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Alice has a Blue Car: Beginning the Conversation Around Ethically Aware Decision Making

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Alice has a Blue Car: Beginning the Conversation Around Ethically Aware Decision Making

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Who we meet, what we choose to do, and the costs of our choices, are increasingly influenced and even driven by software systems, algorithms and infrastructure. The decisions underlying how those work are made by people just like you.

This talk challenges the audience to consider the ongoing implications of decisions made early in the design process, and provides practical examples of translating moral standpoints into real-world implementations. Presented by ThoughtWorks Principle Data Engineer Simon Aubury and Project Manager Dr. Maia Sauren.

Who we meet, what we choose to do, and the costs of our choices, are increasingly influenced and even driven by software systems, algorithms and infrastructure. The decisions underlying how those work are made by people just like you.

This talk challenges the audience to consider the ongoing implications of decisions made early in the design process, and provides practical examples of translating moral standpoints into real-world implementations. Presented by ThoughtWorks Principle Data Engineer Simon Aubury and Project Manager Dr. Maia Sauren.

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Alice has a Blue Car: Beginning the Conversation Around Ethically Aware Decision Making

  1. 1. 1 Dr. Maia Sauren- Program and Project manager Simon Aubury- Principal Data Engineer
  2. 2. 2 Alice
  3. 3. 3 why do we care?
  4. 4. 4 the unintended consequences of not knowing your users
  5. 5. 5
  6. 6. 6 To understand our users we need data
  7. 7. 7© ThoughtWorks 2019 References: https://uxdesign.cc/designing-ethically-pt-2-535ac61e2992 “Data is not the new oil, data is the new asbestos”
  8. 8. 8
  9. 9. 9
  10. 10. 10 10 01 11
  11. 11. 11 10 01 11 ??
  12. 12. tolerable noise or tolerable signal? 12
  13. 13. relative noise relative signal 13
  14. 14. WHAT COULD POSSIBLY GO WRONG </sarcasm> 14
  15. 15. 15
  16. 16. the law is a blunt instrument for a reason 16
  17. 17. technology is a blunt instrument for the same reason 17
  18. 18. the user is the signal not the noise 18
  19. 19. design for high signal tolerance 19
  20. 20. which of your users provide the broadest definition of signal? 20
  21. 21. 21
  22. 22. 22
  23. 23. 23
  24. 24. 24
  25. 25. From theory to practice
  26. 26. 26 Product - we’re an insurance co
  27. 27. 27 Alice wants some insurance
  28. 28. 28 What should Alice’s premiums be? A machine can do this Premium ($) = Risk (%) X Value ($) ● Features ● Algorithms
  29. 29. 29 UX ● Consent - meaningful consent ○ Gathering ○ Storage ○ Aligned to 3rd parties ● TOS - did you read it? What are our assumptions?
  30. 30. 30 UX - simple example What are our assumptions?
  31. 31. 31 UX - simple example What are our assumptions?
  32. 32. UX - protect the individual Randomized Response Technique
  33. 33. 33 Do you drive drunk? Collect useful insights across a population
  34. 34. 34 What data to calculate risk? What are our assumptions?
  35. 35. 35 Dev What are our assumptions?
  36. 36. 36 Dev What are our assumptions?
  37. 37. 37 Infra & security & storage What are our assumptions?
  38. 38. 38 Legal - Aust What are our assumptions?
  39. 39. 39 Legal - UK What are our assumptions?
  40. 40. 40 Expectations Is this fair? example@hotmail.com example@gmail.com
  41. 41. 41 Expectations Is this fair?
  42. 42. 42 Back to our risk algorithm How’s this looking? Premium ($) = Risk (%) X Value ($) ● Features ○ Gender if ! EU ○ Car Model ○ Colour (maybe) ○ Postcode ○ Customer Name ○ Email
  43. 43. 43 What data to calculate risk? What are our assumptions? X X
  44. 44. 44 What data to calculate risk? What are our assumptions?
  45. 45. 45 Alice has a blue car Even if we don’t explicitly set out to capture the data; there’s implicit relationships in our feature set
  46. 46. 46 don’t reinvent the trolley problem other people have found solutions!
  47. 47. 47 Google AI Fairness
  48. 48. 48
  49. 49. 49
  50. 50. 50 EthicalOS
  51. 51. 51 Tarot cards of tech
  52. 52. 52 Design Ethically Toolkit
  53. 53. 53 Design Ethically Toolkit
  54. 54. 54 Data Ethics Canvas
  55. 55. 55 Data Ethics Canvas
  56. 56. 56 Nicolas Steenhout
  57. 57. 57 Game Access
  58. 58. 58 Glitch TOS
  59. 59. 59 Tenon.io
  60. 60. 60 rtl.wtf
  61. 61. 61
  62. 62. 62 Risk-Based Ethical Use of Data
  63. 63. which of your users provide the broadest definition of signal? 63
  64. 64. 64 Links http://ecee.colorado.edu/~ecen7438/ https://gameaccessblog.org.uk/ https://pair-code.github.io/what-if-tool/ https://theodi.org/wp-content/uploads/2019/07/ODI-Data-Ethics-Canvas-2019-05.pdf https://slideplayer.com/slide/4472170/ DrAfter123 / Vetta / Getty Images https://www.flickr.com/photos/spiderman/2061070613 https://broadlygenderphotos.vice.com/ https://www.monash.edu/__data/assets/pdf_file/0007/216475/An-investigation-into-the-relationship-between-vehicle-colour-and-crash-risk.pdf https://www.mdpi.com/journal/ijerph https://www.express.co.uk/life-style/cars/910479/car-insurance-email-address-gmail-hotmail https://gradientinstitute.org/news/Gradient%20Institute%20Submission%20-%20Australia's%20AI%20Ethics%20Framework%20Discussion%20Paper.pdf https://consult.industry.gov.au/strategic-policy/artificial-intelligence-ethics-framework/ https://incl.ca/wp-content/uploads/2018/05/accessibility-security.pdf http://rtl.wtf/tac/booboos/ http://tarotcardsoftech.com/ https://evapenzeymoog.com/ https://www.designethically.com/toolkit http://www.soc.univ.kiev.ua/sites/default/files/newsfiles/4_slides_rrt.pdf Tenon.io https://incl.ca/ https://glitch.com/legal/#tos
  65. 65. Thank you 65 Maia Sauren maia.sauren@thoughtworks.com @sauramaia Simon Aubury simon.aubury@thoughtworks.com @SimonAubury

Notas do Editor

  • MC SPEAKING,

    Dr. Maia Sauren is a program and project manager at ThoughtWorks, with backgrounds in large-scale organisational transformation and healthcare applications. Maia’s previous lives were in biomedical engineering, science communication, and education.

    Simon is a Principal Data Engineer with expertise in enterprise system design for large data systems.
  • MAIA SPEAKING
    Who is alice?
    What does Alice need, what is relevant ; and what are the consequence of our decisions?
    Tonight we want to engage you in a conversation .. what guidelines should you consider for your organisation and yourself when building solutions?
    What are the iimplications of decisions made early in the design process, and how this translates to real-world outcomes.
  • Maia speaking
    Why do we care about alice, or her data? Why would anyone want that?
  • Maia speaking
    Why do we care about alice, or her data? Why would anyone want that?
  • MAIA SPEAKING
    Gender neutral ads for STEM jobs
    Women click on the ads more
    Ads become more expensive to show to women
    Women see fewer ads for jobs in STEM

    Algorithm aim: minimise spend
  • Maia speaking
    Data needs to be
    Accurate
    Clean
    Diverse

  • MAIA SPEAKING
  • MAIA SPEAKING

    What is a signal?
    What is an error?
  • MAIA SPEAKING

    What is a signal?
    What is an error?
  • MAIA SPEAKING

    What is a signal?
    What is an error?
  • MAIA SPEAKING

    What is a signal?
    What is an error?
  • MAIA SPEAKING
    Noise as it applies to
    Within greater context
    Contextualised as relative harm, relative good, relative harm minimisation


  • MAIA SPEAKING
    How do you teach that
    To a child
    To a machine

  • MAIA SPEAKING
    How do you teach that
    To a child
    To a machine

  • Bias laundering through technology - Gretchen Mccullock, Because internet
  • MAIA SPEAKING
  • MAIA SPEAKING
    So you have to give it blunt flexibility
    In the design stage
  • MAIA SPEAKING
  • MAIA SPEAKING
    So you have to give it blunt flexibility
    In the design stage
  • MAIA SPEAKING
  • Alice Saunders
    Refused airline ticket by British Aerospace
  • MAIA SPEAKING



  • MAIA SPEAKING


    https://broadlygenderphotos.vice.com/


  • MAIA SPEAKING

    Frank Starmer Follow
    A large red stone (Uluru, south of Alice Springs)
    An adventure south of Alice Springs to visit Uluru. Here is a sunset photo.
    -- CC / https://www.flickr.com/photos/spiderman/2061070613
  • SIMON SPEAKING

    Let’s illustrate data ethics with a scenario
    Can we describes the value judgements we make about Alice
    and approaches we make when generating, analysing and disseminating data about Alice
    Do we understand good practice in computing techniques, ethics and information assurance appreciative of relevant legislation,

    Guidelines - sensible defaults for individual teams
    Framework - translating values to behaviours
    Ethical position - organisational value statement

    And then

    Guidelines:
    Framework:
    Ethical position:





  • SIMON SPEAKING
    We’re going to apply this lens by imaging the product decisions in a hypothical insurance co

    Congrats on the new job
    Think about the
  • SIMON SPEAKING
    Alice has a need - an approariatly pricved insurance product
    What is the best thing for Alice?
    Let’s design a product
  • SIMON SPEAKING

    Could have actuaries and risk tables
    People are dumb and lazy – we need robots to do the maths for them.



  • SIMON SPEAKING

    Firstly - we need to engage with Alice. This is typically a sales funnel
    our business is based on data
    TOS - plain language; (Linkedin has a sentance that’s 91 words long)
    no dark patterns;
    accessible and meaningful (eg. language)
    organisational value statement
  • SIMON SPEAKING

    Option 1 - poor
    Option 2 - better ;
    has choice
    An explanation of WHY;
    No word “Other” may make people feel like an after-thought
    And an explicit call out that it’s kept private
    Option 3 - best; don’t ask
  • SIMON SPEAKING

    Credit decision based on non-compliance with instructions - blue pen users get a different weighting
    AFter much investigation, was discovered to be most correlated to the pen left in the office

    Data is not truth
    Humans create, generate, collect, capture, and extend data. The results are often incomplete, and the process of analyzing them can be messy. Data can be biased through what is included or excluded, how it is interpreted, and how it is presented.

    if the data is crappy, even the best algorithm won't help. Sometimes it's referred as "garbage in – garbage out".

  • SIMON SPEAKING

    Respect privacy and the collective good
    Now : let’s discuss an approach for data capture for a sensitive topic.
    “Did you cheat on your partner in the last 12 months”
    “Dispose of waste illegally.” or “Lie on an insurance application”
    The randomized response technique (RRT) nearly 50 years can be used minimize the response bias from questions about sensitive issues.
    Protect the individual ; but collect accurate data in aggregate
    Respondents would be randomly assigned to answer either the sensitive question or the inverse question with a “yes” or “no” answer [8]. As is shown in
    Table 1, the assignment undergoes a randomization procedure, such as rolling a die



  • SIMON SPEAKING

    Back to our example; how can we capture a meaningful response : do you drive drunk
    We want a represnatative answer;
    but if we ask Alice directly we’re unlikely to get an accurate anser
    How do you Respect privacy and the collective good ONLINE … without a dice?

    Randomized Response Technique can be applied online by a bit of A/B testing






  • SIMON SPEAKING

    Imagine we’ve now got data that our users are happy to share; and we feel that it’s important to our business
    Or we have access to a partner data provider
    What can we derive from these insights?
    Basic example

    Basics of
    Features (Also known as parameters or variables) are the factors for a machine to look at. Those could be car mileage, user's gender, age, postcode; features are column names
    Algorithms - we might pick ML algoirtim (maybe a decision tree for classification, or clusstering model) that based on the data classifies users, and determines a likely risk
  • SIMON SPEAKING

    Let’s discuss algoirthmic approaches to understanding Alice
    There;’s nothing new here; techniques here have been used for > 40 years
    To illustrate - lket’;s think of the simplify case where we have a corpus of relationships
    We can use algoirimit classifiers to associate frequence clusters of info with historric outcomes
    If historically we’ve seen high correlations between April and Aries ; perhaps we can automate this assumption as part of our business
    We could gloss this up as “AI”
    Just because AI can do something doesn’t mean that it should.
    What’s the effect on ALice is we apply this approach blindly?
  • SIMON SPEAKING

    With only gender-neutral pronouns in languages like Malay, Google Translate says men are leaders, doctors, soldiers, and professors, while women are assistants, prostitutes, nurses, and maids.

    The results are similar in Hungarian. According to findings by Strawberry Bomb, Google Translate interprets men as intelligent, and women as stupid. It also suggests that women are unemployed, cook, and clean.

    Don’t presume the desirability of AI
    Just because AI can do something doesn’t mean that it should. When AI is incorporated into a design, designers should continually pay attention to whether people’s needs are changing, or an AI’s behavior is changing.

  • SIMON SPEAKING

    Dev responsibilities
    Information handling
    Responsibilities for all parties; partners; 3rd parties providers; stakeholders

    PCI, PII, HIPA
    HIPPA alone - 51 pages in the overview document for one tech vendor
    Payments PCI - 71 pages for the same vendor
  • SIMON SPEAKING

    notify the Australian Information Commissioner and individuals whose personal information has been subject to a data breach likely to result in serious harm
    data breach arises when the following three criteria are satisfied:

    There is unauthorised access to or unauthorised disclosure of personal information,
    This is likely to result in serious harm to one or more individuals; and
    The entity has not been able to prevent the likely risk of serious harm with remedial action.
    assessment within 30 days



    Respect privacy and the collective good
    While there are policies and laws that shape the governance, collection, and use of data, we must hold ourselves to a higher standard than “will we get sued?” Consider design, governance of data use for new purposes, and communication of how people’s data will be used.

    Call out downsides of holding data
    It’s a liability;
    Enduring
  • SIMON SPEAKING

    Last line highlight
  • SIMON SPEAKING

    Admiral admitted to charging drivers higher premiums for using a Hotmail address.
    It found some enquires using a Hotmail address up to £31.36 more expensive than those using a Gmail account.
  • SIMON SPEAKING

    Car colour does in fact make a big difference to road safety -
    Silver, grey, red and black cars are most likely to be involved in accidents whereas white, orange and yellow are the safest colour choices
  • SIMON SPEAKING

    So it’s getting a little harder to work out what features to use
  • SIMON SPEAKING

    What do we now know about alice
  • SIMON SPEAKING


    Baxck to our example ; did we pick good
    Features (Also known as parameters or variables) are the factors for a machine to look at. Those could be car mileage, user's gender, age, postcode; features are column names
    Algorithms - we might pick ML algoirtim (maybe a decision tree for classification, or clusstering model) that based on the data classifies users, and determines a likely risk
    DID we find or influence our algoithm in any other way?
  • SIMON SPEAKING


    Even if we don’t explicitly set out to capture the data; there’s implicit relationships in our feature set
    Models can discover remarkable correlations
    Can we describes the value judgements we make about Alice
    and approaches we make when generating, analysing and disseminating data about Alice
    Do we understand good practice in computing techniques, ethics and information assurance appreciative of relevant legislation,

    Guidelines - sensible defaults for individual teams
    Framework - translating values to behaviours
    Ethical position - organisational value statement

  • MAIA SPEAKING
  • MAIA

    What If tool from Google inspect a machine learning model

    Test algorithmic fairness constraints
    Examine the effect of preset algorithmic fairness constraints, such as equality of opportunity in supervised learning.

  • MAIA SPEAKING

    This is how we’re thinking internally ; a canvas Risk-Based Ethical Use of Data
  • MAIA SPEAKING

  • MAIA SPEAKING

  • MAIA SPEAKING

  • MAIA SPEAKING

  • MAIA SPEAKING

  • Moriel Schottlender
  • MAIA SPEAKING

    This is how we’re thinking internally ; a canvas Risk-Based Ethical Use of Data
  • MAIA SPEAKING
  • Nobody speaking

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