With Big Data, we are creating artificial intelligences that no human can understand

by Viktor Mayer-Schönberger and Kenneth Cukier, in QZ.COM

Computer systems currently base their decisions on rules they have been explicitly programmed to follow. Thus when a decision goes awry, as is inevitable from time to time, we can go back and figure out why the computer made it. For example, we can investigate questions like “Why did the autopilot system pitch the plane five degrees higher when an external sensor detected a sudden surge in humidity?” Today’s computer code can be opened and inspected, and those who know how to interpret it can trace and comprehend the basis for its decisions, no matter how complex.

With big-data analysis, however, this traceability will become much harder. The basis of an algorithm’s predictions may often be far too intricate for the average human to understand

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As a society, we’ve often seen such new entities emerge when a dramatic increase in the complexity and specialization of a particular field produced an urgent need for experts to manage the new techniques. Professions like law, medicine, accounting, and engineering underwent this very transformation more than a century ago. More recently, specialists in computer security and privacy have cropped up to certify that companies are complying with the best practices determined by bodies like the International Organization for Standards (which was itself formed to address a new need for guidelines in this field).

Big data will require a new group of people to take on this role. Perhaps they will be called “algorithmists.” They could take two forms—independent entities to monitor firms from outside, and employees or departments to monitor them from within—just as companies have in-house accountants as well as outside auditors who review their finances.

These new professionals would be experts in the areas of computer science, mathematics, and statistics; they would act as reviewers of big-data analyses and predictions. Algorithmists would take a vow of impartiality and confidentiality, much as accountants and certain other professionals do now. They would evaluate the selection of data sources, the choice of analytical and predictive tools, including algorithms and models, and the interpretation of results. In the event of a dispute, they would have access to the algorithms, statistical approaches, and datasets that produced a given decision.

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There are no foolproof ways to fully prepare for the world of big data; it will require that we establish new principles by which we govern ourselves. A series of important changes to our practices can help society as it becomes more familiar with big data’s character and shortcomings. We must design safeguards to allow a new professional class of “algorithmists” to assess big-data analytics — so that a world which has become less random by dint of big data does not turn into a black box, simply replacing one form of the unknowable with another.  Read on in Quartz