This workshop seeks to explore the impacts of big data across four domains: environment; urban structures/cities; bio-medicine/life sciences and cyber-resilience/security. The workshop will apply approaches from the social science and humanities to explore the socio-technical context in which big data in Asia are generated and the various (re)uses to which they are put. It will explore the wider societal transformations – ranging from commercial to governmental and scientific – while bearing in mind that technological developments cannot be neatly separated from the social, economic and political challenges ahead.
In Asia, “big data” has begun to be recognized a significant economic and political force. The Singapore government appointed its first “chief data scientist” in 2014, promising to develop the nation’s capacity for data analysis to improve service delivery in fields such as health care and transport. In China, Web businesses such as Baidu, Alibaba, and Tencent are already massive data-owners and are investing heavily in big data mining and analysis research. Amidst this enthusiasm for “big data,” there has been limited reflection on the broader significance of the transformations that it promises. The social context of the creation of data, culturally relative interpretations of scale, and temporality and historicity of how data have been made to work in Asia are relatively unknown. As well as its potential benefits, big data poses significant social, economic, and political challenges. Unresolved questions include: Who has access to data and who should have access? Who owns and who should own data? Do individuals have a right to their own data? These and other questions reflect the critical importance of comparing how different governing regimes and philosophies and forms of capital management inflect how data is used or thought about in the highly heterogeneous continent. To what extent does the collection of personal data lead to new forms of discrimination? Is more data always better? How do scientists and their societies decide when data is conclusive? Do “big data” generate greater objectivity? Will access to “big data” create new “digital divides”? These questions and the challenges they pose are of immediate regional and global significance.
Although a growing number of empirical studies of “big data” are emerging, few are focused on Asia. This workshop aims to move towards redressing this by calling attention to the need for comparative research. One starting assumption is that the meanings of data are socially and culturally determined; data is not universal. This implies that data and “big data” must be studied in its specific local social and cultural contexts. More specifically, understanding the meanings and uses of data in Asia will require studying it in various local contexts in order to develop case studies that can be used for comparative purposes and to identify under-addressed issues and research questions. Such an approach resists facile cultural assumptions about “Asia” and ill-informed attempts to apply lessons or models developed for use in less than relevant contexts. The workshop will engage particularly with the following dimensions:
1) Generation, use, and re-use of big data
The focus is on where big data come from, how they are selected, appropriated and validated. In other words – what is becoming data and how, who has access to them and who owns them. This includes processes of generating data; how data are transformed into information; how they gain reliability, validity and credibility and which kind of work they perform. Attention to the genealogy of data introduces the need for a historical dimension that will will yield many productive insights as to how numbers — used for purposes from trade and business, to government and religious purposes — have been used to create or assert power or to counter it. The historical dimension dissociates “big data” from merely the history of the recent past and will also provide a valuable backdrop to address and question what is actually “new” about big data. An STS approach provides for rich ethnographic descriptions of the diverse settings in which Big data practices emerge, for which purposes, who the users are as well as an analysis of the outcomes. More recently, predictive analytics has expanded the range of predictions, serving the objectives of a variety of users. The uses of big data have also raised new sensitive issues, such as protection of privacy and unresolved questions of authorship and ownership. STS also highlights the performativity of big data, i.e. their capacity to act and exert influence through their use.
2) Communication and visualization of big data
Visualization plays a crucial part in all aspects of big data. It is indispensable in a technical sense for transforming data into images, but equally for communicating with a variety of audiences, users and practitioners. What are the technics of such visualizations and how do they play a role in public or individual knowledge production processes? This workshop will explore the relationship between big data and visualization and the role of images in presenting and representing big data.
3) Data policy & big data literacy
The workshop will address policy-relevant issues based on a better understanding of present and future practices and uses in the above mentioned domains. Policies across a range of domains are increasingly informed by big data. What are the benefits, risks, and limits of big data-based policy? One particularly important aspect of this policy development involves seeking ways to increase data literacy in order to best position Asian citizens to take advantage of big data in the future.
4) Global and geopolitical regional implications
Big data producers and aggregators are currently centered around North America, particularly within corporations such as Amazon, Google, and Facebook. What geopolitical implications do such corporatized practices of data have for Asian nations and peoples? How may Asia best position itself to benefit from big data given regional distributions of data and data production? Moreover, to what extent is capital-production an end-goal of data considering the para-governmental structures constructed by the private entities of the multi-national scale? What alternative approaches are being engendered in Asia? What kinds of policies regulating big data and big data practices are necessary to mitigate the risks posed by big data?