Algorithms, Accountability, and Professional Judgment (Part 3)
So much of the public admiration for Big Data and algorithms avoids answering basic questions: Why are some facts counted and others ignored? Who decides what factors get included in an algorithm? What does an algorithm whose prediction might lead to getting fired actually look like? Without a model, a theory in mind, every table, each chart, each datum gets counted, threatens privacy, and, yes, becomes overwhelming. A framework for quantifying data and making algorithmic decisions based on data is essential. Too often, however, they are kept secret or, sadly, missing-in-action.
Here is the point I want to make. Big Data are important; algorithmic formulas are important. They matter. Yet without data gatherers and analyzers using frameworks that make sense of the data, that asks questions about the what and why of phenomena–all the quantifying, all the regression equations and analysis can send researchers, policymakers, and practitioners down dead ends. Big Data become worthless and algorithms lead to bad decisions.
Few champions of Big data have pointed to its failures. All the finely-crafted algorithms available to hedge fund CEOs, investment bankers, and Federal Reserve officials before 2008, for example, were of no help in predicting
Here is the point I want to make. Big Data are important; algorithmic formulas are important. They matter. Yet without data gatherers and analyzers using frameworks that make sense of the data, that asks questions about the what and why of phenomena–all the quantifying, all the regression equations and analysis can send researchers, policymakers, and practitioners down dead ends. Big Data become worthless and algorithms lead to bad decisions.
Few champions of Big data have pointed to its failures. All the finely-crafted algorithms available to hedge fund CEOs, investment bankers, and Federal Reserve officials before 2008, for example, were of no help in predicting
