在人类的绝大多数研究机构中，我们过去往往假设，所获的信息都是小的、精确的、可以推测因果的。但是世界变了，因为数据变得巨大、处理飞快和非精确。雪上加霜的是，这些数据基本都由机器处理和作出预测。
千禧一代大都接受这样的改变。过去的执政者曾经担心过科技会暴露过多隐私，所以建设了一套管理机制（事实上互联网的早期设计者的确“不太尊重”传统的隐私和知识产权）。作者声称人们是愿意分享在线上分享个人信息的，他说这是一个“数据”的服务特性。
与此同时，数据分析的危险性从隐私权转移到了“非确定性”（原文probability）：算法会预测一个可能性——你得心脏病的可能性，被给予贷款的可能性，甚至是犯罪的可能性。这导致了一个“伦理”性的问题关于人的直觉和数据的预测，如果人所认为的数据所说的相左该怎么办？
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.
