’t all the time the smoothest. The time period “friction” within the digital sense normally refers to something that makes experiences cumbersome. It’s an analogy to the bodily resistance that happens when objects work together. Digital friction is available in many kinds, from irritating flows to complicated copy. However loads of eventualities truly profit with a little bit of resistance. Its killer function is mitigating unintended penalties, akin to an unintentional Alexa shopping spree.

You’ve seemingly already encountered intentional friction many instances. Most apps leverage it for damaging actions, account safety, and error dealing with, as really helpful by specialists from Norman Nielsen Group to the journal you’re at the moment studying.

But friction has discovered a brand new calling within the age of synthetic intelligence. When carried out accurately, it will possibly enhance the effectivity of AI programs akin to machine studying algorithms. These algorithms are sometimes used to personalize experiences via predictive suggestions. Some purposes incorporating these algorithms notice that including a little bit of friction to their interface can flip every consumer interplay into a possibility to enhance algorithmic high quality.

Whereas much less friction makes an app smoother, a bit extra could make it even smarter.

Varied examples of the friction in in style apps. (Sources: Discord, MacOS, MailChimp, Slack, Airbnb, GitHub, Lyft, and Venmo) (Large preview)

Friction As A Characteristic

Earlier than venturing down the AI rabbit gap, let’s discover some easy examples showcasing the essential advantages of friction in UX. These are a useful basis to construct off as we ascend into extra complicated purposes for machine studying algorithms. No matter your familiarity, this may floor the next classes in first ideas.

Stopping Unintended Penalties

A typical use for friction is error prevention, the fifth entry in Jakob Nielsen’s list of usability heuristics. In eventualities with the potential for high-cost errors, akin to irreversible deletion, apps typically request affirmation earlier than executing requests. Confirmations typically show in a modal, locking the remainder of the display screen to extend concentrate on copy explaining an motion’s implications. This additional step supplies some additional time to contemplate these ramifications.

“By forcing us to decelerate and suppose at this actual second, we’re stored from making doubtlessly disastrous selections by chance.”

— Archana Madhavan in Amplitude’s “Onboarding With The IKEA Effect: How To Use UX Friction To Build Retention

Generally extra resistance is current when the results could be catastrophic. For example, a affirmation could contain cognitive work akin to typing “DELETE” to submit a deletion request. This degree of resistance is sensible when contemplating the humbling truth of life from Steve Krug’s traditional UX ebook Don’t Make Me Think, which states, “We don’t learn pages. We scan them.” This makes it straightforward to think about how a streamlined design could make it too straightforward to miss the results of a click on.

A generic delete modal example that requires a user to type the phrase “delete account” into an input field to confirm their deletion request
A delete modal that requires cognitive effort to attenuate accidents. (Picture supply: UX Psychology) (Large preview)

Whereas these ways could look comically cumbersome, they mitigate devastating downsides. This use of friction is sort of a prepare’s brakes screeching to a halt proper in time to keep away from a collision — everybody breathes a sigh of reduction, disaster averted. This additionally outlines the essential framework for understanding when so as to add friction. It boils all the way down to a cost-benefit evaluation: do the rewards of streamlining outweigh the danger? If not, sluggish it down. Now let’s transfer on from a black & white instance to enterprise right into a grayer space.

Nudging Towards Wholesome Conduct

Some issues aren’t classifiable as errors however nonetheless aren’t in anybody’s finest curiosity. Attempting to resolve them turns into depraved as a result of there isn’t a proper or unsuitable answer. But that doesn’t make failing to deal with them any much less of an existential threat. Contemplate social media’s medley of knee-jerk, tribalistic habits. It has led many to query the worth of those apps altogether, which isn’t good for enterprise, or society at massive. In an try and encourage extra considerate discourse, these platforms flip to friction.

A prompt displays when a user attempts to retweet an article. The headline reads, “Headlines don’t tell the full story,” and the subheadline reads, “You can read the article on Twitter before Retweeting”. Available actions include an article card that can be clicked to read the article as well as buttons to “Retweet” & “Quote Tweet”
A Twitter immediate nudges customers to learn an article earlier than retweeting it. (Picture supply: Twitter) (Large preview)

Twitter explored adding an extra step that asks individuals to learn articles earlier than retweeting them. This nudge goals to craft a extra reliable expertise for everybody by slowing the unfold of misinformation. Based on their reporting, individuals proven the immediate opened articles 40% extra typically, and a few determined to not retweet it in any case. They constructed on this success by displaying a warning earlier than customers submit messages which include harmful language.

Instagram additionally carried out an analogous function in its combat towards on-line bullying. Adam Mosseri, the Head of Instagram, published a blog post stating that this “intervention provides individuals an opportunity to replicate.” Though particular knowledge isn’t offered, they recommend it had promising results in cultivating a extra humane expertise for his or her communities.

These examples present how sooner is just not all the time higher. Generally we’d like restraint from saying issues we don’t imply or sharing issues that we don’t perceive. Friction helps algorithms in an analogous method. Generally in addition they want extra details about us in order that they don’t suggest issues we gained’t recognize.

Understanding Preferences & Aims

Let’s shift focus to AI with a easy instance of how friction performs a task in machine studying algorithms. You’ve in all probability signed up for an app that begins by asking you a bunch of questions on your pursuits. Behind the scenes, an algorithm makes use of these solutions to personalize your expertise. These onboarding flows have develop into so frequent over the previous decade that you will have forgotten a time earlier than apps have been sensible sufficient to get to know you.

A screen from Twitter’s 2014 onboarding flow with a headline that reads “What are you interested in?” & a subheadline that reads “Choose one or more of the options below, and we’ll suggest some good stuff for you”. Below this text is a list of checkboxes that correspond to categories of content featured on Twitter
Twitter’s onboarding movement in 2014. (Picture supply: (Large preview)

You’ll have by no means even questioned why you need to undergo a desire seize movement earlier than attending to discover content material. The worth is clear as a result of nobody desires the quickest path to one thing irrelevant. Many apps are merely within the enterprise of creating related connections, and these personalization ways have been probably the greatest methods to take action. A McKinsey report illuminates this additional by reporting that “35 % of what customers buy on Amazon and 75 % of what they watch on Netflix come from product suggestions based mostly on such algorithms.”

“The highest two causes that prospects churn are 1) they don’t perceive your product, and a pair of) they don’t get hold of any worth from it. Buyer onboarding can clear up each of those points.”

— Christina Perricone in HubSpot’s “The Ultimate Guide to Customer Onboarding

Maybe these onboarding flows are so acquainted that they don’t really feel like friction. They might appear to be needed steps to unlock an app’s worth. Nonetheless, that perspective shortly modifications for anybody designing considered one of these flows. The inherent rigidity lies in trying to stability the diametrically reverse wants of two events. On the one hand, an algorithm supplies higher output relative to its enter (though asymptotes exist). Success is a perform of maximizing knowledge assortment touchpoints, however this tends to lead to extra steps with extra complicated questions.

Briefly, the faster an app makes a suggestion, the extra seemingly it will likely be unsuitable. However, a particularly lengthy onboarding movement is unlikely to make a tremendous first impression on new customers. I had the pleasure of strolling this tightrope when designing the onboarding movement at Headliner. Every new step we added all the time felt like it will be the straw that broke the camel’s again. We nervously monitored our activation studies for indicators we went too far however surprisingly noticed no significant dropoff. But, even a slight lower would simply be definitely worth the improved retention that personalization yielded.”>
Headliner’s new consumer onboarding movement. (Supply:

The Product Design Supervisor at Sew Repair, Deanna Alcorn, documented their means of working through this. The stress is clearly illustrated when she asks the query, “How will we get prospects to guage as many photos as potential whereas retaining it enjoyable and quick?”. Whereas their case examine is a good reference, the suitable answer will likely be totally different for each app. Your onboarding movement ought to comply with the wants of your algorithm whereas balancing the wants of your customers.

With that mentioned, there may be one app that’s legendary for its speedy personalization, and surprisingly, it doesn’t have any onboarding movement in any respect.

Giving An Algorithm Glasses

TikTok’s personalization is so good that the New York Occasions compares it to mind reading. However after signing up for his or her service, you may simply begin searching! In stark distinction, Instagram has a number of onboarding steps with out the identical algorithmic popularity. How can TikTok have such a bonus if it doesn’t even ask you what you wish to see?

 A sequence of screens sampled from Instagram’s onboarding flow. The first screen has a headline that reads, “Next, you’ll be able to sync your contacts to find your friends,” with a “continue” button and a smaller “skip” button. The second screen reads “Get Facebook suggestions” with a “continue” button and a smaller “skip” button. The third screen reads “Discover people” above a list of popular users that can be followed
Instagram’s quite a few onboarding screens acquire data to enhance their algorithmic suggestions. (Picture supply: Instagram) (Large preview)

That is due to some intelligent interface improvements. TikTok’s design turns consumer engagement into clear indicators they use to tweak their algorithms. Content material suggestion high quality is a direct perform of this, which some confer with as an algorithm’s vision.

Optimizing an app’s key interactions to understand implicit signals makes an explicit means of capturing preferences unnecessary.

Engagement Alerts

Each interplay is a chance to enhance understanding via bidirectional suggestions. An interface ought to present system suggestions to the consumer participating with it whereas additionally reporting to the system how efficiency meets consumer expectations. All the things from button faucets to the absence of motion can develop into a sign. Interfaces that efficiently incorporate this are known as algorithm-friendly.

A diagram illustrated by Maximillian Piras displays a feedback loop: “user engagement” → “signals” → “data collection” → “model training” → “content filtering” → “personalization” → back to “user engagement” in a continuous loop
A suggestions loop in an algorithm-friendly interface. (Picture supply: Maximillian Piras) (Large preview)

A examine by Apple’s Machine Learning Research Department particulars their success in leveraging engagement indicators, which they imagine “present robust indications of a consumer’s true intent,” to effectively prepare a machine studying mannequin via a course of referred to as Reinforcement Learning from Human Feedback. Their outcomes documented “vital accuracy beneficial properties in a manufacturing deep studying system,” that means that an interface designed nicely sufficient to research naturally occurring consumer habits is all that’s wanted to create personalization that seems like thoughts studying.

Instagram truly employs this strategy as nicely, though its strategy is a bit much less cohesive since they appear to be in a perpetual state of transition.


However what precisely makes an interface algorithm-friendly? In TikTok’s case, it was the design determination to solely present one video at a time. That’s proper, friction! By lowering the knowledge density within the viewport at any given time, they elevated their understanding of a consumer’s focus. This localizes interactions (or lack thereof) to particular content material as high quality measures.

Gustav Söderström, the Co-President, CPO & CTO at Spotify has referred to this strategy as “giving the algorithm glasses.” Examine this to the medley of distractions in different feeds, and it’s straightforward to think about which one is best at accumulating knowledge.

Using friction as a tool allows designers to craft an interface that separates engagement signals from noise.

An animation by Maximillian Piras displays an “algorithm vision” comparison of TikTok’s feed with Instagram’s. TikTok provides cleaner signals since only one piece of content remains in the viewport at a time.
Algorithmic visibility comparability of TikTok & Instagram’s house feeds. (Supply: Maximillian Piras) (Large preview)

As we return to my aforementioned framework for evaluating when so as to add friction, we are able to perceive the way it is sensible on this state of affairs. Whereas every interplay could take barely longer, related content material could be discovered faster. The trade-off is sensible since relevance sits atop a consumer’s hierarchy of wants.

Moreover, in case you have been to measure friction over an extended time horizon, you seemingly would discover an expertise with higher personalization feels extra frictionless. It’s because the effectivity in serving to customers discover what they’re in search of would persistently compound (though, once more, asymptotes exist). So every subsequent go to theoretically requires much less work on the consumer’s half, which makes the alternate strategy seem like the cumbersome one.

“The key of why a few of these merchandise are so good at suggestions is just not truly that they’ve higher algorithms. It’s the identical algorithms with a extra environment friendly consumer interface.”

— Gustav Söderström in The Verge’s “Why Spotify wants to look like TikTok

Whereas TikTok popularized this interface, anyone who was single within the final decade could discover a similarity to courting apps. Utilizing directional gestures as engagement indicators dates again to the swipeable card paradigm Tinder introduced in 2012. They, too, restricted the viewport to at least one consequence at a time and used actions to tell subsequent suggestions. However TikTok took it mainstream since not everybody wants a courting app, and people who do will churn as soon as they’ve met somebody.

The outcomes of utilizing this paradigm in on a regular basis leisure led many platforms to repeat it in hopes of the identical algorithmic beneficial properties. The most recent to embark on this journey is Spotify, a lot to the chagrin of their users. In truth, this determination even landed it on Mashable’s checklist of worst app updates in 2023. However Söderström says they don’t have a alternative, and he believes in the long term, the sign readability will make up for any interim backlash due to how a lot faster it will possibly study consumer preferences. Critics fail to understand how necessary these modifications are for Spotify’s future.

In the machine learning age, apps with inefficient interfaces for signal analysis risk becoming uncompetitive.

An animation by Maximillian Piras displays an “algorithm vision” comparison of Spotify’s old & new home feed designs within their mobile app. Their new home feed provides cleaner signals since only one piece of content remains in the viewport at a time.
Algorithmic visibility comparability of Spotify’s previous & new house feeds. (Supply: Maximillian Piras) (Large preview)

Making Lemonade

The explanation this strategy is so highly effective is as a result of compounding nature of fine knowledge. Optimizing indicators for any particular person consumer creates a knowledge community impact that advantages everybody else. It even turns negatives into positives! A person dangerous expertise can mitigate others from encountering the identical, making the system antifragile.

This strategy dates again to 2003 with the introduction of Amazon’s item-to-item collaborative filtering. You might realize it as “prospects who seen this additionally seen this.”

An illustration by Maximillian Piras displays an icon of a user that is connected to three icons representing media files. Each of these media files is then connected to many more media files, which then are recursively linked to many others. This linking of media files continues until the icons are cropped by the edge of the canvas
Merchandise-to-item collaborative filtering. (Picture supply: Maximillian Piras) (Large preview)

This sort of filtering produces high-quality suggestions with restricted consumer knowledge. It does so by constructing relationships between objects to proxy consumer preferences. With solely two to a few knowledge factors, an algorithm can draw connections throughout all the dataset. It successfully piggybacks off earlier patterns which might be comparable sufficient.

This implies an app like TikTok solely wants a couple of swipes earlier than it will possibly make high-probability assumptions about your preferences. That’s why friction is so helpful in algorithm-friendly interfaces. If the preliminary interactions ship clear indicators, then an algorithm can graph a consumer’s pursuits nearly instantly.

Friction In The Future

We started prior to now by reviewing how friction discovered its means into UX toolkits via error prevention and wholesome nudges. Then we moved on to its capability to assist algorithms study consumer preferences and targets. Whereas express onboarding flows are nonetheless in vogue, TikTok is popularizing an interface that makes them pointless by utilizing implicit engagement indicators resulting in vital algorithmic beneficial properties. But the machine studying age is simply starting, and friction is simply accelerating its evolution.

Inverting The Pareto Precept

We’ve centered on algorithms that suggest content material, however extra numerous makes use of of personalization could emerge as a result of newfound capabilities of Giant Language Fashions. These fashions unlock the flexibility to control unstructured data at scale. This permits engagement patterns of larger complexity to be analyzed and productized. The result’s algorithms can suggest way more than media and metadata.

Maybe they will craft utterly customized function units based mostly on our preferences and targets. Think about deciding on results in Photoshop and seeing recommendations akin to “Creators who used this impact additionally used this one.” These capabilities might enhance the utilization of buried options that solely energy customers have a tendency to seek out.

Microsoft is exploring this by adding Copilot to its products. They declare the “common individual makes use of lower than 10% of what PowerPoint can do,” however AI will unlock all that latent worth.

A collage of various Microsoft interface elements that illustrate how users can type their goals into a chat window so generative AI can help them execute tasks. A block of AI-generated text is displayed & a cursor parses values in a dropdown labeled “Adjust”. Each value allows the user to manipulate the generated text based on parameters such as “Text length”.
Microsoft Copilot makes use of LLMs in an try and unlock the 90% of options that almost all customers don’t know exist. (Supply: Microsoft Design) (Large preview)

Utilizing LLMs to create function suggestion engines is a captivating thought. It could permit builders to cease counting on the Pareto Precept for prioritization. Particularly as a result of Joel Spolsky claims the 8020 rule is definitely a fantasy.

“Loads of software program builders are seduced by the previous “80/20” rule. It appears to make plenty of sense: 80% of the individuals use 20% of the options… Sadly, it’s by no means the identical 20%. All people makes use of a special set of options.”

— Joel Spolsky in “Strategy Letter IV: Bloatware and the 80/20 Myth

It could be good if irreducible simplicity in interface design have been solely an influence legislation away, however function creep is difficult to fight when totally different individuals discover worth in several choices. It’s unrealistic to imagine that there’s some golden 20% of options driving 80% of worth. If there was, then why isn’t the Pareto Precept ever utilized to content material?

I can’t think about a group at YouTube suggesting that eradicating 80% of movies would enhance the service. As an alternative, it’s seen as a routing downside: discover the suitable piece of content material for the suitable individual. If machine studying algorithms can suggest options, I hope the worth of friction goes with out saying at this level. The effectivity beneficial properties unlocked by algorithm-friendly interfaces completely apply.

Hallucinations Or Creations

The current inflection level within the functionality of LLMs unlocks a completely new computing paradigm. The legendary UX researcher Jakob Nielsen believes it introduces the first new UI paradigm in 60 years, which he calls Intent-Primarily based Final result Specification. As an alternative of telling computer systems what to do, we now clarify an end result to allow them to decide how to realize it.

Utilizing machine studying algorithms to suggest options is one instance. One other pretty new instance that you simply’re seemingly accustomed to is chatbots like ChatGPT. Lots of of tens of millions of individuals already use it, which is a testomony to how out of this world the expertise is. But therein lies an issue: typically its responses actually aren’t grounded in actuality as a result of it tends to make them up! This isn’t apparent to these unfamiliar with the expertise’s internal workings since there aren’t many safeguards. Because of this, some individuals develop into dangerously overreliant on its unverified output.

In a single case, a lawyer based legal arguments on research from ChatGPT solely to seek out out in courtroom that a number of cited sources turned out to be utterly nonexistent. The lawyer’s protection was that he was “unaware of the likelihood that its content material may very well be false.” Examples like this reinforce the significance of friction in stopping unintended penalties. Whereas ChatGPT’s empty state mentions its limitations, they clearly aren’t said explicitly sufficient for everybody.

The empty state for ChatGPT displays three columns of text above the input field. The columns are titled “Examples”, “Capabilities”, & “Limitations”. Within each column, there are three rows of text blocks, all displayed in the same font size, color, & weight
ChatGPT’s empty state mentions its limitations, however the lack of textual content hierarchy on this design means many customers are prone to overlook them. (Picture supply: ChatGPT) (Large preview)

Additional steps and prompts, akin to these talked about earlier, might higher educate customers about what’s known as a “hallucination.” It’s a phenomenon of chatbots confidently outputting responses that don’t align with their coaching knowledge. Much like telling a lie if you don’t have an accurate reply, though that characterization overly anthropomorphizes the software program.

But some see hallucinations as extra of a function than a bug. Marc Andreessen, the co-founder of Netscape, states during an interview that “one other time period for hallucination is simply merely creativity.” He views it as a big evolution from the hyperliteral programs of the previous as a result of they will now brainstorm and improvise.

The issue is that chatbot interfaces are usually simplistic by trying to be one dimension matches all. Extra controls or modes would educate customers about accessible output sorts to allow them to specify which they anticipate. Generally we might want an imaginative response from a inventive companion. Different instances we would like the hyper-accuracy of a deterministic calculator, akin to ChatGPT’s Wolfram plugin.

Maybe a creativity slider or persona selector much like Maggie Appleton’s exploration will higher align the system to consumer wants. Nonetheless it’s carried out, a little bit of friction can maximize advantages whereas minimizing dangers.

Discovering Your Friction

We’ve lined utilizing friction for easy error prevention to complicated algorithm optimizations. Let’s finish with a couple of suggestions that make implementing it as clean as potential.

Peak-Finish Rule

When including resistance to an expertise, the Peak-Finish Rule is a helpful psychological heuristic to leverage. It’s rooted in studies by Daniel Kahneman & Amos Tversky, the place they discovered that notion of painful experiences doesn’t are likely to correlate with length. It’s the height & finish of the expertise that topics recall.

In observe, experts suggest that delight is a perform of optimistic emotional peaks and rewarding emotional payoffs. Optimizing for the height & finish supplies room to shift focus from time spent and steps taken as efficiency indicators; lengthy and sophisticated experiences can nonetheless be pleasant if designed accurately.

A chart titled “Peak-End Rule” by Maximillian Piras displays “emotion” on the y-axis and “time” on the x-axis. A trend line is drawn that begins below the midway point of the y-axis labeled with a neutral face emoji. The trendline shoots upward to an all-time high point located two-fourths through the graph and labeled with a happy face emoji. The trendline drops to its lowest point, located three-fourths through the graph and labeled with a sad face emoji. The trendline ends closer to its all-time high, labeled with a happy face emoji
The Peak-Finish Rule states that experiences are usually recalled by the emotional peaks & finish state versus their full length. (Picture supply: Maximillian Piras) (Large preview)

Maps Aren’t Territories

Individuals expertise friction emotionally, however builders see it as a worth on a chart. In the identical means {that a} map is just not a territory, this ratio is simply an approximation of the particular expertise. It’s one thing to contemplate when evaluating any methods for including or eradicating friction. Since purposes are complicated ecosystems, any measurements ought to contemplate a holistic view. Each step has second-order effects, which makes one-dimensional measurements liable to blind spots.

For instance, when a unsuitable file is deleted, the info can’t report individuals cursing at their laptop display screen. Neither is it prone to embody the context of them opening a brand new file simply to recreate their previous file from scratch. The identical subjectivity applies to all situations of friction. For example, are your studies geared up to measure the trade-off of an motion that takes longer however leads to higher knowledge assortment? It’d enhance algorithmic effectivity, which compounds throughout a neural community.

As we’ve mentioned, higher suggestions are likely to yield higher retention, which tends to yield extra income if a enterprise mannequin aligns with utilization. Myopic measurements will miss a majority of these beneficial properties, so make certain to research friction in a means that actually issues.

Maintain Pushing

As software program is consuming the world, AI is consuming software program. If it’s a paradigm shift as huge as social, cell, and even the net, then purposes should adapt or die. If you wish to stay aggressive within the machine studying age, then don’t worry friction.

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