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Nadia Piet - Design Thinking for AI

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Questo talk è un invito a designer e innovators di tutto il mondo a partecipare, sfruttando le opportunità e affrontando le sfide dell’intelligenza artificiale per creare human(ity)-centered applications e significative user experiences. Partiremo con un corso intensivo sull’intelligenza artificiale e il machine learning, poi ci interrogheremo sul ruolo dei designer, esplorando alcuni aspetti critici della progettazione, su come applicare le nostre competenze di designer per avvicinare l’IA a valori sociali, economici e per l’utente. Infine presenteremo una panoramica pratica di come utilizzare il design thinking process che conosciamo e condurlo a quello meno familiare dell’intelligenza artificiale. E allora scopriamo, definiamo e progettiamo futuri desiderabili per l’intelligenza artificiale!

Nadia Piet - Design Thinking for AI

  1. 1. Exploring the role of design (thinking) in AI/ML for WUD Rome by Nadia Piet @nadiapiet
  2. 2. Hi! I’m Nadia Piet 2006 2019 Freelance service & strategic designer and researcher with a focus on emerging and humanity-centered tech and futures “We shape our tools and then our tools shape us” — Marshall McLuhan
  3. 3. Where do (service/UX) design and AI/ML intersect? What’s the role of design(ers) in the AI/ML development process?
  4. 4. aimeets.design toolkit
  5. 5. artificial intelligence ?
  6. 6. artificial intelligence the practice of making computers do things traditionally thought of as requiring human cognition
  7. 7. artificial intelligence machine learning ≄ meansgoal
  8. 8. machine learning programming ≄ data rules output output data rules
  9. 9. A new way of communicating with computers Useful for problems where the output is clear, but rules aren’t Predictions are probabalistic (%)
  10. 10. Prediction Output Cake Model Chef Training Learning Practice GPU Hardware Utensils Algorithm(s) Instructions Recipe Data Input Ingredients Disclaimer: Please note this is a highly simplified representation of the actual process. + System + +
  11. 11. classification clustering regression
  12. 12. (semi-)intelligent (semi-)adaptive (semi-)autonomous systems
  13. 13. AI will not tell us problems worth solving or questions worth asking or inefficiencies worth preserving
  14. 14. human(ity)- centered design / design (thinking) ?
  15. 15. Where do (service/UX) design and AI/ML intersect? What’s the role of design(ers) in the AI/ML development process?
  16. 16. with of design AI / for/
  17. 17. system requirements system limitations ML engineering space
  18. 18. user needs system requirements user experience system limitations design space ML engineering space
  19. 19. user needs system requirements user experience system limitations design of design for
  20. 20. 🤖 16 user-centric design/engineering considerations
  21. 21. Enabling new types of user experiences
  22. 22. Turning tech capabilities into user and social value user-centered problem solving data-driven opportunity spotting tech-driven opportunity spotting Build on existing applications Leveraging dataResearch to application How might AI/ML help solve [this] in a unique way? How might the data we have access to create value (for our users)? How might we leverage AI/ML (in processes where good outcomes are clear but rules aren’t)? Developing new models
  23. 23. User research & domain experts for modelling Output (label prediction) User experience Input (data sets) Features (factors) Objective (question to answer) Business value User input
  24. 24. Trade-offs in choosing an algorithm + training a model Precision % of predictions that are relevant Recall % of objects that are predicted VS How important is .. Accuracy % of predictions are correct Transparency ability to trace back why/how VS
  25. 25. Benchmarking + evaluating Plot: Current human benchmark Baseline model Minimum confidence level Minimum benchmark to provide value to user 100% accuracy (?) 0% accuracy
  26. 26. Cost of errors Confusion matrix Positive Negative Positive :) True positive :( False negative Negative :( False positive :) True negative Machine prediction Userreality
  27. 27. Navigating design values (per use case) Emotional relationship (‘warm tech’) Instrumentalism (‘cold tech’) Automation bias / reliance Lack of trust / manual Personalization Privacy Pro-active (invasive?) Re-active (dormant?) Human touch Computational efficiency
  28. 28. Prototyping the experience (not the model)
  29. 29. Onboarding + managing expectations
  30. 30. Explainability
  31. 31. User feedback for machine teaching Data Action Interface Model
  32. 32. User feedback for machine teaching
  33. 33. User feedback for machine teaching
  34. 34. User autonomy + data consent by Philip van Allen
  35. 35. Anticipate + design for (graceful) failure
  36. 36. Data bias & fairness Figure 2–5: ‘COMPAS Software Results’, Julia Angwin et al. (2016)
  37. 37. Ethics, data privacy & (un)intended consequences
  38. 38. Translating subjective human experience into computational parameters
  39. 39. user needs system requirements user experience / trade-offs system limitations design space engineering space Picking + training a model Evaluating your model Cost of errors Explainability User autonomy User feedback + machine teaching Bias + Fairness Spotting opportunities Expectations + graceful failure
  40. 40. Bridging AI and design
  41. 41. “Now is our opportunity to shape that future by putting humanists and social scientists alongside people who are developing artificial intelligence”  - Marc Tessier-Lavigne President of Stanford University
  42. 42. The next design (r)evolution industrial economy product design service economy service design experience economy experience design digital / computational economy algorithm / AI design
  43. 43. “Human-centered design has expanded from the design of objects to the design of algorithms that determine the behavior of automated or intelligent systems” - Harry West (CEO frog)
  44. 44. 🙋 Thank you all Grazie mille Dankjewel Questions? Curious? Ideas? Let’s connect @nadiapiet hello@nadiapiet.com
  • cgvw

    Jun. 18, 2021
  • LuanaHohmann

    Jul. 17, 2020
  • salvatorelarosa

    Dec. 4, 2019

Questo talk è un invito a designer e innovators di tutto il mondo a partecipare, sfruttando le opportunità e affrontando le sfide dell’intelligenza artificiale per creare human(ity)-centered applications e significative user experiences. Partiremo con un corso intensivo sull’intelligenza artificiale e il machine learning, poi ci interrogheremo sul ruolo dei designer, esplorando alcuni aspetti critici della progettazione, su come applicare le nostre competenze di designer per avvicinare l’IA a valori sociali, economici e per l’utente. Infine presenteremo una panoramica pratica di come utilizzare il design thinking process che conosciamo e condurlo a quello meno familiare dell’intelligenza artificiale. E allora scopriamo, definiamo e progettiamo futuri desiderabili per l’intelligenza artificiale!

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