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UX STRAT Europe 2019: Ruth Tamari

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UX STRAT Europe 2019: Ruth Tamari

  1. 1. UX Principles to Consider When Designing UX for AI Ruth Tamari
  2. 2. About myself
  3. 3. UX Principles for AI Trust Clarify Simplify Control Humanize
  4. 4. TRU ST
  5. 5. Humans are more likely to forgive each other than to forgive machines. If users are to trust AI systems, we must strive for algorithmic transparency.
  6. 6. Trust is dynamic, Trust is dependent, Trust must be maintained and managed. Likewise, mistrust is dynamic; it too can be maintained and it too should be actively managed A Taxonomy of Emergent Trusting in the Human–Machine Relationship | 2017 | Robert R. Hoffman
  7. 7. How to establish trust • Users tend to trust other users’ experience • Users trust their own experience • Follow users’ actions • Clearly identify data sources • Show system certainty with which a recommendation is made
  8. 8. CLA RIF Y
  9. 9. Humans seek explanations to satisfy certain purposes or goals. Clear explanations enrich users’ mental models which in turn enhance performance as well as nurture trust in AI.
  10. 10. Metrics for Explainable AI: Challenges and Prospects | 2018 | Hoffman et al. Users questions User Goal How does it work? Feeling of satisfaction at achieving and understanding how the system made a specific decision What does it achieve? Understanding of the system’s functions and use What will it do next? Feeling of trust based on the predictability of the system What would it have done if X were different? Resolution of curiosity at achieving an understanding of the system
  11. 11. How to clarify AI • Understand the persona • Use “Local Explanations” • Focus explanation • Treat explanations as a process
  12. 12. SIM PLI FY
  13. 13. Humans are constantly bombarded with big, bold, noisy accentuating data. Effective AI systems meet users’ needs while they co-exist and correspond with an ecosystem of products competing for attention.
  14. 14. The presentation format of recommendations affects how people perceive and receive the recommendation. The complexity of the information, the presentation format and appearance, the recipients’ context at the time of presentation and their ability to attend to the information all affect the impact of the information. Designing Recommendations | 2019 | Elizabeth F. Churchill
  15. 15. How to simplify AI • Favor text over visual explanations • Use the right vocabulary
  16. 16. Simplicity is the ultimate form of sophistication Leonardo da Vinci
  17. 17. CON TRO L
  18. 18. Humans are typically most comfortable when in control. “Black box” systems steer users away from their comfort zone and into unplanned interactions, confusing pathways and unpredictable outcomes.
  19. 19. We found that a large number of participants used the systems without their intelligent features…This behavior was often used as a coping strategy for problems with the system, but could also be a feature of intelligent systems to stress user control or raise “algorithmic awareness” When People and Algorithms Meet: User-reported Problems in Intelligent Everyday Applications | 2019 | Eiband et al.
  20. 20. How to help users control AI • Provide insight into what to expect • Support Undo and Redo • Focus on failure—don’t assume success • Ask for user feedback • Allow editing • Allow Users to Turn Intelligence on and off
  21. 21. HUM ANI ZE
  22. 22. Humans generally develop good rapport on the basis common grounds. To enhance user engagement, design interactions that are as close to human behavior as possible.
  23. 23. Version 1: Man: “Book it for June 31.” Google Assistant: “There are only 30 days in June.” Version 2: Man: “Book it for June 31.” Google Assistant: “Actually, June has only 30 days.” Alexa, Should We Trust You? | 2018 | Judith Shulevitz
  24. 24. How to humanize AI • Personify the system • Make the user experience enjoyable and engaging (efficient is no longer enough)
  25. 25. Strategically speaking, a brilliant data-driven algorithm typically matters less than thoughtful UX design. Thoughtful UX designs can better train machine learning systems to become even smarter. AI Won’t Change Companies Without Great UX, Harvard Business Review | 2017 | Michael Schrage
  26. 26. People dump AI advisors that give bad advice, while they forgive humans for doing the same | 2016 | Michael J. Coren Learning to trust artificial intelligence systems Accountability, compliance and ethics in the age of smart machines| 2016 | Dr. Guruduth Banavar Metrics for Explainable AI: Challenges and Prospects | 2018 | Hoffman et al. Trust and Recommendations | Victor et al. When People and Algorithms Meet: User-reported Problems in Intelligent Everyday Applications | 2019 | Eiband et al. A Taxonomy of Emergent Trusting in the Human–Machine Relationship | 2017 | Robert R. Hoffman Designing Recommendations | 2019 | Elizabeth F. Churchill Why micro-interactions are important for UX | 2018 | Athina Ntosti Microinteractions in User Experience | 2018 | Nielsen Norman Group, Alita Joyce, October 21, 2018 User Empowerment and the Fun Factor | |2002 | Jakob Nielsen Micro-interactions: why, when and how to use them to improve the user experience | 2018 | Vamsi Batchu Personification of the Amazon Alexa: BFF or a Mindless Companion? | Dr. Irene Lopatovska, Harri et Williams Alexa, Should We Trust You? | 2018 | Judith Shulevitz Microiteractions, Designing with details | Dan Saffer Silence is gold AI Won’t Change Companies Without Great UX, Harvard Business Review | 2017 | Michael Schrage The Challenge of Crafting Intelligible Intelligence | 2019 | Daniel s. Weld and Gagan Bansal References
  27. 27. Thank You!

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