Data Processing Obstacles

Question 1

What is Math snobbery?

Answer 1

The belief that certain areas of mathematics are more important or valuable than others, leading to elitism and exclusion of those who work in “less prestigious” areas.

Question 2

Critical questions for big data

Answer 2

Data Minimization - bigger data is not always better data.

Context - Big Data loses its meaning, assumptions and context affect interpretations / decisions

Ethics - Just because it is accessible does not make it ethical.

Legal - consent of the experimental subjects

Question 3

Why is opacity a problem?

Answer 3

Faulty data, invalid assumptions and defect models cannot be corrected when they are hidden

Blackbox services are often wondrous to be hold, but our blackbox society has become dangerously unstable, unfair and unproductive

Question 4

How is opacity maintained?

Answer 4

1. real secrecy, for example, lack of disclosure
2. Legal secrecy
3. Obfuscation

Question 5

Give an example of effective versus failed disclosure

Answer 5

There’s a spectrum of disclosure between preserving secrecy and providing transparency

Depth, scope, and timing of disclosure can vary depending on whether we want to keep secrecy or not

For example, in-depth, preserving secrecy would mean, shallow and cursory disclosure

Another example for scope, providing transparency would mean disclosure provided to the public generally

For timing the example would be that it could be immediate disclosure if you want to provide transparency