PRIVACY, BIG DATA AND GOVERNANCE MAY 2015

YOUR FACULTY

• Marty Abrams

• JoAnn Stonier

• Hilary Wandall

EVOLUTION OF AN INFORMATION ECONOMY IMPACTS COMPLIANCE AND GOVERNANCE

• Mainframe era – Data and systems were the same – early awareness

• Database technologies – Data could serve many systems, secondary

use became an issue – fair information practice principles based on

consent

• Emergence of predictive sciences – Broad data sets used to predict

future behavior – concerns about fairness and dignity

• Internet – Explosion of observation, often by many parties – new

ethics programs

• Internet of Things – Observation accelerates – growing tracking

discussions

• Big Data – All data available for predictions – governance becomes an

imperative

NATURE OF DATA RELATED TO INDIVIDUALS

• Origins of data have changed as technology has evolved

• Mainframe era data was collected directly from us

• Today fastest growing data sets come from observation and product of

predictive sciences

• To understand effective privacy one needs to understand the origins of

data

DATA TAXONOMY BASED ON ORIGIN

• Was developed for OECD March 2014 workshop

• Data may be segmented into four classifications based on

origin

• Provided – Comes directly from individual with their direct knowledge

• Observed – Individuals’ behavior is monitored on the Internet, public

places and sensors built into things like cars

• Derived – The product of grouping data and simple math

• Inferred – Categories that come from predicative sciences

POLICY IMPLICATIONS FROM TAXONOMY

• Observed and inferred data are fastest growing data sets

• Often without the attention of busy individuals

• Therefore consent, while very important, becomes less effective

as a governing mechanism

• Data originates in a manner invisible to individuals

• How does one fill the gap?

WHY IS THE BIG DATA DIFFERENT?

• Volume, velocity, diversity – blending data robustly

• More room for processing errors

• Diverse data sets stretch compatibility to stated purpose

• Goes beyond testing best intuition – data drives questions

• Business logic is a question

• Correlation not causation

• Is the correlation meaningful

SO WHY BIG DATA?

• Big data sees things that go beyond intuition, and that vision

improves outcomes

• Health

• Disaster relief

• Improved markets

• The improvements are compelling and inevitable

• Big data also may enhance discrimination and mistakes

• Governance must be in place to facilitate the compelling and

prevent inappropriate results

GOVERNANCE OBJECTIVES

• Determine when it is appropriate to repurpose data pertaining

to people

• Assure the processing is conducted in an accurate manner

• Guide the application of remarkable insights

IAF HAS BEEN DEVELOPING A GOVERNANCE PROCESS

• Based on key concepts

• Data protection assures the full range of fundamental rights, not just a

narrow definition of privacy

• Reticence risk is meaningful and real

• A fairness assessment is necessary

• This takes us beyond compliance to ethics

Part A

Part B

Part C

Part D

Big Data Project Structure

• Description of the unified ethical framework. • Creates a basis for the interrogation guidance.

• Interrogation guidance for implementing the code. • Illuminates the key issues that must be considered in

making a judgment on whether a Big Data project is fair, responsible and ethical

• Mechanisms for enforceability. • Ensures compliance with code.

• Contextual interrogation questionnaire. • Customized for organizations, industries, mediums.

FIND MARTIN ABRAMS

• mabrams@informationaccountability.org

• www.informationaccountability.org

• Key papers are on the website

• A number of those papers will soon be available in Japanese