Author: Cosimo Accoto

“When data mining aims to disclose information about the moral competences and values of individuals or groups – an undertaking we call ‘morality mining’ –, novel ethical problems emerge. These are only partially covered by the current debate on ethical data mining focusing on privacy with respect to discrimination, threats to autonomy, misuse of data, and the consequences of erroneous information. An ethics of morality mining is of particular relevance for research in social science and psychology that increasingly relies on data emerging from social networks, media portals, etc., where people act from or at least in accordance with their own values. In this conceptual contribution, we outline the basic idea of morality mining, explain why we believe that morality mining is associated with novel ethical problems, and suggest ways to address these problems”

http://www.encyclog.com/_upl/files/ChristenAlfanoBangerterLapsley_MoralityMin… 

“Recently, Savage and Burrows (2007) have argued that one way to invigorate sociology’s ‘empirical crisis’ is to take advantage of live, web-based digital transactional data. This paper argues that whilst sociologists do indeed need to engage with this growing digital data deluge, there are longer-term risks involved that need to be considered. More precisely, C. Wright Mills’ ‘sociological imagination’ is used as the basis for the kind of sociological research that one might aim for, even within the digital era. In so doing, it is suggested that current forms of engaging with transactional social data are problematic to the sociological imagination because they tend to be ahistorical and focus mainly on ‘now casting’. The ahistorical nature of this genre of digital research, it is argued, necessarily restricts the possibility of developing a serious sociological imagination. In turn, it is concluded, there is a need to think beyond the digitized surfaces of the plastic present and to consider the impact that time and temporality, particularly within the digital arena, have on shaping our sociological imagination”

http://onlinelibrary.wiley.com/doi/10.1111/j.1467-954X.2012.002120.x/abstract…

“Current science is characterized by the almost exclusive use of data in digital form, with the overwhelming quantities of data referred to as the “data deluge” largely arising due to the deployment of digital tools and, techniques, and computational methods (Hey and Trefethen 2003). We have become used to talking about data in terms of hundreds of terabytes and tens of petabytes, and in all forms of research new ways of storing, retrieving, organizing and processing these huge quantities of data are in constant demand. Some see this as a new paradigm of scientific method: data-driven or data-intensive science which is re-shaping our understanding of what it means to be a scientist and to do scientific research (Hey, Tansley, and Tolle 2009). If this is so, the visual will play a crucial role in this emerging mode of conducting science as visual renderings of all forms mediate and shape scientists’ access to data. There is an irony in this. The huge quantities of digital data and the computing know-how and power to deal with them promise new insights and breakthroughs in science by sheer dint of quantification”

http://spontaneousgenerations.library.utoronto.ca/index.php/SpontaneousGenera…

“Ethnography, an approach for studying everyday life as lived by groups of people, provides powerful resources for the study of the cultures of virtual worlds. As ethnographers, what interests us about virtual worlds is not what is extraordinary about them, but what is ordinary. We are intrigued not only by the individuals in a group, but by the sum of the parts. We aim to study virtual worlds as valid venues for cultural practice, seeking to understand both how they resemble and how they differ from other forms of culture. We do this by immersing our embodied selves within the cultures of interest, even when that embodiment is in the form of an avatar, the representation of self in these spaces. The goal of this handbook is to provide ethnographers with a practical set of tools and approaches for conducting successful fieldwork in virtual worlds”

http://press.princeton.edu/chapters/s9882.pdf

“The term “Big Data” has been buzzing around in the IT industry for the last few months.  Top universities in the nation are rushing to offer courses on it. Although data-rich companies such as Amazon, Google, and Yahoo! started this process, traditional companies are now exploring it as well. It is a prominent field that could transform management as we know it. In this paper, the authors look at the implications Big Data has for the overall management and direction of  a company, specifically in terms of business strategy. To be sure, this paper is not about learning Big Data, but rather how it integrates with the overall strategy of a company. We begin by defining what Big Data is, and what it is not. We then look at what motivated us to write about big data and its management implications. We also look at applications of Big Data, specifically Hadoop and Google BigQuery”

http://media.wix.com/ugd/36cf28_9daac86c5ceeae18162159a8fb968920.pdf 

“There is, however, a new flavor of innovation on the scene: Big Data. “Big Data”is shorthand for the combination of a technology and a process. The technology is a configuration of information processing hardware capable of sifting, sorting and interrogating vast quantities of data in very short times. The process involves mining the data for patterns, distilling the patterns into predictive analytics, and applying the analytics to new data. Together, the technology and the process comprise a technique for converting data flows into a particular, highly data-intensive type of knowledge. The technique of Big Data can be used to analyze data about the physical world—for example, climate or seismological data—or it can be used to analyze physical, transactional, and behavioral data about people. So used, it is vastly more nimble than old category-driven profiling developed in the late twentieth century and now widely criticized. According to its enthusiasts, Big Data will usher in a new era of knowledge production and innovation, producing enormous benefits to science and business alike.According to its critics, Big Data is profiling on steroids, unthinkably intrusive and eerily omniscient”.

http://www.harvardlawreview.org/symposium/papers2012/cohen.pdf

“It seems like everyone been talking about “big data” recently, speculating on the future of AI and intelligent systems. Big data has been characterized in many ways, from Doug Laney’s original 2001 “3Vs” model to the various recent extended “4Vs” descriptions. Laney’s three Vs are volume, velocity, A Big-Data Perspective on AI: Newton, Merton, and Analytics Intelligence for Complex Systems and variety; the fourth V could be variability, virtual, or value, depending on whom you ask. To most, those Vs indicate “bigness”—big size, fast movement, many types, and significant impact. To me, the “bigness” of big data is derived from its “smallness,” or more precisely, for its inclusion and use of data stemming from all degrees of volume, velocity, variety, value, variability, and so on, whether virtual or real. In particular, big data implies that the long-tail effects on personal living and business operations will be a normal mode in the future. But what does big data really mean in the era of cyberspace?”

http://origin-www.computer.org/csdl/mags/ex/2012/05/mex2012050002.pdf

Useful to sofisticate current (trivial) discourses about data visualization and knowledge

http://spontaneousgenerations.library.utoronto.ca/index.php/SpontaneousGenera…

“As data-intensive and computational science become increasingly established as the dominant mode of conducting scientific research, visualisations of data and of the outcomes of science become increasingly prominent in mediating knowledge in the scientific arena. This position piece advocates that more attention should be paid to the epistemological role of visualisations beyond their being a cognitive aid to understanding, but as playing a crucial role in the formation of evidence for scientific claims. The new generation of computational and informational visualisations and imaging techniques challenges the philosophy of science to re-think its position on three key distinctions: the qualitative/quantitative distinction, the subjective/objective distinction, and the causal/non-causal distinction”

http://spontaneousgenerations.library.utoronto.ca/index.php/SpontaneousGenera…