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Designing AI for Humanity at dmi:Design Leadership Conference in Boston


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As design leaders we must enable our teams with skills and knowledge to take on the new and exciting opportunities that building powerful AI systems bring. Dynamic systems require transparency regarding data provenance, bias, training methods, and more, to gain user’s trust. Carol will cover these topics and challenge us as design leaders, to represent our fellow humans by provoking conversations regarding critical ethical and safety needs.
Presented at dmi:Design Leadership Conference in Boston in October 2018.

Published in: Design
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Designing AI for Humanity at dmi:Design Leadership Conference in Boston

  1. 1. Designing AI for Humanity Carol Smith @carologic dmi:Design Leadership Conference #DLC18. October 9, 2018 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License except where noted otherwise.
  2. 2. Designing AI for Humanity / @carologic Help humanity...
  3. 3. AI is as imperfect as the humans making it
  4. 4. HMW Engender Trust?
  5. 5. Westworld
  6. 6. Source of Westworld stories?
  7. 7. Designing AI for Humanity / @carologic Writer made data/content • Creates backstories and scripts • Environmental design • Data: Who, what, how, and when of experience
  8. 8. Designing AI for Humanity / @carologic Why should we care? • What are his bias’? • How did this affect the experience? • Does it matter?
  9. 9. What might change with different writer(s)?
  10. 10. Who programmed and trained Westworld?
  11. 11. Designing AI for Humanity / @carologic Scientists • Triage system - “Step into analysis” • Ever leave? • Imagine negative consequences?
  12. 12. Designing AI for Humanity / @carologic Reveries introduced…
  13. 13. Designing AI for Humanity / @carologic New programming • Ford/Arnold as programmer • No context for staff • Should have provided information
  14. 14. Dynamic - not sentient (yet)
  15. 15. Designing AI for Humanity / @carologic Please don’t make this AI
  16. 16. Designing AI for Humanity / @carologic To engender trust, provide transparency • Data • Training/programming of system • Rationale/bias/logic
  17. 17. Designing AI for Humanity / @carologic What is AI?
  18. 18. AI is present when computers/machines – Exhibit intelligence – Perceive their environment – Take actions/make decision to maximize chance of success at a goal Our Road to Self-Driving Vehicles | Uber ATG
  19. 19. Designing AI for Humanity / @carologic AI/Cognitive computers are • Algorithms • Know ONLY what you teach • Control ONLY what given control of • Aware of nuances and can continue to learn
  20. 20. Dynamic Data + training - Apply to new situations
  21. 21. Not sentient Not unknowable black box
  22. 22. Designing AI for Humanity / @carologic Taxonomies and Ontologies coming to life (NOT like humans learn) Photo:
  23. 23. We need AI for that!
  24. 24. Designing AI for Humanity / @carologic Like Any Good Design • Understand problem deeply • Build right AI system • Different problems require different systems
  25. 25. Tool for lawn care
  26. 26. Designing AI for Humanity / @carologic Start asking questions…
  27. 27. Who? What problem?
  28. 28. Subject matter experts are required
  29. 29. Designing AI for Humanity / @carologic Who will use the system and why? • What are their goals? • What problems are they trying to solve? • Are they working independently?
  30. 30. Designing AI for Humanity / @carologic What do users need to know? What changed? What are outliers?What comes next? What is unexpected? What is new? How can I tell what changed? Increase/decrease in frequency? Are my assumptions validated?
  31. 31. Designing AI for Humanity / @carologic Anticipate changes with AI system • Scope/intention? • Improvements? • Better or faster?
  32. 32. Designing AI for Humanity / @carologic Unintended consequences? • Understand user’s fears • Address them to protect users
  33. 33. Designing AI for Humanity / @carologic Content
  34. 34. Designing AI for Humanity / @carologic Data Source • In existence? • Available? • High quantity? • High quality? Photo by sunlightfoundation
  35. 35. Designing AI for Humanity / @carologic Number Five “Needs Input” Short Circuit (1986 film) Ally Sheedy and Number Five (Tim Blaney)
  36. 36. Designing AI for Humanity / @carologic Curation • Source and Bias? • Who is creating/curating collection? – Respected experts – Diverse
  37. 37. All Data is biased
  38. 38. Social class, resource availability Race, Gender, Sexuality Culture, Theology, Tradition More…
  39. 39. “We often have no way of knowing when and why people are biased.” - Sandra Wachter Q&A: Should artificial intelligence be legally required to explain itself? By Matthew Hutson, May. 31, 2017. Interview with Sandra Wachter, data ethics researcher at Univ. of Oxford and Alan Turing Institute.
  40. 40. Humans teach what we feel is important… teach them to share our values. Grady Booch, Scientist, philosopher, IBM’er
  41. 41. Designing AI for Humanity / @carologic Ready for Use • Experts review results
  42. 42. Designing AI for Humanity / @carologic Data source • Few techs – Detailed, digital notes – Prefer using chemicals • Most techs – Rough written notes – Prefer “all natural” treatments Neither are wrong. Limited data created a bias.
  43. 43. Designing AI for Humanity / @carologic Humans required to teach and monitor AI • Water • Prune/Shape • Cull
  44. 44. Only as good as data and time spent improving it.
  45. 45. Designing AI for Humanity / @carologic Training and Accuracy
  46. 46. Designing AI for Humanity / @carologic Experts to train system • Vetting? • Availability? • Process? • Maintain quality?
  47. 47. Accuracy You’re cloning a colleague no-bake cookies photo by Melissa Hillier - recipe blogged at mXw1- cgk3ow-6kzJog-6kA5oZ-aYqEpT-MMkVVV-7aQLnM-ecL6fm-6kEd67-5ykEkC-2bsTnp3-dCh7J9-T4tu4i-8HdYNJ-73SMVr-6uwEGT-6kE34b-MMkEqr-6kEFws-6kEjVu-25rwHBc-6kA42g-6kzTi4-T36Moj-7Bx3rf-7vPVhb-6YNEHC-amariC-neddpV-ZNpJHE
  48. 48. Designing AI for Humanity / @carologic Priority of accuracy across industries Higher Priority 90-99%+ Lower Priority 60-89% accuracy is acceptable Financial Ecommerce
  49. 49. Designing AI for Humanity / @carologic Responsible, Intentional Design Some rights reserved by Rocky VI - License:
  50. 50. Designing AI for Humanity / @carologic Make it your business to keep people safe • Monitor system • Identify warning signs
  51. 51. Designing AI for Humanity / @carologic Privacy • What must a user reveal? • Who owns the data? • Life expectancy of data? • Keep data safe PAPA (Privacy, Accuracy, Property, Accessibility) Ethical Issues in IS by Richard Mason.
  52. 52. Designing AI for Humanity / @carologic Plan for unintended consequences • Scenarios – not every one • Focus on worst situations: – What happens when it becomes a Nazi? – What happens when it does XYZ?
  53. 53. Designing AI for Humanity / @carologic What will you do? • What is the method for “turning it off”? • Who is notified immediately? • Unintended consequences of turning it off? Google’s new tensor processing units:
  54. 54. Designing AI for Humanity / @carologic Secure back doors and brakes • “If it’s not usable, it’s not secure.” – Jared Spool, IAS17 • “Ensure humans can unplug the machines” – Grady Booch, Ted Talk Unintuitive and Insecure: Fixing the Failures of Authentication, Jared Spool, IA Summit 2017 Grady Booch, Scientist, philosopher, IBM’er
  55. 55. Don’t be ableist How People with Disabilities Use the Web: Overview /
  56. 56. Designing AI for Humanity / @carologic Communicating About The System Strong Bad Email #45 – Techno - Strong Bad makes a techno song. Homestarrunnerdotcom Published on Mar 31, 2009
  57. 57. Designing AI for Humanity / @carologic Communicate Responsibly • How is communication about the AI handled? • How do you report issues? • To whom?
  58. 58. Designing AI for Humanity / @carologic Potential Bias • Show awareness • Acknowledge issues • Overcommunicate
  59. 59. Designing AI for Humanity / @carologic To engender trust, provide transparency • Who made the data? • Who trained/programmed the system? – When updated? • Why system providing data it is?
  60. 60. Designing AI for Humanity / @carologic Displaying and comparing information • AI generated content vs. other • Confidence
  61. 61. Designing AI for Humanity / @carologic Crowdsourcing Quality • Show examples – Potential signs of building bias • How can a user report?
  62. 62. AI matures: update communication approach
  63. 63. Designing AI for Humanity / @carologic Ethics for AI
  64. 64. Trolley Problem Trolley Car 36, Rockford, Illinois Does the Trolley Problem Have a Problem? What if your answer to an absurd hypothetical question had no bearing on how you behaved in real life? By Daniel Engber. June 18, 2018. Image of anxious hypothetical trolley car lever operator by Lisa Larson-Walker
  65. 65. If we don’t ask tough questions, who will?
  66. 66. Designing AI for Humanity / @carologic Create a code of conduct/ethics • What do you value? • How helping people? • What lines won’t your AI cross? • How will you track your progress? Inspired by “3 guiding principles for ethical AI, from IBM CEO Ginni Rometty” by Alison DeNisco. January 17, 2017, Tech Republic
  67. 67. Designing AI for Humanity / @carologic Guidance • UXPA Code of professional conduct ACM Code of Ethics and Professional Conduct
  68. 68. Designing AI for Humanity / @carologic Take Responsibility • Humans in control • Support humans – Social consequences – Job displacement How to Keep Your AI from Turning into a Racist Monster By Megan Garcia.
  69. 69. Designing AI for Humanity / @carologic Hire/work with people affected by bias
  70. 70. Designing AI for Humanity / @carologic Explore AI - Don’t fear AI • Try out tools (appendix and notes) • Pair with others • Teach others about AI
  71. 71. Designing AI for Humanity / @carologic Create ethical, transparent and fair AI • Intentional design • Less-biased content • Communicate responsibly about AI Toward ethical, transparent and fair AI/ML: a critical reading list By Eirini Malliaraki, Feb 19 via tweet from @robmccargow transparent-and-fair-ai-ml-a-critical-reading-list-d950e70a70ea
  72. 72. Designing AI for Humanity / @carologic Continue the conversation… LinkedIn – CarolJSmith Twitter - @Carologic Slideshare – carologic
  73. 73. Designing AI for Humanity / @carologic Appendix Additional Information and Resources
  74. 74. Designing AI for Humanity / @carologic Barriers to Data • Literacy and awareness • Connection to internet – economics and location. • Access to pertinent data • Fear of AI Ethical Issues in IS by Richard Mason
  75. 75. Designing AI for Humanity / @carologic Types of Machine Learning
  76. 76. Designing AI for Humanity / @carologic Supervised Learning • Specialists involved in content creation and training • Programmer and/or GUI • Most common Artificial Intelligence Demystified by. Rahul December 23, 2016. Analytics Vidhya
  77. 77. Designing AI for Humanity / @carologic Annotating Content Image created by Angela Swindell, Visual Designer, IBM
  78. 78. Designing AI for Humanity / @carologic Supervised Machine Learning - GUI Watson Knowledge Studio, Supervised Machine Learning:
  79. 79. Designing AI for Humanity / @carologic Types of Machine Learning • Unsupervised learning – Machine defines patterns • Reinforced learning – Games – rules and rewards Artificial Intelligence Demystified by Rahul December 23, 2016. Analytics Vidhya
  80. 80. Designing AI for Humanity / @carologic Pattern recognition • Natural Language Processing • Image Analysis IBM Watson
  81. 81. Designing AI for Humanity / @carologic Deep Learning • Classify objects based on features • Can be applied to other types of AI Toward ethical, transparent and fair AI/ML: a critical reading list By Eirini Malliaraki, Feb 19 via tweet from @robmccargow transparent-and-fair-ai-ml-a-critical-reading-list-d950e70a70ea
  82. 82. Designing AI for Humanity / @carologic AI Tools • A list of artificial intelligence tools you can use today — for businesses, by Liam Hanel, July 11, 2017 on Lyr.AI %E2%80%8Afor-businesses/ and intelligence-tools-you-can-use-today-for-personal-use-1-3-7f1b60b6c94f • Best AI and machine learning tools for developers, By Christina Mercer, Sep 26, 2017 in Techworld from IDG wearables/best-ai-machine-learning-tools-for-developers-3657996/ • 15 Top Open Source Artificial Intelligence Tools by Cynthia Harvey, September 12, 2016 on Datamation top-open-source-artificial-intelligence-tools.html • IBM Watson Developer Tools (free trials):
  83. 83. Designing AI for Humanity / @carologic Want to Know More? • The Rise Of Artificial Intelligence As A Service In The Public Cloud Rise Of Artificial Intelligence As A Service In The Public Cloud by Janakiram MSV , Forbes Article: Courses at
  84. 84. Designing AI for Humanity / @carologic 10 Major Milestones in the History of AI
  85. 85. Designing AI for Humanity / @carologic Additional Resources • “How IBM is Competing with Google in AI.” The Information. competing-with-google-in-ai?eu=2zIDMNYNjDp7KqL4YqAXXA • “The business case for augmented intelligence” augmented-intelligence-36afa64cd675 • “Comparison of machine learning methods applied to birdsong element classification” by David Nicholson. Proceedings of the 15th Python in Science Conference (SCIPY 2016). • “Staples’ “Easy Button” Comes to Life with IBM Watson” in Business Wire, October 25, 2016. Button%E2%80%9D-Life-IBM-Watson • “How Staples Is Making Its Easy Button Even Easier With A.I.” by Chris Cancialosi, Forbes. with-a-i/#4ae66e8359ef • “Inside Intel: The Race for Faster Machine Learning”
  86. 86. Designing AI for Humanity / @carologic More Resources • “Update: Why this week’s man-versus-machine Go match doesn’t matter (and what does)” by Dana Mackenzie. Science Magazine. Mar. 15, 2016 man-versus-machine-go-match-doesn-t-matter-and-what-does • “For IBM’s CTO for Watson, not a lot of value in replicating the human mind in a computer.” by Frederic Lardinois (@fredericl), TechCrunch, Posted Feb 27, 2017. for-watson-not-a-lot-of-value-in-replicating-the-human-mind-in-a-computer/ • “Google and IBM: We Want Artificial Intelligence to Help You, Not Replace You” Most Powerful Women by Michelle Toh. Mar 02, 2017. Fortune. • “Facebook scales back AI flagship after chatbots hit 70% f-AI-lure rate - 'The limitations of automation‘” by Andrew Orlowski. Feb 22, 2017. The Register • “Microsoft is deleting its AI chatbot's incredibly racist tweets” by Rob Price. Mar. 24, 2016. Business Insider UK. Special Thanks: Soundtrack to 'Run Lola Run', 1998 German thriller film written and directed by Tom Tykwer, and starring Franka Potente as Lola and Moritz Bleibtreu as Manni. Soundtrack by Tykwer, Johnny Klimek, and Reinhold Heil
  87. 87. Designing AI for Humanity / @carologic Even More Resources • “IBM’s Automated Radiologist Can Read Images and Medical Records” by Tom Simonite, February 4, 2016. Intelligent Machines, MIT Technology Review. radiologist-can-read-images-and-medical-records/ • “The IBM, Salesforce AI Mash-Up Could Be a Stroke of Genius” by Adam Lashinsky, Mar 07, 2017. Fortune. • "Google can now tell you're not a robot with just one click" by Andy Greenberg. Dec. 3, 2014. Security: Wired. • “Essentials of Machine Learning Algorithms (with Python and R Codes)” by Sunil Ray, August 10, 2015. Analytics Vidhya. • IBM on Machine Learning • “At Davos, IBM CEO Ginni Rometty Downplays Fears of a Robot Takeover” by Claire Zillman, Jan 18, 2017. Fortune. • “Google and IBM: We Want Artificial Intelligence to Help You, Not Replace You” by Michelle Toh. Mar 02, 2017. Fortune.
  88. 88. Designing AI for Humanity / @carologic Yes, even more resources • Video: “IBM Watson Knowledge Studio: Teach Watson about your unstructured data” • “The optimist’s guide to the robot apocalypse” by Sarah Kessler, @sarahfkessler. March 09, 2017. QZ. • “AI Influencers 2017: Top 30 people in AI you should follow on Twitter" by Trips Reddy @tripsy, Senior Content Manager, IBM Watson . February 10, 2017 influencers-2017-top-25-people-ai-follow-twitter/ • “3 guiding principles for ethical AI, from IBM CEO Ginni Rometty” by Alison DeNisco. January 17, 2017, Tech Republic • "Transparency and Trust in the Cognitive Era" January 17, 2017 Written by: IBM THINK Blog • "Ethics and Artificial Intelligence: The Moral Compass of a Machine“ by Kris Hammond, April 13, 2016. Recode. machine
  89. 89. Designing AI for Humanity / @carologic Last bit: I promise • "The importance of human innovation in A.I. ethics" by John C. Havens. Oct. 03, 2015 • "Me, Myself and AI" Fjordnet Limited 2017 - Accenture Digital. • "Testing AI concepts in user research" By Chris Butler, Mar 2, 2017. concepts-in-user-research-b742a9a92e55#.58jtc7nzo • "CMU prof says computers that can 'see' soon will permeate our lives“ by Aaron Aupperlee. March 16, 2017. see-soon-will-permeate-our-lives • “The business case for augmented intelligence” by Nancy Pearson, VP Marketing, IBM Cognitive. 36afa64cd675#.qqzvunakw
  90. 90. Designing AI for Humanity / @carologic Definition: Artificial Intelligence • Artificial intelligence (AI) is intelligence exhibited by machines. • In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2] • Capabilities currently classified as AI include successfully understanding human speech,[4] competing at a high level in strategic game systems (such as Chess and Go[5]), self-driving cars, and interpreting complex data. Wikipedia:
  91. 91. Designing AI for Humanity / @carologic Definition: The Singularity • If research into Strong AI produced sufficiently intelligent software, it might be able to reprogram and improve itself. The improved software would be even better at improving itself, leading to recursive self-improvement.[245] The new intelligence could thus increase exponentially and dramatically surpass humans. Science fiction writer Vernor Vinge named this scenario "singularity".[246] Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. Because the capabilities of such an intelligence may be impossible to comprehend, the technological singularity is an occurrence beyond which events are unpredictable or even unfathomable.[246] • Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.[246] Wikipedia:
  92. 92. Designing AI for Humanity / @carologic Definition: Machine Learning • Ability for system to take basic knowledge (does not mean simple or non-complex) and apply that knowledge to new data • Raises ability to discover new information. Find unknowns in data. • More Definitions: • Algorithm: a process or set of rules to be followed in calculations or other problem- solving operations, especially by a computer. • Natural Language Processing (NLP):