In this session, I explain why user experience design for artificial intelligence matters. How you make machine learning transparent to users is one of the great design challenges of our time—but a necessary one.

1. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
2 / 2
© Lawrence Lek

2. „H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
MACHINE LEARNING WON’T REACH ITS POTENTIAL
– AND MAY ACTUALLY CAUSE HARM –
IF IT DOESN’T DEVELOP
IN TANDEM WITH USER EXPERIENCE DESIGN.
Caroline Sinders, Fast Company

3. What are the challenges for UX?
© Lawrence Lek

4. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Develop relevant use cases

5. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Spotify - Discovery Weekly
Discovery Weekly is an automated music
recommendation digest for each Spotify user every
monday. It uses a feedback loop mechanism to
personalize, optimize or automate the existing
service.

6. Spotify - Discovery Weekly
© Fabien Girardin

7. Spotify - Discovery Weekly
© Fabien Girardin

8. „H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S SMACHINE LEARNING WILL CHANGE CUSTOMER
PERSONAS FOREVER
Andre Smith, Digitalist Magazine / SAP

9. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Re-think customer personas

10. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Re-think customer personas
Thanks to machine learning, computers will soon know your
customers better than your customers know themselves.
• They’re much better at „guesswork“ than humans are
• More efficient targeting of new customers
• More cost-effective

11. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Create intuitive AI interfaces

12. „H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
THE FUTURE OF MACHINE LEARNING
IS COMING UP WITH A HYBRID LANGUAGE THAT
BRIDGES DESIGN AND ENGINEERING.
Caroline Sinders, Fast Company

13. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Make tons of data manageable

14. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Make tons of data manageable
Big data techniques and analytics changed the way that
businesses conduct their everyday operations. The sheer
volume of data is where Deep Learning algorithms come in to
deliver superior insights.

15. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Use data to be super-relevant or be silent

16. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Let users tell about poor information

17. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Let users tell about poor information
For example in banking, one could consider the temporal evolution of
account balances to segment savings behaviors. This type of
algorithms that leads to decision-making needs to learn to be more
precise.
It’s the designer’s job to find ways to let users tell implicitly or
explicitly about poor information.

18. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Design for discovery
• Filter Bubble - Tweak algorithms to
be less accurate
• Profile Detox - Let an open door to
reshape profiles
• Human Computation - Enlist humans
to give more diversity
© Fabien Girardin

19. „H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S SMOBILE PHONES HAVE BECOME
SLOT MACHINES!
Tristan Harris, a former Google product manager

20. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Design for engagement responsibly

21. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Design for engagement responsibly
Today, algorithms typically score the relevance of social and news
content. Major online services are fighting to hook people, grab their
attention for as long as possible. Their business is to keep users
active as long and frequently as possible on their platforms. They use
techniques that promote addiction = hooking people endlessly
searching for the next reward.
That new power raises the need for new design principles in the age
of machine learning.

22. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Empathy is not (yet) available

23. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Empathy is not (yet) available
The ethical and practical considerations of machine learning
have to be shaped by how products using machine learning
affect users and how users can understand and see those
effects.

24. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Questions are the new answers
© Jan Korsanke

25. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Questions are the new answers
In future computers won’t deliver answers before asking
you back (a string of) questions. But what are rules and
etiquette for machines?
© Jan Korsanke

26. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Illustrate for transparency

27. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Illustrate for transparency
When users don’t understand how an algorithm gets its results,
it can be difficult to trust the system. Transparency
communicates trust.

28. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Seamful design

29. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Seamful design
Designers must know that a „Prediction Feature“ is not the
same as informing, and consider how well such a prediction
could support a user action.

30. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Machine bias: AI can lead to discrimination

31. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Challenges for UX
Machine bias: AI can lead to discrimination
Most of the current facial recognition techniques use the same
data set, which was trained on mainly white people. It would
not recognise people with other skintones.

32. „H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
ULTIMATELY,
DESIGNERS MUST ACT AS A BULWARK
AGAINST IRRESPONSIBLE,
UNETHICAL USE OF AI.
Katharine Schwab, Fast Company

33. H O W D E E P L E A R N I N G
C H A N G E S T H E
D E S I G N P R O C E S S
Principles for designing AI responsibly
• AI must be designed to assist humanity
• AI must be transparent
• AI must maximize efficiencies without destroying the dignity of people
• AI must be designed for intelligent privacy
• AI must have algorithmic accountability
• AI must guard against bias
Satya Nadella, Microsoft CEO

34. Alexander Meinhardt
krunchtime.org
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