Author: Hans Lammerant (VUB) Presented at: Workshop on Exploring the Positive and Negative Impacts of Big Data Across Sectors

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BYTE:
Horizontal analysis of societal externalities
Hans Lammerant - VUB
Big data roadmap and cross-disciplinary community for
addressing societal externalities

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Externalities
Definition
• Positive externalities occur when a product, activity or decision by an actor causes positive
effects or benefits realized by a third party resulting from a transaction in which they had no
direct involvement.
• Negative externalities occur when a product, activity or decision by an actor causes costs (or
harm) that is not entirely born by that actor but that affects a third party.
Problems
• Economical concept -> application in non-economical areas?
• Boundary internal - external

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Big Data Assessment
Case study Raw dataset or typical volume used
– order of magnitude
Total volume of data holdings –
order of magnitude
Environment PB not available
Energy TB PB
Crisis
informatics
GB (daily volume) not available
Smart city GB not available
Healthcare GB TB
Culture small GB
Maritime KB-GB not available
Table 1: Volume of data – order of magnitude

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Big Data Assessment
Case study Velocity of data acquisition
Environment high
Energy high
Crisis informatics high
Smart city high
Healthcare low
Culture low
Maritime low

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Big Data Assessment
Variety
- Challenge present in all case studies
Veracity
- Challenge present in several, but not all, case studies
- Term covers several issues: correctness, resolution or granularity, uncertainty about
meaningfullness
Conclusion
- Big Data as umbrella term captures variety of situations

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Technical challenges
Data Acquisition:
- switch to sensor-driven data acquisition
- access to other data sources
Data Analysis: only occasionally mentioned as a challenge
Data
Acquisition
Data
Analysis
Data
Curation
Data
Storage
Data
Usage

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Technical challenges
Data Curation
• Interoperability
• Veracity
• Privacy, data protection, data security
Data Storage: only occasionally mentioned as a challenge
Data
Analysis
Data
Acquisition
Data
Curation
Data
Storage
Data
Usage

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Technical challenges
Data Usage
• mindset, organisational cultures -> Adaptation challenges
Conclusion:
• Challenges mostly in data collection, data curation and data usage phase
• Often technical challenges are translation of societal externalities
Data
Analysis
Data
Acquisition
Data
Curation
Data
Storage
Data
Usage

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Economic Externalities
Economic externalities +/- Crisis
Informatics
Culture Energy Environment Healthcare Smart city
Improved efficiency + + + + +
Innovation + + + + + + +
Changing business models +/- - +/- +/- +/- +/-
Employment +/- + + +/-
Dependency on public
funding
- - - - - -

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Social and Ethical Externalities
Social and ethical
externalities
+/- Crisis
Informatics
Culture Energy Environment Healthcare Smart city
Improved efficiency and
innovation
+ +/- + + +/- +/- +
Improved awareness and
decision-making
+ + + + +
Participation + + + + +
Equality - - - -
Discrimination - - - -
Trust - –/+ - - –/+ –/+

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Legal Externalities
Legal externalities +/- Crisis
Informatics
Culture Energy Environment Healthcare Smart city
Privacy - - - - - -
IPR - - - - -
Liability,
accountability
- - - - - -

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Political Externalities
Political externalities +/- Crisis
Informatics
Culture Energy Environment Healthcare Smart city
private vs. public and
non-profit sector
- - - - -
losing control to actors
abroad
- - - - -
improved decision-
making and participation
+ + + +
political abuse &
surveillance
- - -

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Conclusions
Economic externalities
• Positive impact: improvement efficiency and innovation
• Changing and new business models -> possibility of dependence on dominant players
• Public funding proves to be important in kick-starting a data economy
Social and ethical externalities
• Improved efficiency and innovation for social, non-profit objectives
• Risk for negative impact on privacy and equality and for new discriminatory practices
• Problems with trust

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Conclusions
Legal externalities
• Data protection and intellectual property rights prove to be a barrier to big data
• Both serve to protect other societal values, but are considered outdated and too restrictive
• Also other legal frameworks need updating or clarification
Political externalities
• Relation between public and non-profit versus private sector
• Fear to lose control to actors abroad
• Perspectives on these issues varied a lot

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Big data and externalities
Causal explanation?
How does big data affects interactions between actors?
Big Data
Interactions
between actors
Externalities

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Categorisation of externalities
• Benefits: practices aiming at capturing and maximizing the benefits of big data
• Regulatory practices: practices aiming at maximizing an objective at a societal
level by balancing interests, but which are now negatively affected by big data.
They show up as negative externalities because their balancing of interests does
not deliver the same positive results any more.
• Protective practices: practices aimed at preserving other values or interests,
which now get negatively affected.

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Categorisation of externalities
Benefits Negative effects on regulatory
practices
Negative effects on protective
practices
Improved efficiency and
innovation
IPR Equality
Improved awareness and
decision-making
Losing control to actors abroad Anti-discrimination
Participation Private vs. public and non-
profit sector
Privacy
Improved political decision-
making and participation
Trust (includes fear of capture
and competition issues)
Liability, accountability
Political abuse & surveillance

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Effect of big data on interactions
• Larger amount of interactions between actors
• Higher visibility of actors
• Higher penetration of organisational boundaries → traditional gatekeeping gets
disrupted
• Data becomes network good → positive network effects
• Shift in transactions from exchange of goods to delivery of services → shift from
momentary transaction to regulating continuous data flows
• Changing role of internet: from market place where actors meet into digital
environment in which value creating production processes take place

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Effect on regulatory and protective
practices
• Current regulatory and protective practices reflect old transaction model
→ high transaction costs → become disfunctional and result in negative
externalities (e.g. rights clearance in copyright, consent in data protection)
• Enlarged visibility and penetration of boundaries: privacy problems for
individuals and for organisations
• Positive network effects: anonymization becomes unreliable, propagation of
discriminatory effects

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QUESTIONS
Any questions?