Big data and technology assessment: research topic or competitor?

ABSTRACT

With its promise to transform how we live, work, and think, Big Data has captured the imaginations of governments, businesses, and academia. However, the grand claims of Big Data advocates have been accompanied with concerns about potential detrimental implications for civil rights and liberties, leading to a climate of clash and mutual distrust between different stakeholders. Throughout the years, the interdisciplinary field of technology assessment (TA) has gained considerable experience in studying socio-technical controversies and as such is exceptionally well equipped to assess the premises and implications of Big Data practices. However, the relationship between Big Data as a socio-technical phenomenon and TA as a discipline assessing such phenomena is a peculiar one: Big Data may be the first topic TA deals with that is not only an object of inquiry, but also a major competitor, rivaling TA in several of its core functions, including the assessment of public views and visions, means and methods for exploring the future, and the provision of actionable knowledge and advice for political decision-making. Our paper explores this dual relationship between Big Data and TA before concluding with some considerations on how TA might contribute to more responsible data-based research and innovation.

KEYWORDS:

• Big data
• technology assessment
• responsible research and innovation
• interdisciplinarity

Notes on contributors

Gernot Rieder is a Ph.D. fellow at the IT University of Copenhagen. His dissertation investigates the rise of Big Data in public policy and the social, ethical, and epistemological implications of data-driven decision-making. Gernot serves as an assistant editor for the journal ‘Big Data & Society’.

Judith Simon is Full Professor for Ethics in Information Technologies at the University of Hamburg. She is co-editor of the journals ‘Philosophy & Technology’ and ‘Big Data & Society’. She also serves on the executive boards of the International Society for Ethics and Information Technology (inseit.net) and the International Association for Computing and Philosophy (iacap.org).

Notes

1. Although many definitions have been proposed (see Press 2014), there is ‘a pronounced lack of consensus about the definition, scope, and character of what falls within the purview of Big Data’ (Ekbia et al. 2015). One of the most popular characterizations is Laney’s (2001, 2012) notion of the ‘3Vs’, which focuses on measures of magnitude and conceptualizes Big Data as growth in data volume, velocity, and variety. Other approaches have shifted the focus from data properties to new analytical possibilities, describing Big Data science as a ‘God’s-eye view’ (Pentland 2012) that ‘lets us examine society in fine-grained detail’ (Pentland 2014). In contrast to such technology-oriented perspectives, scholars from the social sciences and humanities have pointed to the cultural dimension of Big Data, arguing that the real novelty of Big Data lies in the growing significance and authority of quantified information in ever more areas of everyday life (see Leonelli 2014). From this perspective, Big Data constitutes a complex socio-technical phenomenon that rests on an interplay of science, technology, ideology, and mythology (see Jurgenson 2014; boyd and Crawford 2012). It is this latter perspective that will guide our analysis.

2. Citizens’ passivity may have multiple causes. A survey by Turow, Hennessy, and Draper (2015) on consumer data collection in both digital and physical commerce, for instance, finds that people’s provision of personal information is not the result of either consent, ignorance, or indifference, but rather a sense of resignation and powerlessness, a feeling that it is futile to even try to manage and control what companies can learn about them.

3. For an overview of the TA landscape and its various strands, see van Est and Brom (2012) and Grunwald (2009).

4. While explicit references to Big Data were rare in the program of the 1st European TA Conference in Prague in 2013 (see PACITA 2013; Michalek et al. 2014), two years later, at the 2nd European TA Conference in Berlin, the term had become more common and a dedicated session sought to investigate the ‘Governance of Big Data and the Role of TA’ (see PACITA 2015).

5. See the ABIDA website: http://www.abida.de/en (Accessed 12 April 2017).

6. See POST’s Big Data program website: http://www.parliament.uk/mps-lords-and-offices/offices/bicameral/post/work-programme/big-data/ (Accessed 12 April 2017).

7. Another example is the recent report Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights (Executive Office of the President 2016).

8. For a concise overview of the ABIDA project, see: http://www.abida.de/en/content/abida-das-projekt (Accessed 12 April 2017).

9. To give but one example, when reporting on the status of the now abolished US Office of Technology Assessment (OTA) back in the early 1980s, Project Director and Senior Analyst Fred B. Wood writes that ‘OTA’s multidisciplinary staff […] of 80-90 professionals spans the spectrum of physical, life, and social sciences, engineering, law, and medicine’ (Wood 1982).

10. For more on TA’s ‘problem-oriented’ version of interdisciplinarity, see Schmidt (2008).

11. Regarding such compartmentalization in higher education, see Newell (2010); regarding research, see Pan, Boucherie, and Hanafi (2015).

12. For a deeper, historically grounded discussion of such participatory technology assessment (pTA), see Joss and Bellucci (2002).

13. In fact, reasons for public participation are manifold (see Wesselink et al. 2011). The argument proposed in this paragraph refers to David Collingridge’s well-known ‘dilemma of control’. Collingridge (1980) states: ‘The social consequences of a technology cannot be predicted early in the life of the technology. By the time undesirable consequences are discovered, however, the technology is often so much part of the whole economics and social fabric that its control is extremely difficult’. Like Collingridge, TA searches for ways and means to deal with and, both in theory and practice, overcome this quandary. For an insightful analytical discussion of the dilemma, its assumptions and relationship to TA, see Liebert and Schmidt (2010).

14. Quoted from the U.S. Congress Technology Assessment Act of 1972, Public Law 92-484, § 2(d) and § 3(c), which created the now defunct OTA, see: https://www.gpo.gov/fdsys/pkg/STATUTE-86/pdf/STATUTE-86-Pg797.pdf (Accessed 12 April 2017).

15. For a selective overview of public engagement methods, see Parliamentary Office of Science and Technology (2001); for a review and critical discussion of large-scale survey research – and its paradigms – see Bauer (2008).

16. For a list of participatory methods, including time and cost estimates, see Involve (2005) and the Participation Compass: http://participationcompass.org/article/index/method (Accessed 12 April 2017).

17. As an indication of this interest in a European policy context, consider studies such as the commissioned report Big Data Analytics for Policy Making (EC 2016b), events such as the EurActiv stakeholder workshop Big Data & Policy Making, see http://www.euractiv.com/section/digital/video/big-data-and-policy-making/, or research initiatives such as the Framework Programme 7 projects SENSEI, see http://www.sensei-conversation.eu/, and EuroSentiment, see http://eurosentiment.eu/ (Accessed 12 April 2017).

18. Data scientists are usually well aware of the various limitations of their craft. For a balanced account of prediction in the era of Big Data, see Silver (2012).

19. Consider, for instance, IBM’s advertising slogan for their predictive analytics products, which prompts customers to ‘optimize the future with better decisions today’. See: http://www.ibm.com/analytics/us/en/technology/predictive-analytics/ (Accessed 12 April 2017).

20. For an overview of the different practices and institutions of parliamentary TA in Europe, see Nentwich (2016).

21. In this respect, TA could also learn from the digital methods community, which has employed Web-based tools to map controversies around, for example, global warming (Weltevrede and Borra 2016), biofuels (Eklöf and Mager 2013), or GM food (Marres and Rogers 2000), embracing the epistemic opportunities of online data mining while remaining attentive to potential limitations and the perils of competitive marketization (see Rieder and Sire 2014).

Additional information

Funding

This work was supported by Austrian Science Fund (AT) [grant number P 23770].