在人類的絕大多數研究機構中，我們過去往往假設，所獲的信息都是小的、精確的、可以推測因果的。但是世界變了，因為數據變得巨大、處理飛快和非精確。雪上加霜的是，這些數據基本都由機器處理和作出預測。
千禧一代大都接受這樣的改變。過去的執政者曾經擔心過科技會暴露過多隱私，所以建設了一套管理機制（事實上互聯網的早期設計者的確“不太尊重”傳統的隱私和知識產權）。作者聲稱人們是愿意分享在線上分享個人信息的，他說這是一個“數據”的服務特性。
與此同時，數據分析的危險性從隱私權轉移到了“非確定性”（原文probability）：算法會預測一個可能性——你得心臟病的可能性，被給予貸款的可能性，甚至是犯罪的可能性。這導致了一個“倫理”性的問題關于人的直覺和數據的預測，如果人所認為的數據所說的相左該怎么辦？
In many ways, the way we control and handle data will have to change. We're entering a world of constant datapdriven predictions where we may not be able to explain the reasons behind our decisions. What does it mean if a doctor cannot justify a medical intervention without asking the patient to defer to a black box, as the physician must do when relying on a big-data-driven diagnosis? Will the judicial system's standard of "probable cause" need to change to "probabilistic cause" - and if so, what are the implications of this for human freedom and dignity?
New principles are needed for the age of big data, which we lay out in Ch.9. Although they build upon the values that were developed and enshrined for the world of small data, it's not simply a matter of refreshing old rules for new circumstances, but recognizing the need for new principles altogether.
The benefits to society will be myriad, as big data becomes part of the solution to pressing global problems like addressing climate change, eradicating disease, and fostering good governance and economic development. But the big-data era also challenges us to become better prepared for the ways in which harnessing the technology will change our institutions and ourselves.
Big data marks an import step in humankind's quest to quantify and understand the world. A preponderance of things that could never be measured, stored, analyzed, and shred before is becoming datafied. Harnessing vast quantities of data rather than small portion, and privileging more data of less exactitude, opens the door to new ways of understanding. It leads society to abandon its time-honored preference for causality, and in many instances tap the benefits of correlation.
The ideal of identifying causal mechanisms is a selfp-congratulatoryillusion; big data overturns this. Yet again we are at a historical impasse where "god is dead". That is to say, the certainties that we believed in are once again changing. But this time they are being replaced, ironically, by better evidence. What role is left for intuition, faith, uncertainty, acting in contradiction of the evidence, and learning by experience? As the world shifts from causation to correlation, how can we pragmatically move forward without undermining the very foundations to explain where we are, trace how we got here, and offer an urgently needed guide to the benefits and dangers that lie ahead.
