2017 How Deep Learning Changes the Design Process (2)
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The document discusses how deep learning influences the design process, highlighting the need for integration with user experience (UX) design to maximize its potential. Key challenges include redefining customer personas, creating intuitive AI interfaces, and ensuring ethical considerations in AI use to prevent discrimination and enhance user trust. Ultimately, the document emphasizes the responsibility of designers to implement principles that foster transparency, accountability, and ethical AI deployment.
2017 How Deep Learning Changes the Design Process (2)
- 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 KRUNCHTI M E I NTE RACTIVE