Quiz 2 - Big Data

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Quiz 2 - Big Data
2015-04-29 12:41:45

big data quiz2
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  1. What is a prediction?
    to “state or estimate . . . that an action or event will happen in the future or will be a consequence of something.”
  2. Types of PREDICTIONS
    • - consequential predictions
    • - preemptive predictions
    • - preferntial predictions
  3. consequential predictions
    • Predictions that attempt to anticipate the likely consequences of a
    • person’s action
  4. example of consequential predictions
    When a lawyer predicts “what the courts will do in fact,” she anticipates the legal consequences of future courses of conduct in order to advise clients whether it is feasible to avoid the risk of state sanction.
  5. Preemptive predictions
    ● Intentionally used to diminish a person’s range of future options.

    ● preemptive predictions do not usually adopt the perspective of the actor. Preemptive predictions are mostly made from the standpoint of the state,a corporation, or anyone who wishes to prevent or forestall certain types of action
  6. example of preemptive predictions
    Governments, corporations, and individuals will use big data to preempt or forestall activities perceived to generate social risk. Some loan companies, for example, are beginning to use algorithms to determine interest rates for clients with little to no credit history, and to decide who is at high risk for default.
  7. Preferential predictions
    stroke your preferences in order to sell goods and services
  8. example of preferential predictions
    Google’s bid to create personalized search engines is a prime example of society’s increasing reliance
  9. Preemption
    - It's about big data’s power to enable a dangerous new philosophy of preemption

    - Prediction industries flourish in a society where anyone and anything can be perceived as a potential threat bc it's lucrative to exploit risk that can later be avoided.  In such cases, prediction often precipitates the attempt to preempt risk

    - Preemption strategy comes at a significant social cost
  10. example of preemption
    predictive algorithms to generate no fly lists that employs predictive algorithms preempts the need for any evidence or constitutional safeguards. 

    Prediction simply replaces the need for proof.
  11. presumption
    Everyone is presumed innocent until proven guilty.

    -If legal universe has a prime directive, it is probably the shared understanding that everyone is presumed innocent until proven guilty.

    -In legal discourse, the presumption of innocence is usually construed, narrowly, as a procedural safeguard enshrined within a bundle of “due process” rights in criminal and constitutional law.
  12. presumption (cont'd)
    • Taken together, privacy and due process values seek to limit what the
    • govt (and, to some extent, private sector) is permitted to presume about individuals absent evidence that is tested in the individual's’ presence, with their participation.

    As such, these values aim to provide fair and equal treatment to all by setting boundaries around the kinds of assumptions that can and cannot be made about people
  13. 6 provocations for Big Data
    • - Automating Research Changes the Definition of Knowledge.
    • - Claims to Objectivity and Accuracy are Misleading
    • -Bigger Data are Not Always Better Data
    • -Not All Data Are Equivalent
    • - Just Because it is Accessible Doesn’t Make it Ethical
    • -Limited Access to Big Data Creates New Digital Divides
  14. Rationale in Support of Full or Partial Autonomy
    • Facilitate flow of information on public issues
    • Obtain personal info for research
    • Write under a pseudonym
    • Encourage reporting of bad behavior
    • Amnesty programs
    • Protect donors
    • Protect economic interests
    • Protect from unwanted intrusions
    • Review applications based on qualifications only
    • Protect reputation and assets
    • Avoid persecution
    • Encourage experimentation and risk taking
    • Protect personhood (“none of your business”)
    • Traditional expectations (caller ID)
  15. Rationale for Identifiability
    • Aid in accountability
    • Judge reputation
    • Pay dues or receive just deserts
    • Aid efficiency and improve service
    • Determine bureaucratic eligibility
    • Aid research
    • Protect health and consumers
    • Aid in relationship building
    • Aid in social orientation
  16. European privacy approaches
    • Comprehensive Laws
    • -European general laws that govern the collection, use and dissemination of
    • personal information by public and private sectors.

    -Right to be forgotten law.
  17. What are European Comprehensive Laws?
    European general laws that govern the collection, use and dissemination of personal information by public and private sectors.
  18. U.S. privacy laws
    • Sectoral Laws
    • Idea is to avoid general laws and, instead, focus on specific sectors

    Advantage: enforcement is achieved  through a range of mechanisms

    Disadvantage: new legislation has to be introduced with each new technology.

    Patriot Act allows the FBI to obtain court orders through the Foreign Intelligence Surveillance Court to confiscate or collect data that assists in an investigation ostensibly undertaken to protect against terrorism.

    • -De-anonymization/Re-identification?
    • Public and/or private data are cross-referenced in an effort to identify a
    • specific individual
    • -Components of Just War Theory and their
    • meanings
  19. What are U.S. Sectoral Laws?
    Idea is to avoid general laws and, instead, focus on specific sectors
  20. Advantage of Sectoral Laws
    enforcement is achieved  through a range of mechanisms
  21. Disadvantage of Sectoral Laws?
    new legislation has to be introduced with each new technology
    Sectoral Laws          European approaches
    Comprehensive Laws        U.S. approaches
    • Sectoral Laws --> U.S.
    • Comprehensive Laws --> European
  23. Jus Ad Bellum
    • 1. Just Cause
    • 2. Comparative justice
    • 3. Competent authority
    • 4. Right intention
    • 5. Probability of success
    • 6. Last Resort
    • 7. Proportionality
  24. Jus In Bello
    • 1.  Distinction
    • 2.  Proportionality
    • 3.  Military necessity
    • 4.  Fair treatment of prisoners of war
    • 5. No means malum in se