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Perpetuating Bias and Amplifying Hate with Artificial Intelligence

6:52 with Hope Armstrong and Michelle Zohlman

Machine learning opens up a substantial ethical debate, as it can perpetuate bias and amplify hate. We'll look at real-world examples where AI got it wrong, and then we'll explore some best practices to prevent mistakes.

New Terms:

  • Artificial intelligence (AI): creating intelligent machines that can simulate human thinking
  • Machine learning: A subset of AI; the ability for the computer to analyze information and then create new rules, enabling the computer to learn and create on its own

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    MICHELLE: You've probably heard about self driving cars, voice assistance, or 0:00
    some artificial intelligence approaches to science and healthcare. 0:04
    Like IBM Watson that helps us dig through vast libraries of complicated 0:08
    information in an instant. 0:12
    All of these things are powered by machine learning, 0:14
    which is a subset of artificial intelligence. 0:18
    Artificial Intelligence, abbreviated as AI, 0:21
    is the overarching concept of creating intelligent 0:25
    machines that can simulate human thinking. 0:30
    Machine learning is a subset of AI and it is the ability of the computer to 0:35
    analyze information and then create new rules. 0:40
    So instead of humans doing all of the programming, 0:43
    we can instead point computers in the right direction and 0:46
    they can continue on a path of self improvement. 0:50
    We're giving computers the chance to learn and create on their own. 0:53
    Machine learning opens up a substantial ethical debate. 0:56
    Cathy O'Neil, author of the book Weapons of Math Destruction, 1:02
    explains it like this. 1:06
    Big Data processes codify the past, they do not invent the future. 1:07
    Doing that requires moral imagination and that's something only humans can provide. 1:14
    We have to explicitly embed better values into 1:20
    our algorithms creating Big Data models that follow our ethical lead. 1:23
    Sometimes that will mean putting fairness ahead of profit. 1:27
    Increasingly, machine learning is organizing information such as search 1:31
    results according to invisible parameters that reveal biases in society. 1:37
    Even though developers may not identify as racist or 1:42
    sexist, the algorithms or the data fed to the system might be. 1:47
    The software we create can amplify inequalities to make them even worse. 1:51
    HOPE: The data that informs machine learning can have biases, 1:58
    this is called algorithmic bias. 2:03
    In 2016, University of Virginia computer science professor Vicente Ordonez, 2:06
    noticed a sexist pattern in two image collections commonly used for research. 2:12
    Images of shopping and washing are linked to women, 2:17
    images of coaching and shooting are linked to men. 2:21
    This is just one example of how machine learning can amplify sexism. 2:26
    It can amplify racial bias too. 2:30
    In a TEDx Talk by Joy Buolamwini, 2:34
    she explains how facial recognition software didn't detect her face. 2:37
    Those who develop the algorithm hadn't trained it to 2:43
    recognize a range of skin tones and facial structures. 2:47
    In addition to perpetuating biases, 2:51
    technology can shape our perception of reality. 2:53
    Guillaume Chaslot, a former software engineer at YouTube created 2:56
    the algorithm that predicts which video to play next. 3:00
    He admitted the algorithm tries to find rabbit holes that will draw 3:04
    you into more videos on a given topic. 3:09
    Algorithms optimized for 3:12
    increasing watch time can inadvertently lead to radicalization. 3:14
    A comprehensive study in Science Magazine found that fake news is 3:19
    shared six times more than accurate stories. 3:23
    When systems are not factually checked, 3:27
    they use virality as the only success metric. 3:30
    Interestingly, this is how conspiracy theories are spread. 3:33
    Similarly, something as innocuous as 3:38
    autocompletion in the Google search bar shapes our reality. 3:41
    Let's search for "climate change is." 3:45
    The search prediction makes recommendations based on 3:52
    your search history as well as trending searches in your area. 3:55
    I live in an area that's supportive of reversing climate change, so 3:58
    my results are mostly reflective of that. 4:03
    In a place resistant to climate change, the results may look very different. 4:06
    While google.com generally looks the same for everyone, 4:11
    it's subtle nudges like these that keep people in differing realities. 4:15
    It's one of the reasons why political polarization is at a 30 year high. 4:20
    Rashida Richardson is the Director of Policy Research at AI Now, 4:27
    a research institute studying the social implications of artificial intelligence. 4:32
    She has said that while there are anti-discrimination laws to protect 4:38
    people regarding housing, credit, and employment, they have limits. 4:42
    Racial disparities persist because of historical exclusion. 4:47
    In a paper she authored, the team researched predictive policing 4:50
    systems that forecast criminal activity. 4:55
    The predictions are flawed because they're trained on racially discriminatory data. 4:59
    The system took corrupt, illegal, and racially biased police 5:04
    activity in the past and created more of that for the future. 5:10
    MICHELLE: So what do you think? 5:15
    Should computers be making automatic decisions? 5:17
    Is it okay in some applications, but not in others? 5:19
    Consider its application in finance, insurance, 5:23
    healthcare, employment, and law enforcement. 5:27
    Should algorithms amplify any content? 5:30
    Who should decide? 5:33
    As machine learning is still relatively new, 5:35
    I don't think anyone has perfected this yet. 5:38
    Here are a few ideas. 5:41
    Carefully select training data. 5:42
    Ensure the algorithm is fed a dataset that reflects a wide spectrum of humanity and 5:46
    audit for biases. 5:52
    Put measures in place to regulate the system once it's released to the world. 5:53
    If an automated process relies on an algorithm to review it, 5:59
    provide an option for a manual review. 6:03
    An algorithm cannot understand the nuances of a complicated decision as a human can. 6:06
    Sometimes AI is not an appropriate solution, it has limitations. 6:13
    Build diverse teams. 6:17
    A diverse group minimizes bias and products and 6:21
    better serves a broad audience. 6:24
    Do your due diligence before releasing a product by 6:27
    conducting user testing with a diverse range of participants. 6:31
    Al is an emerging technology that's just beginning to develop best practices. 6:36
    Do you have any other ideas for making artificial intelligence ethical? 6:41
    For more information, 6:46
    check out the Algorithmic Justice League link in the teacher's notes. 6:47

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