Image: Akritasa (cc)

Interaction designersversus algorithms

@gilescolborne

No matter how coolyour user interface is,it would be betterif there were less of it. Alan Cooper

Algorithms and artificial intelligence give us the power to simplify interactions. What does that mean for interaction design practice?

Spotify’s Discover Weekly is one of it’s most delightful and valuable features, according to users I’ve spoken to. But it’s a playlist. If you were the interaction designer, what might you have contributed?

Stuff that the user wants

goes here.

Maybe this sketch?

It looks like the real work was being done by the engineer who wrote the algorithm.

Is that changing the nature of interaction design?

What about something like this. I know people who’ve spent a lot of time figuring out how to help users move through this inofmation.

How might this be redesigned?

Book me an off peak return from Bath to London for next Tuesday with a seat reservation on the way back at 4:30.

Would you like to add a Zone 1-5 Travelcard for £5.80?

Yes plz

That comes to £78.20 including booking fee. Want to go ahead?

Trainline BookingMenu My tickets

OK

Chatbots can answer the same questions in a natural way that feels familiar to users.

The interaction here is with the collection of natural language algorithms beneath the hood.

Here is exactly what you need

right nowSo now I’m wondering, how much design will get displaced by data scientists and algorithm engineers.

Ariel Luenberger

Let’s imagine we’re designing for a bus company.

This chap needs to know ‘where’s my bus?’. How could an algorithm help?

Well, you need to begin with data…

Imagine we have layers of data. We know when buses were late, what the weather was like, locations of roadworks, traffic and so on. We could use it to predict how late your bus will be.

Machine learning is not magic it’s engineering

Well, if you come up with an idea, you need to know enough about algorithms to have a sensible conversation with an engineer. Here are the basics of that conversation…

Here’s the task. You have input data (weather, traffic patterns and so on), an algorithm, and some outputs (is the bus late?).

You need to know what kind of output is useful to the user. Is it enough to say ‘late?’ Or do you need to give a precise delay? More detailed output means a more complex engineering challenge.

The engineer chooses the algorithm and trains it by showing it sample inputs (weather, traffic, etc.) and known outputs (when the bus actually arrived) until the algorithm can fit inputs and ouputs.

If your data is inaccurate (for instance the GPS doesn’t work well in some areas) then your algorithm will learn to make inaccurate predictions. So you need to be able to judge data quality.

If your problem is complex and relies on lots of different data sets, then you’re going to need more training data. That can be hard to get hold of. Engineers will get nervous if you keep adding data sets. So which ones do you really need?

High varianceHigh bias

If you don’t have an accurate algorithm, you can at least choose how to be wrong. Biased consistantly, variable around an average. In our case it’s better to be biased (towards saying the bus will be on time) rather than to be right on average.

If the data in the layers is unnecessarily complex then the algorithm may be unreliable, too. So rather than throw raw data at the algorithm, it’s a good idea to simplify whats in each data set.

Do you need to know precise rainfall times, hour by hour, or just ‘did it rain in the morning’. That affects how much data is in your data set. Sometimes less data gives better accuracy - like turning up the contrast on a scanned image of text to make it more legible.

At the end of this you’ll have a trained algorithm that delivers the information you want based on the data you have. But it may still not be accurate enough. So you’ll need a closed beta or a live service with a feedback loop to keep up the training.

Ariel Luenberger

So we built a prediction machine. All the way through there’s a dialogue between designer and engineer about what’s possible and how to present it.

Perhaps as tools and APIs proliferate, designers will take on the job of training algorithms. But the real place designers add value is in defining what the outputs should be and how they’re presented to the user.

If you wrap up your recommendations in an interface that promises human-like interactions with less than human manners, then people will revolt.

Interfaces like this offer suggestions in a subtler, less pushy way. Designing the etiquette of suggestions will be important in next generation interaction design.

Book me an off peak return from Bath to London for next Tuesday with a seat reservation on the way back at 4:30.

Would you like to add a Zone 1-5 Travelcard for £5.80?

Yes plz

That comes to £78.20 including booking fee. Want to go ahead?

Trainline BookingMenu My tickets

OK

If you’re dealing with natural language interfaces, a lot of the same rules apply.

You need a set of training data - transcripts of call customer service conversations.

You may need simplify that data - for instance by looking for the successulf conversations.

And you need to think how to set users’ expectations about talking to a bot.

The adventure game Lost Pig has you telling an Orc what to do. So you know to keep it simple and expect errors.

It’s cute, has personality and humour, and serves an engineering purpose.

You’ll need to map out conversations as flowcharts. But there’s a lot of copywriting you’ll need to do around those flows to make it feel natural. For instance, you may want to give a long answer the first time someone asks a question and then a shorter summary the second time.

Book me an off peak return from Bath to London for next Tuesday with a seat reservation on the way back at 4:30.

Would you like to add a Zone 1-5 Travelcard for £5.80?

Yes plz

That comes to £78.20 including booking fee. Want to go ahead?

Trainline BookingMenu My tickets

OK

I’ve always looked to human conversation patterns to figure out how to solve interaction design problems.

Now I find that understanding human to human conversation is core design knowledge.

And what about Discover Weekly? Well, a large part of the design work there was about understanding how to package up the service. Playlists were familiar.

And limiting the size of the playlist gave it a feel of a mix tape from a friend, rather than a data dump from an algorithm.

The designers made it feel elegant and approachable.

So our core skills are still important. There’s a rich future for interaction design.

But the journey to evolve our practice and knowledge is just begining.

Image: Akritasa (cc)

Thank you

@gilescolborne