Curated lists of recommendations – for products, programmes, or even services – make users’ decisions easier by limiting the options available to them. But is offering fewer choices necessarily the same thing as creating good user experiences? When an increasing proportion of what users see at all is determined by algorithms, how can we as UX professionals make sure that these experiences continue to be as valuable for users as they are for online retailers? In this session, we will consider how insights from psychology, behavioural economics and workshop participants' own experiences can help us evaluate, design - and even make better choices ourselves.

1. Designing Decisions
Jessica Cameron
Lead User Researcher
Twitter @jessscameron
12 June 2019

3. (My)
agenda
• We do not make decisions in
rational ways
• Technology is changing how
we make decisions
• Can we help make sure that
these changes are improving
the user experience?
• Or at least not harming it?

5. Decision making is hard.
Our job is to make it easier.
Photo by Tyler Nix for Unsplash

6. How we make decisions

7. System 1 System 2
Information processing
Photos by Goh Rhy Yan and Dan Gold on Unsplash

8. We are given a set of options
We need to be able to process
and evaluate those options
We need to figure out which of
those options is the best
We’re only human…
Photo by Daniel Cheung on Unsplash

9. 1. We are given a set of options
“If you control the menu,
you control the choices.”
Tristan Harris

10. Time Cost CO2
1:20 £68 193 Kg
4:20 £94 24 Kg
1. We are given a set of options

11. 1. We are given a set of options

12. Working memory is limited in
capacity and duration
It suffers when people are
distracted by irrelevant
information
Hick’s Law: The time it takes to
make a decision increases with
the number and complexity of
choices
2. We need to process and remember those options

13. 2. We need to process and remember those options

14. 2. We need to process and remember those options

15. 2. We need to process and remember those options

16. We find it easier to make
decisions when we can see the
information in front of us.
Alternate presentations of
information need to rely on
different strategies.
2. We need to process and remember those options

17. 3. We need to figure out which of those options is the best
Photo by Victoriano Izquierdo on Unsplash

18. Design features

19. Design features

20. Decoy options

21. Decoy options

22. Decoy options

23. Decoy options

24. Defaults

25. Defaults: Vulture

26. Defaults: Gap

27. Social influence

28. Recommendations: Amazon

29. Recommendations: Booking.com

30. Social influence: Booking.com

31. Photo by pixpoetry on Unsplash
Autonomy and free will
We resist losing control
Best to use nudges, not
sledgehammers
But user beware: choices may not
be choices at all

32. Photo by Matt Noble on Unsplash

33. Changing technology

34. Photo by Matt Noble on Unsplash
We don’t even have to choose our own clothes anymore

35. Photo by Matt Noble on Unsplash
We are living in the future

36. Photo by Matt Noble on Unsplash
We are living in the future
“Data is the foundation for virtually
every part of our business. From the
playlist personalization engine to the
ad targeting capability, we have
turned even more to data science.
Every year brings billions of more
listening hours with the associated
additional preference information,
adding more and more precision to
our song-selection.”
Tim Westergren, Letter to
Shareholders, 2016

37. We are living in the future

38. Photo by Matt Noble on Unsplash
Who do we trust to make these decisions for us?
Photos by Tim van der Kuip on Unsplash, MarketWatch/Everett Collection

39. Photo by Matt Noble on Unsplash
Example: Facebook

40. Example: Netflix
We are given a set of
options
We need to be able
to process and
evaluate those
options
We need to figure
out which of those
options is the best

41. Example: Netflix
We are given a set of options
“Netflix has identified over 2,000 taste
communities. In one group are subscribers who
streamed House of Cards and also It’s Always
Sunny in Philadelphia.”
https://www.buzzfeednews.com/article/nicolenguyen/netflix-
recommendation-algorithm-explained-binge-watching

42. Example: Netflix
We need to be able to process and evaluate
those options
British Movies based on a book from the 1980s
Campy Horror Movies
Comic Book and Superhero Movies for ages 11
to 12
Foreign Vampire Movies from the 1970s
Understated Biographical Documentaries
https://gist.github.com/blech/7818890

43. Example: Netflix
We need to figure out which of those options is the best
https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76

44. Example: Netflix
And then once we’ve started, we
make the easy decision to
continue…
Even if we know it’s not in our best
interests

45. Example: YouTube

46. Example: Twitter

47. Activity
Defining our research questions
xx

48. How can we do user research that will help us balance the pros and cons of ‘supported’
decisions?
What matters most to users?
• Is it useful?
• Is it helpful?
• Is it trustworthy?
• Is it understandable?
What are the questions we can ask?
https://www.nngroup.com/articles/machine-learning-ux/
How can we keep the user at the centre of technology?

49. Choose any service (website or app) you wish to evaluate – your own, one that you are
familiar with, or one you’ve seen for the first time today.
Some suggestions:
• Any of the examples from this talk
• Netflix, YouTube, Twitter, Amazon, Booking.com, LuckyTrip, Stitch Fix…
• eBay
• Hopper
• Google Trips
Which services can we consider?

50. USEFUL
Are users guided towards the
best choices for them, or not?
Are inputs and outputs
transparent or hidden?
HELPFUL TRUSTWORTHY UNDERSTANDABLE
Research Map
Does it make users
comfortable or nervous?
Does it save users time, or
cost them time?
Designing Decisions workshop | UX Scotland 2019 | Jessica Cameron | @jessscameron

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Thank you
Jessica Cameron
@jessscameron