Big data’s escalating interest in and successful use of preemptive predictions as a means of avoiding risk becomes a catalyst…

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Big data’s escalating interest in and successful use of preemptive predictions as a means of avoiding risk becomes a catalyst for various new forms of social preemption. More and more, governments, corporations, and individuals will use big data to preempt or forestall activities perceived to generate social risk. Often, this will be done with little or no transparency or accountability. Some loan companies, for example, are beginning to use algorithms to determine interest rates for clients with little to no credit history, and to decide who is at high risk for default. Thousands of indicators are analyzed, ranging from the presence of financially secure friends on Facebook to time spent on websites and apps installed on various data devices. Governments, in the meantime, are using this technique in a variety of fields in order to determine the distribution of scarce resources such as social workers for at-risk youth or entitlement to Medicaid, food stamps, and welfare compensation.

Of course, the preemption strategy comes at a significant social cost. As an illustration, consider the practice of using predictive algorithms to generate no-fly lists. Before the development of many such lists in various countries, high-risk individuals were generally at liberty to travel—unless the government had a sufficient reason to believe that such individuals were in the process of committing an offense. In addition to curtailing liberty, a no-fly list that employs predictive algorithms preempts the need for any evidence or constitutional safeguards. Prediction simply replaces the need for proof.

Taken to its logical extreme, the preemption philosophy is not merely proactive—it is aggressive.

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Prediction, Preemption, Presumption; How Big Data Threatens Big Picture Privacy. By Ian Kerr and Jessica Earle. 

Seminal reading of Preemptive Prediction algorithms and big data. 

(viaalgopop)