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Ethical Design

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Interpreting Data

6:21 with Hope Armstrong and Michelle Zohlman

We'll consider the ethics of drawing conclusions based on data.

New Terms:

  • Predictive analytics: The use of statistical techniques to analyze current and historical facts to make predictions about future or otherwise unknown events. - Wikipedia

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    MICHELLE: In 2017, The Australian obtained an internal Facebook report. 0:00
    Reportedly it explained how advertisers could use their 0:05
    ad platform together psychological insights based on Facebook activity. 0:08
    The report which was commissioned by one of Australia's top banks postulated 0:13
    that advertisers could target millions of teenagers with ads when they feel insecure 0:18
    and worthless. 0:23
    This is called predictive analytics. 0:24
    Predictive analytics uses statistical techniques to analyze current and 0:30
    historical facts to make predictions about future or otherwise unknown events. 0:35
    This brings up the complexity of data consent. 0:41
    Data we're willing to share can imply conclusions we don't want to share. 0:44
    Yes, users willingly post information about their personal life on Facebook. 0:48
    But that does not give Facebook explicit consent to interpret that data. 0:53
    Draw conclusions on their mental health and 0:58
    then sell that health assumption to advertisers to sell more goods. 1:00
    Further, an inaccurate diagnosis could harm someone's well-being. 1:05
    In 2012, a New York Times article revealed Target, 1:10
    a popular retailer in the United States, predicted a woman's pregnancy. 1:13
    A man complained about Target sending coupons for baby clothes and 1:19
    cribs to his daughter. 1:22
    He thought they had been encouraging her to become pregnant. 1:24
    Although they actually had accurately predicted she was already pregnant. 1:27
    Target assigns each shopper a unique number called the guest ID. 1:32
    Attached to that guest ID is a detailed profile that contains everything 1:37
    they've ever bought at Target and their behaviors such as using a coupon, 1:42
    filling out a survey, or opening an email. 1:47
    HOPE: Per the New York Times, here's a sampling of data Target may have on file. 1:51
    Target declined to confirm the information it collects or purchases. 1:56
    Age, whether you are married and have kids, 2:02
    which part of town you live in, how long it takes you to drive to the store. 2:05
    Your estimated salary, whether you've moved recently, 2:11
    what credit cards you carry in your wallet. 2:15
    What websites you visit, ethnicity, job history, the magazines you read. 2:19
    If you've ever declared bankruptcy or got divorced, 2:26
    the year you bought or lost your house. 2:31
    Where you went to college, what kinds of topics you talk about online. 2:34
    Whether you prefer certain brands of coffee, paper towels, 2:39
    cereal, or applesauce. 2:44
    Your political leanings, reading habits, 2:46
    charitable giving, the number of cars you own. 2:49
    I don't mean to pick on Target. 2:54
    This is typical for retailers of this size. 2:56
    Target's guest marketing analytics department 2:59
    reviews this data to make predictions. 3:02
    The pregnancy prediction score enables target to send coupons and 3:06
    ads timed to the pregnancy stages. 3:11
    This is bound to lead to uncomfortable circumstances. 3:14
    Somebody sent a box of baby formula to my parents' house in my name 3:18
    when I was younger, and my parents grilled me. 3:23
    They thought I might be pregnant and hiding it from them. 3:26
    I wasn't pregnant, but looking back on it now, I must have inadvertently set off 3:30
    a trigger to lead a marketer to think I was pregnant. 3:34
    Pervasive social sorting enables companies to identify, 3:39
    categorize, and rate customers. 3:43
    This affects a person's available choices and 3:47
    even results in paying different prices for the same goods. 3:50
    According to a study of 5 top auto insurers in 15 US cities, 3:55
    the Consumer Federation of America found that good drivers of lower 4:00
    economic status consistently pay significantly more for 4:05
    auto insurance than higher economic status drivers. 4:09
    Four out of five of the nation's largest auto insurers regularly charged 40% to 4:14
    92% more to lower income drivers, even when they have perfect driving records. 4:20
    Further, the insurance company Allstate analyzes consumer shopping habits. 4:28
    Per CFA, Allstate charges a higher rate to customers not identified 4:34
    as bargain shoppers. 4:39
    The consumer's location is another factor. 4:41
    If other insurers offer competitive rates in the area, 4:45
    Allstate provides a lower rate. 4:49
    CFA cautions against unfair price optimization. 4:51
    Traditional rating factors such as driving safety record, or 4:56
    the average number of miles driven are more appropriate. 5:00
    MICHELLE: Advocates of data mining speak about improving the user experience 5:05
    through personalizing experiences to tailor to a user's needs. 5:10
    But how much personal information are you willing to give up for that convenience? 5:14
    And what about when data is given with one consent in mind and 5:20
    then later it is used in another context? 5:24
    When it comes to data privacy, the ethical line is difficult to draw. 5:28
    Increasingly, behavioral data is making automated decisions on groups of people. 5:33
    It's one thing to see an irrelevant ad. 5:39
    It's another level to be discriminated against in crucial areas such as finance, 5:41
    insurance, and healthcare. 5:46
    And data driven persuasion systematically influences people's behavior, 5:48
    even in political elections. 5:54
    Being mindful of how data is used and abused. 5:57
    I'm interested in more privacy conscious tools coming out, such as Matomo, and 6:00
    alternative to Google Analytics. 6:05
    Matomo analytics is an open source tool that keeps data on your server instead 6:07
    of sharing it with third parties. 6:12
    For more tool recommendations, 6:15
    check out the Ethical Design Handbook linked in the teacher's notes. 6:17

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