T14P03 - The Data/Sensor Revolution and Public Policy

Topic : Science, Internet and Technology Policy

Panel Chair : Jouke de Vries - jouke.de.vries@rug.nl

Panel Second Chair : Gideon Shimshon - g.j.shimshon@fgga.leidenuniv.nl

Panel Third Chair : Sarah Giest - s.n.giest@fgga.leidenuniv.nl

Objectives and Scientific Relevance of the panel

Call for papers

Session 1 Not everything that can be counted counts

Discussants

Jos BERENS - j.b.berens@fgga.leidenuniv.nl - Leiden University, Centre for Innovation - Netherlands

WAHYUDDIN YASSER - yasser.wahyuddin@gmail.com - EVS-RIVES, ENTPE - France

Not everything that can be counted counts, and not everything that counts can be counted: Debunking the myths of data driven decision-making

Bibi van den Berg - b.van.den.berg@fgga.leidenuniv.nl - Universiteit Leiden, Faculty of Governance and Global Affairs, Institute for Security and Global Affairs - Netherlands

Ruth Prins - r.prins@fgga.leidenuniv.nl - Universiteit Leiden, Faculty of Global Affairs, Institute for Security and Global Affairs - Netherlands

This paper examines a number of underlying assumptions in the debate about using Big Data and data analytics for policy making. It will point out three flaws in the reasoning of the previous paragraph:

First, more data does not alleviate the limitations of human beings. No matter how much data or information or even knowledge we throw at human beings, they will always be flawed decision-makers with bounded rationality, who cannot even cope with having to choose between more than 7 kinds of jam, let alone make rational sense of ever more data and information.

Second, data analytics systems are not as ‘objective’ (read: rational) as they may appear to be. Many of these systems use algorithms designed by humans, which means that the flawed, biased, stereotypical thinking of humans is implemented into them. Studies have shown extensively that there are significant risks with respect to discrimination and privacy in the use of Big Data (not only for public policy but across the board).

Third, human beings may have difficulty interpreting the outcomes of analyses of large data sets, and the recommendations systems make, not only because they themselves are not (entirely) rational (see under ‘First’), but also because of human-computer interaction. Extensive empirical studies in the field of Human Computer Interaction have revealed that human beings project their entire repertoire of social-emotional skills on machines whenever these show even a semblance of intelligence or even animation. ‘Anthropormorphisation’ revolves around the idea that human beings ascribe intentions, ‘aliveness’, feelings and a level of intelligence to inanimate objects, even pretty dumb ones. This means they may be inclined to uncritically take seriously the outcomes of data analytics operations, which further aggravates the problems mentioned under ‘Second’.

 

After this critical assessment of some of the key assumptions underlying the debate about using Big Data for policy making the paper will end with a normative question: should we embrace widespread use of Big Data for this purpose, and if so, under which conditions should be do so. The authors will argue that Big Data and data analytics have much to offer to policy makers when decisions must be made on policies and interventions that do not involve human actors, e.g. for infrastructural projects or industry regulations. For policies and interventions that do involve human actors (citizens), caution is required. As David Human famously argued: can does not imply ought.

Data-Driven Innovation as a Strategy : Towards Responsible Innovation and Adaptation for Humanitarian Response and Sustainable Development

Thomas Baar - t.j.baar@fgga.leidenuniv.nl - Centre for Innovation (Leiden University) - Netherlands

Jos BERENS - j.b.berens@fgga.leidenuniv.nl - Leiden University, Centre for Innovation - Netherlands

The unprecedented availability of large-scale data is profoundly impacting the development and humanitarian sector. Governments, companies, researchers, civil society and other organisations are actively experimenting, innovating and adapting to the new opportunities associated with new types of data and methodologies. Particular focus is brought in these fields as to how data-driven innovation could fill critical information gaps and therewith support organisations in their strategy and operations as well as policy development. This drive for innovation has been supported in various official statements (i.a. UN Data Revolution, 2014), shared commitments (i.a. Grand Bargain, 2016) and through the establishment of new partnerships (i.a. Global Partnership for Sustainable Development Data and the Global Alliance for Humanitarian Innovation). Nonetheless, development organisations and humanitarian actors themselves tend to have limited aptitude for conducting data-driven innovation as they often lack required / necessary resources, capacities and knowledge (Baar et al, forthcoming). This leads to the current state of affairs, wherein data-driven innovation within these sectors is still usually facilitated and driven by external actors.

 


At the moment, we see two critical obstacles to the success of data-driven innovation for humanitarian response and sustainable development. While there is plenty of experimentation happening, little is know about the potential risks of many of these new tools and methods (Lepri et al., 2016). Especially for longer-term risks, more research is needed to prevent this sector from deploying tools that eventually might harm the populations the sector aims to serve. Risks include the exposure of sensitive (personal) information; false information being generated by lack of training or mal-intent; and secondary risks including loss of target population’s trust, legal liability for aid agencies, and other negative consequences. This hence brings forth a certain ‘data responsibility gap’ to data-driven innovation. Simultaneously, data-driven innovations receive limited uptake by humanitarian and development actors. This ‘adaptation gap’ originates from: (1) a lack of awareness of the potential of new types of data-driven innovation and how to realise it; and (2) a disconnect between the design of tools and methods, and the needs and desires of their end-users; and (3) the required investment for adequate implementation of data-driven innovation in existing processes.

 


On the basis of two case studies, we will define three core principles for designing data-driven innovation in the humanitarian and sustainable development sectors of data-driven tools and methods. Firstly, we will reflect on the realisation of a data platform to test core assumptions underlying the work of Aids Fonds and its partners by combining and analysing various data source to answer strategic questions. The second study focuses on the development of a forecasting model on the basis of standardised consumption data to support demand and order planning as part of the operations of Médecins Sans Frontières/Doctors Without Borders (MSF). We will show that leveraging the potential that new types of data and methods have to offer, requires: (1) strong collaboration with end users and others with a vested interest in their deployment and use during the innovation process; (2) an assessment of potential data responsibility challenges to inform the design of data-driven innovation; and (3) ensuring that data-driven innovation is not perceived as a product, but rather as a strategy including training in use of the tool or method to ensure adaptation into current operations and responsible use.

To What Extent the Grand Lyon Metropole can harness the Smart Meter Project towards the Governance of Territorial Climate Energy Plan (PCET) Study case: Smart Electric Lyon project initiated by EDF [French Electric Utility Company]

WAHYUDDIN YASSER - yasser.wahyuddin@gmail.com - EVS-RIVES, ENTPE - France

In 2012, EDF officially launched smart meter experimentation project in Lyon Metropole area. The project established a consortium named Smart Electric Lyon (SEL) brought in around twenty industrials in energy sector, electrical home automation, information and communication, and supported financially by ADEME ( the French environment and energy agency).

 

Technically, the main purpose of SEL is to bring the solutions that are being tested using a new smart meter equipment sensor named “Linky” installed in 25,000 homes and 100 businesses. SEL offers information services, technical solutions, and new tariffs to help the consumers to better manage their daily electricity consumption, which are associated with Linky.

 

These new advanced features involve the emergence of a new type fine-grained data of customers’ energy consumptions at the level of households or industrial units which is captured automatically by sensor Linky in a real time basis. The data is bi-directionally unfolded and communicated in order to be available for public and private urban managers and also for the customers themselves. In doing so, sensor Linky establishes new data sources beyond traditional methods, censuses, questionnaires, and registries [Desroisiers, 1993], frames what many authors called “data revolution” [Kitchin 2014, Townsend 2013, Cukier and Mayer-Schoenberger 2013] which allows both the traceability and the interoperability of the very fine-grained data of people pattern behaviors [Boullier 2015, Lupton 2014]. 

 

Thus, this paper aims principally to determine the consequence of the Big Data Revolution in the daily practice of political and administrative systems. But, first and foremost we tend to apply this leading question within the urban governance issues, whether such big data revolution promotor like SEL could possibly employed as an instrument for the urban managers (notably in the PCET program of Grand Lyon) as enlightened by Margetts and Hood as “detector and effector” of urban governance in the digital era [Margetts & Hood, 2013].

 

A profound and intense observations, which are empirically conducted closely within the Grand Lyon authority, SEL consortium, its instigators, and its first enforcers, had been constituted as our primary source to construct this paper. A cross-actors investigation allow us to grasp the dynamic of SEL involvement in the industrial and also public governance. It is completed by documentary analysis, and ethnographic sequences of observations notably realized within the showroom of SEL.

 

The results of our research shows that Grand Lyon possesses an auspicious ecosystem for the private sectors like EDF to promote smart meter project. SEL has been seen as a successful achievement bearing in mind that SEL has currently become a national standard reference of smart meter. It is a powerful instrument for EDF in respect of market electricity liberalization and a means to readjust the company’s market strategy. For the Grand Lyon, the impact of big data revolution to their political practices is still vague, as incorporating the systems to PCET policy remains the subject of power struggle between Grand Lyon and the existence of multi-layers’ stakeholders, public, private, and intra-national agencies.

 

Keywords: Lyon ecosystem, SEL, Linky, PCET, Data Revolution, Urban governance

 

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