Big Data Teaches Us We Are Big Paranoid
November 18, 2016
I love election years! Actually, that is sarcasm. Election years bring out the worst in Americans. The media runs rampant with predictions that each nominee is the equivalent of the anti-Christ and will “doom America,” “ruin the nation,” or “destroy humanity.” The sane voter knows that whoever the next president is will probably not destroy the nation or everyday life…much. Fear, hysteria, and paranoia sells more than puff pieces and big data supports that theory. Popular news site Newsweek shares that, “Our Trust In Big Data Shows We Don’t Trust Ourselves.”
The article starts with a new acronym: DATA. It is not that new, but Newsweek takes a new spin on it. D means dimensions or different datasets, the ability to combine multiple data streams for new insights. A is for automatic, which is self-explanatory. T stands for time and how data is processed in real time. The second A is for artificial intelligence that discovers all the patterns in the data.
Artificial intelligence is where the problems start to emerge. Big data algorithms can be unintentionally programmed with bias. In order to interpret data, artificial intelligence must learn from prior datasets. These older datasets can show human bias, such as racism, sexism, and socioeconomic prejudices.
Our machines are not as objectives as we believe:
But our readiness to hand over difficult choices to machines tells us more about how we see ourselves.
Instead of seeing a job applicant as a person facing their own choices, capable of overcoming their disadvantages, they become a data point in a mathematical model. Instead of seeing an employer as a person of judgment, bringing wisdom and experience to hard decisions, they become a vector for unconscious bias and inconsistent behavior. Why do we trust the machines, biased and unaccountable as they are? Because we no longer trust ourselves.”
Newsweek really knows how to be dramatic. We no longer trust ourselves? No, we trust ourselves more than ever, because we rely on machines to make our simple decisions so we can concentrate on more important topics. However, what we deem important is biased. Taking the Newsweek example, what a job applicant considers an important submission, a HR representative will see as the 500th submission that week. Big data should provide us with better, more diverse perspectives.