Executive Briefing: Understanding the cult of prediction
We're living in a culture obsessed with predictions. In politics and business, we collect data in service of the obsession. But our need for certainty and control leads some organizations to be duped by unproven technology or pseudoscienceoften with unforeseen societal consequences. Farrah Bostic looks at historicaland sometimes funnyexamples of sacrificing understanding for "data."
|Talk Title||Executive Briefing: Understanding the cult of prediction|
|Speakers||Farrah Bostic (The Difference Engine)|
|Conference||Strata Data Conference|
|Conf Tag||Make Data Work|
|Location||New York, New York|
|Date||September 24-26, 2019|
We’re living in a cultural moment is obsessed with making predictions. In politics and in business, we’re constantly coming up with ways to collect more data for a singular purpose: to predict what will happen next. This overwhelming desire for prescience shapes the way we design, measure, and understand everything from products and marketing to politics and movements. Good predictions demand both precision and accuracy. Farrah Bostic walks you through how, in the quest to get more and more granular about how people will behave in the future, in the hopes that we can anticipate or manipulate that behavior, businesses are often tempted to rely on emerging or untested technologies—and sometimes pseudoscience—to get the “data” that fuels those predictions. While this moment seems to be particularly defined by prediction, the practice goes back to (at least) the first lie detectors and has come to encompass practices like hypnosis, technology like medical imaging, and encoded anthropological approaches like microexpressions. But the implications are worse than wasting money and time. Businesses and brands are sacrificing the opportunity to understand things deeply and are simultaneously creating social negative externalities, like normalizing surveillance and misinformation, undermining public trust and values, and dehumanizing the very people whose behavior we want to predict.