Transcript The Fate of the Library in the Age of Big Data

Jeroen van den Hoven
Delft University of Technology
SoBigData Ethics
Unpacking Privacy
Designing for Responsibility
Central ‘SoBigEthical’ Questions
EU has set higher standards for ethics of
research: both in terms of methods, and
practices, consequences, but also aims
What good is SoBigData Research
bringing?
Is it producing good outcomes, without
producing bad outcomes?
Responsible Data Science
Responsible Research
Infrastructure
Dutch Consortium (25 meuro
application pending)
Fairness
Accuracy
Confidentiality
Transparency
Humanitarian
Two vantage points
Responsible
Research and
Innovation
(RRI)
Value Sensitive
Design
RRI: Basic Idea
Applied to Research infrastructure
Responsible Research and Innovation
 A research infrastructure can be said to be “responsible” only if it
accomodates data users, controlers and processors involved in bringing
about epistemic outcomes as responsible agents, i.e. they must have
been enabled
 (A) to obtain – as much was possible – the relevant information on (i) the
consequences of their actions and on (ii) the range of options/alternatives
(e.g. data minimization) open to them and
 (B) to evaluate outcomes and options effectively in terms of relevant
moral values (including, but not limited to wellbeing, justice, equality,
privacy, openness, autonomy, safety, security, accountability, and
efficiency).
 (C) to use these considerations (under A and B) as requirements for
design and development of new functionality, products and services
leading to moral improvement
Research Infra: socio-tech systems
Actor 4
Actor 1
Actor 2
Actor 3 Social Network
Actor 6
Component
1
Human 1
Human 2
Human 3
Component
2
Component
3
Component
4
Sociotechnical Network
SociotechSoc
Technical
Network
Component
5
Value Sensitive Design
Values Built into Systems
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Interfaces
Infrastructures
Algorithms
Ontologies
Code
Protocols
Integrity constraints
Architectures
Governance arrangements
 Identity Management
Systems
 Authorization Matrix
 Procedures
 Regulations
 Incentive structures
 Auction mechanisms
 Voting mechanism
 Monitoring and inspection
Data sets
Responsibility
Privacy
Accountability
Agency
Autonomy
Security
transparency
Models
Algorithms
Express
Implement
Procedures
Protocols
Encryption
Values
Norms
Laws
Ideals
Ethics
Principles
Justify
Audit
Artefacts
Architectures
Materials
Standards
Security
Systems
Infrastructure
Key Problem
21st Century: Value Sensitive Design
Values hierarchy
Values
Norms
Policies
Mechanisms
Protocols
Design requirements
High Level suprafunctional
requirementsl
Example of values
hierarchy
Values
privacy
Norms
Design requirements
Coarse
graining
Risk
mitigation
Accountability
Data
clustering
Pseudony
mization
Data quality
Security
Values
 1. Privacy = data protection
for moral reasons
 1.1. prevention of Harm
 1.2. prevention of
Exploitation, manipulation,
economic disadvantage
 1.3. prevention of
Discrimination
 1.4 Respect for persons
and human dignity, moral
autonomy
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2. Fairness
2.1 Equal Access/openness
2.2 positionality
3. Reliability
3.1. accuracy
3.2. relevance
3.3 veracity
3.4 soundness
4. Responsibility
4.1 transparency
4.2 accountability
4.3 liability
4.4. Agency and control
4.4.1 perspicuous representation of
values and choices in Research
Infrastructure
Design for X
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Design for privacy
Design for security
Design for inclusion
Design for sustainability
Design for democracy
Design for safety
Design for transparency
Design for accountability
Design for responsibility
Design for Responsibility
Design for Responsibility
 X Holding Y Responsible for Z
 X Making Y Responsible for Z
 X Taking Responsibility for Y
 X Feeling Responsible for Y
Decide about criteria for
Responsibility Attribution
Knowledge
Intention
Control
Non coercion
Capacity
Responsibility Apportioning
- Fairness
- completeness
Determine type of responsibility
1. Causality
1. Task Responsibility
2. Blame
2. Negative Task responsibility
3. Accountability
4. Liability
3. Supervising responsibility
4. Self Monitoring Responsibility
5. Role/task
5. Meta- task Responsibility
Task responsibility
1. Task Responsibility
2. Negative Task responsibility
3. Supervising responsibility
4. Self Monitoring Responsibility
5. Meta- task Responsibilities
(a)
(b)
(c)
Obligation to check whether others (or future selves) can see
to it that…
Obligation to see to it that others (or future selves) can see to
it that …
Obligation to prevent moral dilemma’s from arising
Roles, Responsibilities
Legal roles:
 Data controller
 Data processor
 Governance roles (Ethics Board, SoBigData Governance)
 SoBigData Researchers (
 Final Users, End Users
 Software provider
 Data set provider
 Research infrastructure engineers
 Executor
 Secondary Users
 Decentral governance bodies (ERB participating universities)
Teams
Unpacking Privacy
Privacy: Data protection for moral
reasons
Protecting X
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Constrain
generating
Acquiring
accessing
processing
Disseminating
Personal Data
Data Protection
1. Preventing harm
2. Prevention of manipulation and
exploitation, e.g. fairness in markets
for personal data
3. Prevention of Discrimination and
Contextual Integrity
4. Respect moral autonomy
Privacy: Data Protection for Moral Reasons
Design for Privacy
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1. Informed consent
2. Right to be forgotten
3. Identity Management
4. Reciprocal Privacy
5. Coarse graining, anonymization
EX ANTE
 5. Sous-veillance, counter veillance
EX POST
 6. Violation/intrusion detection
 7. Big data applications to detect Big data violations of privacy
Moral Overload
Responsibility, accountability, privacy, security………
Value Pluralism
 Privacy
 Autonomy
 Equity
CONFLICT
 Justice
DILEMMA
 Dignity
 Wellbeing and Happiness
 Safety
 Security
 Sustainability
 Health
 Friendship
 Solidarity
 Dependability
 Usability
 Resilience
 Reliability
 Efficiency
 Flexibility
Moral Overload
 Prosperity AND sustainability
 Security AND Privacy
 Efficiency AND Safety
 Accountability AND Confidentiality
Privacy
Security
Security
Moral Overload
Privacy
Security
Security
No Privacy, no Security (1.0)
Privacy
Security
Privacy or Security (2.0)
Privacy
Security
Privacy & Security (3.0)
Moral axiom
If you can change the world
by innovation today so that you can
satisfy more of your obligations
tomorrow, you have a moral obligation
to innovate today.
SobigData Ethics
Where can we improve upon older
ethical frameworks?
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Design Stance
Better recognition of the difficulties of informed consent;
Definition of Personal data
Specification of “use limitation” and “purpose specification”
Recognizing the potential expansion of identifiability;
Anonymization techniques and implementation;
Algorithmic ethics/ fairness/transparency/accountability and
preventation of discrimination;
 Perspicuoius representation of data, models, algorithms
 Making sure we do not forget we are dealing with people and
not numbers.
How do we make this concrete?
It is only one thing to say that we should safeguard all these
principles. It is quite another thing to make sure that these
principles are actually embedded into design of the research
iunfrastructure. Ethical considerations need to be introduced into:
• Work flows
• A knowledge base of best practices
• Overarching responsibility architecture
This cannot be done by ethicists, but will require a shared
commitment on the part of everyone involved in SoBigData!
Ethics Board
 Helen Nissenbaum, NYU
 Nikolaus Forgó, Hannover
 Jeroen van den Hoven, TU Delft
 Dag Elgesem, Bergen, Norway
 Jeroen Terstegge, DPA Netherlands, Phillips Privacy Officer