What is information - Computation and Governance-by-Data
1. II. Computation What is Information?
Wang, Keren. Ürümqi Grand Bazaar entrance, Xinjiang.
(Digital photography, 7/11/2018)
Teaching slides by Keren Wang kuw148@psu.edu (2022)
2. Gary Kovacs: Tracking Our Online
Trackers (TED, 2012)
Teaching slides by Keren Wang kuw148@psu.edu 2022
3. Governance
-by-Data
Elkin-Koren, Niva, and Michal S.
Gal. “The Chilling Effect of
Governance-by-Data on Data
Markets.”
Dual use of data market:
● Data driven innovation and targeted ads
● Data driven policy-making and targeted
enforcement
● Affecting methods of proof, legal norms,
rule application etc.
Ex. Driving pattern data collected by
automobiles used for customized marketing
AND law-enforcement risk level assessment
Teaching slides by Keren Wang kuw148@psu.edu 2022
4. Personalized
Law
Tailored laws that vary person by person
➔ Personally tailored speed limits based on
the driver’s “risk score”
➔ Tailored parking ticket fine based on
individual income level & parking record
➔ Using prediction tools to assist
sentencing and parole decisions
➔ Personalized allocation of gov.
assistance
➔ Provisioning medical care based on
personalized risk factors
Teaching slides by Keren Wang kuw148@psu.edu 2022
5. Personalized law and
Data Analytics
Data Value Chain: collection; synthesis &
analysis; use
● Data market dominated by private firms
● Same data pool allows for several uses
● Low cost and nonrivalrous nature of
private-public data sharing
More cost-effective for government to use
existing private data market
“Always on” society: citizens become data
subjects
Teaching slides by Keren Wang kuw148@psu.edu 2022
6. Elkin-Koren, Niva, and Michal S. Gal. “The Chilling Effect of Governance-by-Data on Data Markets.”
Teaching slides by Keren Wang kuw148@psu.edu 2022
8. Pros of Governance-by-Data or
Algocracy
● Adaptability: regularized customization
of policy-making
● Awards good personal behavior
● Cost-effectiveness
● Maximizing the potential of data market
● Optimizing staff effectiveness
● Reducing bureaucratic inertia,
corruption, political gridlock, and red
tapes
Teaching slides by Keren Wang kuw148@psu.edu 2022
10. What Could
Go Wrong?
Privacy paradox - gap b/w the perceived
value of personal privacy and the actual
willingness to share personal info online for
small rewards.
Transparency paradox - transparency
needed to ensure the government did not
manipulate its data and algorithms would
also make the data transparent to malicious
actors.
Teaching slides by Keren Wang kuw148@psu.edu 2022
11. What Could Go Wrong?
● Higher legal burdens in an “always on” society
● Inaccurate and discriminatory profiling
● Increase incentives for identity theft
● Loss of personal sphere and reasonable
expectation of privacy
● Loss of the “right of forgetting”
● Data distortion from surveillance-conscious
data subjects
● Manipulation and abuse by malicious actors
Teaching slides by Keren Wang kuw148@psu.edu 2022
