2018 EDITION

From data protection and privacy to fairness and trust: the way forward (Workshop 1.4)

November 14, 2018 - 10.30 - 12:30

Room 62.01.048

SESSION DESCRIPTION & OBJECTIVES

Data-driven innovation is deeply transforming society and the economy. Although there are many potential economic and social benefits, big data analytics can also have negative implications. Such implications are not limited to privacy and data protection issues but also include, for instance, discrimination or impairments regarding human dignity, justice, fairness and trust. Given the new EU GDPR framework impetus for privacy-preserving big data technologies is on the rise, yet their implications on such values as, for instance, fairness and trust need further assessments. In this session, we will focus on how the GDPR’s transparency, information and control requirements could help to create an environment in which citizens feel that they can trust organizations collecting and processing their personal data. The session will encourage discussion on realization approaches not only for auditable GDPR compliance, but also for an ethical attitude towards end users of ICT services, aiming at reconciling the protection of citizen’s fundamental rights to privacy and data protection with usability and economic benefits. The workshop will involve relevant players who will share their success stories and concerns related to privacy and other ethical and societal issues as well as their opinions on the necessary next steps regarding responsibility in this field. The goal is to share insights on successes and failures and move forward towards a shared view of human-centered big data technologies.

We will explore questions including the following:

How can we move from technology as the problem (violating privacy) to technology as the solution?
What are the best practices for the design and application of big data solutions that do not violate societal values such as trust and fairness during data processing and analytics?
What are the main hurdles in implementing privacy-preserving technologies beyond the research domain, and how can we overcome these hurdles?
How can we find an adequate balance between the protection of privacy and the utility of data?
Is there an economic logic and business model for why it makes sense to apply privacy-preserving technologies?

AGENDA

10.30 Welcome and introduction