Deliverable D2.3 Report on Ethical and Social Issues

This Deliverable identifies and examines various societal and ethical issues that are relevant to the production of, access to, linking of and re-use of big data in the transport sector.
Chapter 2, on the one hand, discusses the concept of big data, its particular characteristics, and its possible use in the transport sector. On the other hand, it delves into the interaction between ethical and social issues and the ways to integrate these into the existing policy framework. In Chapter 3, the authors examine the various identified ethical and social issues and discuss the challenges and opportunities that may arise in this respect, coming up with notably the following findings:

  • Trust: Although the research in trust has already become relatively mature, the huge amount and diversity of data and data sources provides lots of new opportunities but at the same time poses many challenges for online trust, notably in the context of transportation.
  • Surveillance: Considering the role of government agencies and their increasing requests of information to the private sector for public security purposes, it appears necessary to adopt specific rules to regulate the information flow, to define the rights over data and to ensure adequate enforcement.
  • Privacy (including transparency, consent and control): The advent of the GDPR has had a considerable impact in the domains of privacy, transparency, consent and control. This strengthened legal framework is likely to respond to several ethical issues and thus improve end users' trust in the use of personal data in a big data context.
  • Free will: Although big data-driven profiling practices can limit free will, a huge part of what we know about the world comes from data analysis. Careful and appropriate information analysis can open up plenty of chances and might reduce the limitations and problems for free will.
  • Personal data ownership: This Deliverable concludes that a claim of ownership by a data subject in its personal data would be hard to sustain. Nevertheless, in a big data context, different third-party entities may try to claim ownership in (parts of) a dataset, which may hinder the use of big data, including in the transport sector.
  • Discrimination: Using big data analytics to improve business processes or provide personalised services may lead to discrimination of certain groups of people. Also, the "Digital Divide", i.e. the social differences in access to technology and education or skills to use it, may lead to data-driven discrimination.
  • Environmental: There are trade-off or rebound effects from the use of big data in transport, which limits the effect of big data exploitation or creates unintended consequences. Such trade-off or rebound effects will be further assessed in Deliverable D2.4.

Finally, the last Chapter serves as a conclusion and introduces possible ways of moving forward to encourage the production of, access to, linking of and re-use of big data in the transport sector, with a particular focus on the EU. Particularly, Chapter 4 examines whether regulatory intervention or ethics-by-design are appropriate solutions to the challenges caused by ethical and social issues in relation to big data. The conclusion is that regulatory intervention is not desirable. Instead, the authors advocate an approach whereby ethics-by-design is recognised as an EU legal principle, similarly to privacy-by-design, and is supplemented by self-regulation and soft law. Section 4.3.1 aims to provide inspiration for ethics-by-design core implementation principles to be developed further in working groups at EU level.

Deliverable 2.1 Report on Economic and Political Issues

The proliferation of data availability is reshaping the economic and political realm. On the one hand, big data enables private and public parties to provide better quality services and products. On the other, the usage of data has led to policy response for limiting (e.g. the GDPR) or enabling (e.g. EU’s policy towards a ‘digital single market’) the application of big data.
This report aims at revealing the wider economic and political issues involved with utilizing big data by elaborating on the interaction between transport actors (demand-, supply-, external- and governance actors) and their role in the data economy (as data users, suppliers or facilitators). Subsequently, the interaction between these actors is described on various levels.
On the firm level, private parties use big data use big data for improved situational awareness of the transport system, improving the capacity of transport networks, improving transportation services and facilitating the shift to sustainable transport.
On the industry level, the data economy is expected to grow rapidly in the coming years. While the EU data economy remains to be in a deficit compared to the US regarding structural factors (fewer data SME), cultural/educational factors (ability to create and keep data-related skills), and the presence of IT giants, there is a healthy presence of digital start-ups and innovation capacity.
On the national level, governments utilize big data in improving organisational performance and in service provision and policy making. Subsequently, big data is applied in transport related government tasks, including transport planning, traffic monitoring and public transport provision.
On international level, governments want to control data flows to limit the negative consequences of data, e.g. preventing the misuse of personal or classified data. Another reason is to easier carry out their task as supervisor, e.g. demanding local storage of tax or gambling data to simplify control routines.
Having discussed this, the most critical challenges for the future identified in this report are:

  • Lack of data professionals, in particular within governments;
  • Government compartmentalization, limiting optimal data usage by governments;
  • An insufficient framework that satisfies both the user demand for privacy and the usage of personal information for business innovation. This is particularly relevant for public-private data sharing schemes;
  • Too little awareness on the capacity of big data, as ‘bad’ big data analysis happens quickly, i.e. too little knowledge on what transport-related questions it can and cannot address, and how big data should address these questions.

This document is valuable in the objective definition stage of big data applications in transport. It facilitates the discussion on if big data should be used by providing insight into how that may be. Economic actors are provided with an overview of existing applications of big data in the transport sector. Political actors are given insight into how governments are currently using big data.

Deliverable 5.3 Creating Shared Value for the European Transport Sector (Version 1)

The overall objective of the LeMO dissemination strategy is to stimulate ongoing interest from all stakeholders and interested parties and engage them in a dialogue throughout the course of the project. The success of LeMO will require active involvement of industrial parties, government, policy makers and standards organizations in order to ensure a good understanding and sense of ownership of LeMO’s big data recommendations and research & policy roadmap. In this report, we present LeMO’s strategy to build a LeMO community that will contribute to maximum exploitation of the results of the project in and for the European transport sector. The strategy is supported by a methodology to map the taxonomy of stakeholders in the LeMO community that enables to assess the strengths & weaknesses of creating shared value through the interactions of stakeholders in the LeMO community.

The LeMO community is created around four smaller communities:

  1. Members of the Advisory & Reference Group (ARG)
  2. Case studies
  3. Clustering with other projects
  4. The network of each of the partners

The following conclusions are made with respect of creating shared value through the interactions of stakeholders in the LeMO community:

  • There are ample opportunities of creating shared value through the LeMO community, especially in Western and Northern Europe.
  • Members in the ARG are representing in almost equal numbers both public and private organisations. In addition, organisations cooperating with LeMO in the case studies are mostly information- or data service providers, and are therefore having a role as data facilitators. The expectation is that during the research for these case studies more stakeholders with relevance to LeMO will be identified.

Deliverable 3.1 Case Study Methodology

This deliverable presents a methodology for conducting case studies in transport big data and a structured approach for how the case studies will elucidate constructive information and recommendations on the prerequisites of successful big data implementation in the transport sector from a socio-economic perspective. 

These case studies will involve organisations actively using big data for specific purposes and enable LeMO to understand strategies, actions and changes in behaviour associated with big data and identify their resultant merits and demerits. The LeMO case studies will produce evidence-based, clear and precise questions based on rigorous knowledge that illuminate opportunities, problems and viable solutions to be further investigated in the LeMO roadmap.

This deliverable will be a manual for the project partners during the selection and carrying out of case studies in LeMO project.