ETHICAL PRINCIPLES AND DATA SCIENCE –

REPURPOSING ADMINISTRATIVE & OPPORTUNITY DATA

FCSM Fall Conference 

21 September 2020

Stephanie Shipp

Sallie Keller

Aaron Schroeder

SOCIAL AND DECISION ANALYTICS DIVISION

BIOCOMPLEXITY INSTITUTE
Repurposing all data sources

Local, State, and Federal

Opportunity

Data

Procedural

Data

Designed 

Data

Opportunity 

Data

Procedural 

Data

Administrative 

Data

Keller, S.A., Lancaster, V., Shipp, S. (2017). Building Capacity for Data Driven Governance - Creating a New Foundation for Democracy, Statistics and Public 

Policy Taylor & Francis Publishers. http://amstat.tandfonline.com/doi/full/10.1080/2330443X.2017.1374897#.WbcHY9OGOqA 2
What are our 

obligations to use 

these data ethically?

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Responsible analyses

A cautionary note

April 21, 2016

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Source: https://www.sciencemag.org/news/2019/10/hospital-risk-scores-prioritizewhite-patients?utm_campaign=news_daily_2019-10-

24&et_rid=475169293&et_cid=3044305

Hospital risk scores prioritize white patients

When health risk prediction 

algorithms focus on cost rather than 

illness, racial bias can creep in

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Data Science – Repurposing Data

Panoptican Prison Metaphor

• Today, we can observe behavior based 

on repurposing existing data without 

consent or awareness by those 

providing the data

• Requires a broader view of ethics that 

adapts to the spirit as well as the rules

Source: The Works of Jeremy Benthem, 1791, described 

in Salganik, Bit by Bit (2018)

Source: Keller, S. A., Shipp, S., & Schroeder, A. (2016). 

Does big data change the privacy landscape? a review of the

issues. Annual Review of Statistics and Its Application, 3.

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The Belmont Report

The Belmont Principles provide the foundation for the 

Institutional Review Board (IRB) process and for a 

principles-based approach to ethics.

• Respect for persons – honoring individual wishes

• Beneficence – weighing risks and benefits of study

• Justice – fair distribution of risks and benefits

Led to the Common Rule which governs U.S. 

Government research

1979

Focus is on 

“research involving human subjects”

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https://www.nature.com/articles/d41586-019-01720-7

The Menlo Report 

Department of Homeland Security commissioned a 

report to introduce Belmont principles in ICT 

(Information, Communication, Technology)

The Menlo Report extends the Belmont Principles 

to include Respect for Law and Public Interest

Focus is on 

“research with human-harming potential”

Important in the digital age where the use of 

digital technologies and repurposing of 

data can expose people to risks

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How does this apply 

to our research using

all data?

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Data pipeline starts with data discovery

Keller S, Shipp S, Korkmaz G, et al. Harnessing the power of data to support community-based 

research. WIREs Comput Stat. 2018;e1426. https://doi.org/10.1002/wics.1426 11 11
Example: Data Discovery around Local Housing

Commerical Data 

Black Knight Financial Services

MPF Research (RealPage)

National Association of REALTORS

Real Capital Analytics

Zillow, Redfin

Mortgage Bankers Association

CoreLogic

MLS Data

MRIS 

RealtyTrac

WegoWise

Equifax Credit Scores

TransUnion Credit Data

Experian

Foot Traffic - SentriLock

Axiometrics, Inc

Planet Labs

Blackbridge

CoStar

Local - data sharing agreements

Community Planning & Housing Development

Permitting

Real Estate Assessments

Economic Development

GIS or Mapping Center

Crime data

Fire and EMS

Building Energy Report Cards

Bicycle & Pedestrian Counters

Resident Poll Results

Alerts

State – data sharing agreements

Housing Virginia

Northern Virginia Association of Realtors

VHDA Housing Analysis

Virginia Housing Coalition

Other – mixed access

National Change Database (NCDB)

Community Commons Maps

Crime Reports

IPUMS-USA

National Council on Real Estate Investment 

and Fiduciaries

Location Inc (Neighborhoodscout)

USDA Forest

Maponics

Center for Regional Analysis

Urban Tree Canopy

Yelp

Walk Score

RS Metrics

AirBnB

TripAdvisor

InfoUSA Mailing List

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Data Source Geography

American Community Survey data (Census), 2012-2016 Block Groups

American Time Use Survey (BLS), 2017 National

Youth Risk Behavior Surveillance System, 2015 State

County Health Rankings, 2017 County

Built Environment, e.g., Grocery stores, SNAP retailers, 

recreation centers, community gardens

Address Level

Fairfax real estate tax assessment data (CoreLogic) Address Level

Fairfax Open data: Zoning, Environment, water, Parks, Roads Shapefiles

Fairfax County Youth Survey, 2016

8th, 10th, 12th graders

High School 

Attendance Area

Virginia Department of Education, 2017 High School

National Center for Education Statistics, 2014-2015 High School

Center for Disease Control, 2014-2015 High School

Regional electronic health records (in-process) Individual

Example from 

project in Fairfax, 

Virginia 

• Teen obesity and 

physical activity

• Data can cross 

HIPPA, FERPA, 

Commercial, 

open

Screening & Acquisition

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Definitions

Privacy refers to the amount of 

personal information individuals 

allow others to access about themselves

Confidentiality is the process that data producers and 

researchers follow to keep individuals’ data private

Security applies to data storage and transport

Privatization of Data is the collection, aggregation, and 

(re)processing of personal data to sell to consumers

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What about informed consent?

“Access to data held by the 

government should occur only 

in service to the public 

interest.”

Murray/Ryan Commission on 

evidence-based policy making, 2017

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IRB: Waivers of the elements of consent 

Federal regulations allow IRBs to authorize 

researchers to modify the consent process ... only if 

these criteria are met.

• The research involves no more than minimal risk to the 

subjects. 

• The research could not practicably be carried out without 

the requested waiver or alteration.

• The waiver or alteration will not adversely affect the rights 

and welfare of the subjects. 

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Local government data sharing agreements

Typical language in ”local” data sharing 

agreements

• WHEREAS the County and ENTITY NAME 

seeks to improve the County and its 

citizens’ quality of life and services while 

accelerating the County’s efficiency and 

resiliency through the use of County data

and combining County community planning 

and management skills with ENTITY 

NAME’s analytical and data science 

expertise

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Ethics Checklist for Data 

Science Lifecycle

Project Initiation and Problem 

Identification

Data Discovery, Inventory, 

Screening, and Acquisition

Data Ingestion and Governance

Data Wrangling

Fitness-for-Use Assessment

Statistical Modeling and 

Analysis

After-Project Debriefing

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Keller, S, Shipp, S., Schroeder, A., Korkmaz, G., (2020). Doing Data Science: A Framework and Case 

Study. Harvard Data Science Review. https://hdsr.mitpress.mit.edu/pub/hnptx6lq

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Understanding Bias

Data Science for Public Good project 

Asked students:

What is life like in rural America?

What is the best thing about living in a 

rural community?

What is the worst thing about living in 

rural America?

What is the most pressing problem 

facing rural communities today?

How can a data scientist help rural 

communities?

Source: Shawn Dorius, Associate Professor, Social Demography, Iowa State University, 

Data Science for Public Good, Understanding Rural Bias, Summer 2020 

What is life like in rural America?

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Practical Solutions – Introduce Friction

Social networking company Nextdoor saw 

that too many “suspicious character” 

postings on its online bulletin boards were 

based solely on race,

Eberhardt. Stanford psychologist,

suggested they create a checklist so 

people had to specify suspicious behavior 

before describing appearance

Friction - people have to evaluate their 

reasoning before making bias-based 

assumptions

Incidence of racial profiling fell by 75%

Use of these data in research before 

change would perpetuate bias

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ü Adapt criteria to ensure implementation of ethical 

principles

ü Make ethical considerations and discussion of implicit 

biases an active and continuous part of the project

ü Seek expert help when ethical questions at any stage 

cannot be answered

ü Incorporate ethical guidelines from relevant professional 

societies (e.g., American Statistical Association, American 

Physical Society)

ü Introduce friction by “interrogating ourselves and being 

aware when we’re beginning to make stereotypic 

associations.” (Eberhardt 2020)

ü A principles-based approach allows researchers to make 

decisions and communicate their decision process for cases 

where ethical rules do not yet exist (Salganik 2018)

Incorporating ethics into Data Science Lifecycle

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