Inspired by indy100’s article (1) , I decided to have a look at my Facebook settings, to see how the platform’s AI constructs ‘my’ advertisement preferences… and I got surprised twice. On one hand, I was surprised that the machine knows so much about me. On the other hand, I was also surprised to see some things that are really not ‘my things’ listed among my favorites – presumably just due to some of my random past clicks. Being surprised that I got surprised, I started wondering, does this big-data-based AI really know me? Can it never know me the same well as I know myself? Or is it possible that the big data already knows me so well that it reveals my potential preferences even before I myself could realize that they existed? Do I actually know myself less than the big data does?
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them….. Lately, the term "big data" tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set (Wikipedia)
Intuitively, big data gives people an objective and rational impression. Most of us would expect that such massive collections of information on human behavior must show human life in its perfect reality. Thus, big data is used to predict the unknowns, the potentials, and thoughts of others. Facebook’s advertisement preference is trying to map your remote psyche out so as to predict your future consumption behavior. American courts experiment with letting an AI decide how long criminals should stay in prison to minimize the chances that they will reoffend after being released. Alipay and WeChat will let you take a loan based on all the data they have accumulated on your shopping habits.
The question necessarily arises: does big data really reveal the patterns of human life or, on the contrary, does it actually control our behavior and shape the patterns?
As Harvard University’s Rebecca Lemov argues in Aeon Magazine, big data ‘is not an objective measure of who we are’, but rather a ‘portrait of our hopes and desires’.2 Alarmingly, this self-generated big data – nothing more than ‘clickstreams and navigational choices’, as Ms. Lemov puts it – is in turn used by governments to shape socially significant policies and it even effects our choices, allegiances and relationships. (2)&(3)
In her Ted talk(4) technology ethnographer Tricia Wang showed us that the big data everyone seems to be crazy about actually fails to tell the human story. It lacks insights on the intimate and personal part of human individuals. By applying traditional anthropological methods to obtain qualitative evidence for commercial marketing purposes, Ms. Wang came to a conclusion that was very different from what big data had predicted. She found that the evidence from big data didn’t sufficiently understand the potential customers’ culture and thus failed to predict the market. As she summarized her experience: ‘precious data from humans, like stories, emotions and interactions cannot be quantified’. She termed this missing component of big data models ‘thick data’, closely matching what Ms. Lemov suggested in her Aeon article (2):
‘We need to see the human in the datamachine … it is necessary again to bear in mind that the data is not onlygenerated about individuals but also made out of individuals. It is human data.’
Ignoring all evidence, contemporary society seems to find it hard to waive its enthusiasm on big data. Ms. Wang’s argues that this enthusiasm is linked to humans’ cognition arrogance that values the measurable over the immeasurable. She is right to remind us that big data satisfies our quantification bias well by giving us the comfortable ‘illusion that we know everything’.(4)
To be sure, there are plenty of questions to be answered regarding big data’s role in our life. And yet more will surely emerge in the not too distant future. Is it possible to describe human life in an objective, quantifiable way? Does the general enthusiasm for big data result from our difficulty to process and analyze thick data? Regarding to the definition of big data (data sets that are so large or complex…), I wonder how to understand the connections among quantitative data, qualitative data, big data and thick data? Is thick data actually part of big data? What if with the fast development of technology our current focus on big data will turn out to be nothing more than a necessary step towards processing thick data?
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