Episode 17: Daylight Savings Time and Confirmation Bias – Show Notes This episode was inspired by a clear lack of sleep. Today, we are discussing confirmation bias. Confirmation bias is when people seek out only the evidence that supports their point of view while dismissing contrary evidence. An example might be a smoker highlighting a […]
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Episode 8: Love Ahoy! How OKCupid Tested Anchoring Bias – Show Notes In 2014, OKCupid revealed they had conducted an experiment to test the effects of showing false compatibility rates to users. The experiment was designed to test whether their algorithm was truly generating more meaningful conversations. There were two stages of the experiment. First, […]
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Episode 7: Anchoring Bias – Show Notes Today we are talking about one aspect of why first impressions are so important – anchoring bias. Anchoring bias is when the first piece of information we receive about something (our first impression of ir) is weighted more heavily than it should be. Unlike statistical sampling biases, anchoring […]
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Episode 6: Google Has a Gorilla Problem – Show Notes Google is a forerunner in many areas of data science and artificial intelligence. Yet one problem seems insurmountable for them – properly distinguishing between dark-skinned humans and gorillas. In 2015, an African-American software developer, Jacky Alciné, looking through their automatically-tagged images in Google Photos saw […]
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Episode 5: Hired by Algorithm – Show Notes Finding and applying for jobs is a painful task – made worse by hiring bias pervasive in the process. In today’s episode, we consider advances in helping to eliminate hiring bias from job description to interview. We call out a few of the biases lurking in HR […]
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Episode 4: Statistical Sampling Bias – Show Notes Bias sneaks in to algorithms and data science from multiple sources. Primarily, it comes from statistical or cognitive biases that then lead to biased conclusions or results. In today’s episode, we look at four types statistical sampling bias to understand how biased samples skew algorithms. [2:54] Selection […]
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