This document discusses potential harms from data use including privacy damage from restricting self-expression, discrimination damage from unfair treatment of groups, dehumanization damage from treating people as data points, and manipulation damage from deceiving people with data. It also lists example categories for data profiling and describes an "unintentional evil scale" ranging from clueless to ambitious to characterize behaviors. Finally, it notes concerns about overconfidence in algorithms, methodology, claims of neutrality, and origins related to big data use.

1. DATA SUPERPOWERS

3. PRIVACY DAMAGE
To disallow people to express themselves selectively
DISCRIMINATION DAMAGE
The unfair treatment of a category
DEHUMANIZATION DAMAGE
To treat people as data points
MANIPULATION DAMAGE
To deceive with data
Ethnic Second-City Strugglers
Biker Enthusiasts
Retirement on Empty: Singles
Rural and Barely Making It
State Trooper
Diabetes Interest
HIV Positive
Credit Crunched: City Families
Tough Start: Young Single Parent

6. UNINTENTIONAL EVIL SCALE
Clueless
Clumsy
Overconfident
Mischievous
Ambitious

7. OVERCONFIDENCE: Algorithms
https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de

8. OVERCONFIDENCE: Methodology

9. OVERCONFIDENCE: Neutrality

10. OVERCONFIDENCE: Origins

12. DATA SUPERPOWERS