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Design considerations for machine learning system

  1. Design considerations for a machine learning system Beyond user-centred design process Akemi Tazaki id_farm R&D Botscamp 2016
  2. Presentation overview 6 Key design challenges I faced Discussions4 Suggestions for long-term success From Human centered design perspective
  3. Key Design Challenges & Tips on design process
  4. Danger of lowest common denominator by statistical inference Pitfalls of common pattern While constructing classes edges cases are excluded to avoid overfit. Anthrax, metal fume fever, leukaemia said to have common symptoms to flu Body size adjustment for gestural interface
  5. Closer look on edge data for innovation Questioning, learning to give a proper interpretation and outcomes on the edges. Design tip 1
  6. Effort to control on the inferred logic Controlling one’s error Users by mistake have communicated wrong data. The System computes as a final answer without users being able to take it back. Tiny
  7. Making the learning mechanism transparent and explicit Who are you? How do you learn? ➔ Reinforcement learning (rewards, punishment etc.) ➔ Transfer learning ➔ Deductive logic learning ➔ Adaptive learning (user becomes teacher) ➔ Geometric intelligence (shapes, movement) ➔ etc. Design tip 2
  8. Persuasive vs. Friendly tone of voice Letting users’ control the system behavior for its own detriments for behavioral change ? When users are using the system to change their behaviour should the system be friendly? Can “friendly” be persuasive enough to change users’ behavior? Behavior change triggers in persuasive design ExpertAmateur Good Methods: ● Focus on failure ● Coaching ● Persistence ● Deliberate practice: ● Basic physical practice Goal: Professional Tennis player Bad Method: ● Baby step without setting strict goal
  9. Adjusting bots communication strategy according to the personality of user? With Sensing + Thinking People ● Be specific, confident, well-reasoned ● Demonstrate immediate advantages ● Provide examples; use visual aids With Sensing + Feeling People ● Be supportive, expressive and confident ● Provide examples; demonstrate immediate advantages, profit ● Appeal to emotions and feelings by making it “personal” With Intuitive + Thinking People ● Be specific, well-reasoned; use diagrams ● Use concepts, theories to appeal to intellectual capabilities ● Give them a challenge ● Show how the subject of communication fits in “big picture” With Intuitive + Feeling People ● Be expressive, well-reasoned via visual aids ● Appeal to their intuition ● Give them a challenge ● Show how the subject of communication fits in “big picture” Design tip 3
  10. Expression style and personality Today’s weather? Team’s cognitive bias Team is unaware of how their cognitive bias is shaping the personality of the ML system. Letting users’ sense to evaluate the weather Giving a straightforward data
  11. Gap between ML system and avatar’s representation How do you represent appropriately the system’s cognitive capability? Right expectation on the system Avatar representation may not translate the capability of the system, therefore setting false expectation by the users 3~6 year old 1~2 year old Unknown = expectation is hard to set
  12. Awareness on how it shapes our culture New social skills, new education system for our society? Unwittingly media technology, including communication tools such as chatbots, shapes the way we communicate and think. The Machine is a social being. ku-live-in-tokyo-japan-3390 70511 Miku hologram concert Shaping cultural extremes or bubble based on simplistic “average” algorithm of click.
  13. Evaluating impacts on society in a multi-disciplinary design team pre-2016 Design concerns over usability of UI artefacts in a given context. Inclusion of designers and user researchers to integrate human aspects. 2016 Future Design concerns over long haul effects on humanity. Inclusion of philosopher, historian, social scientists to make an informed decision on cultural and ethical values. Design tip 4
  14. New skills in ML system design project team Social scientist Writer Philosopher How about integrating different skillset in AI engineering team? Designer/Artist Historian Behavior psychologist
  15. Thank you. How are you approaching your design challenges?
  16. About me My past design experiences related to machine learning system is in the domains of natural language process (translation tool) and computer visions applications (gestures, image recognition) as an interaction designer collaborating with engineers. Akemi Tazaki id_farm
  17. References A survey of socially interactive robots icssurvey.pdf Empirical Evidence for a diminished sense of agency in speech interfaces The problem with ‘Friendly’ Artificial Intelligence problem-with-friendly-artificial-intelligence The chatbot will see you now: AI may play doctor in the future of healthcare al-intelligence-chatbots-are-revolutionizing-he althcare/ For sympathetic Ear, More Chinese Turn to Smartphone Program ympathetic-ear-more-chinese-turn-to-smartphone- program.html?_r=0 How a chatbot could help people take their medication -could-help-people-take-their-medication/ Behavior Wizard: A method for Matching Target Behaviors with Solutions 6fbc0cb3fe7d0baf352ec69c100.pdf Interacting with an Inferred World: The Challenge of Machine Learning for Humane Computer Interaction le/1810/248690/Blackwell%202015%20%20Critical%2 0Alternatives%202015%20-%20The%205th%20Decennia l%20Aarhus%20Conference.pdf;sequence=1 March of the machines what-history-tells-us-about-future-artificial-i ntelligenceand-how-society-should Is Anything worth Maximizing? How Technology Hijacks People’s Minds - from a Magician and Google’s Design Ethicist Behavior measurement and change asurement-and-change Alone Together: Why we Expect more from Technology Sherry Turkle Behavior change matrix Behavior Change strategy cards r-change-strategy-cards/ Transparent Active Learning for Robots /chao10_hri_transparent.pdf All photos are credited inline.