在人類的絕大多數(shù)研究機(jī)構(gòu)中叉庐，我們過(guò)去往往假設(shè)舒帮，所獲的信息都是小的、精確的陡叠、可以推測(cè)因果的玩郊。但是世界變了，因?yàn)閿?shù)據(jù)變得巨大枉阵、處理飛快和非精確译红。雪上加霜的是，這些數(shù)據(jù)基本都由機(jī)器處理和作出預(yù)測(cè)兴溜。
千禧一代大都接受這樣的改變侦厚。過(guò)去的執(zhí)政者曾經(jīng)擔(dān)心過(guò)科技會(huì)暴露過(guò)多隱私，所以建設(shè)了一套管理機(jī)制（事實(shí)上互聯(lián)網(wǎng)的早期設(shè)計(jì)者的確“不太尊重”傳統(tǒng)的隱私和知識(shí)產(chǎn)權(quán)）拙徽。作者聲稱人們是愿意分享在線上分享個(gè)人信息的刨沦，他說(shuō)這是一個(gè)“數(shù)據(jù)”的服務(wù)特性。
與此同時(shí)膘怕，數(shù)據(jù)分析的危險(xiǎn)性從隱私權(quán)轉(zhuǎn)移到了“非確定性”（原文probability）：算法會(huì)預(yù)測(cè)一個(gè)可能性——你得心臟病的可能性想诅，被給予貸款的可能性，甚至是犯罪的可能性淳蔼。這導(dǎo)致了一個(gè)“倫理”性的問(wèn)題關(guān)于人的直覺(jué)和數(shù)據(jù)的預(yù)測(cè)侧蘸，如果人所認(rèn)為的數(shù)據(jù)所說(shuō)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.
