ETHICAL ISSUES IN THE BIG DATA INDUSTRY –

IMPLICATIONS FOR INSURANCE

Kirsten Martin, PhD

George Washington University

School of Business

U.S. Department of Treasury

Federal Insurance Office’s (FIO) 

Federal Advisory Committee on Insurance (FACI)

August 17, 2017
Three Areas of Concern – Big Data

1. Source of Big Data

2. Analysis of Big Data

3. Marketing Use of Big Data
Value Chains in Business Kirsten Martin 3

Raw 

Materials Suppliers Manufacturing Shipping/Distribution Retail Customer

Sourcing Issues

1. Treatment of Individuals

2. Environmental Damage

3. Poor Quality

Use Issues

1. Distribution

2. Harm to Consumer

3. Novel 2nd Use

Manufacturing Issues

1. Treatment of Individuals

2. Pollution

3. Poor Quality
1. SOURCE OF BIG DATA
Information Supply Chains Online Kirsten Martin 5

Individuals

Living

Tracking

Companies

Data Aggregators

(Distributors) Customer Insurance 

Companies

But ....

0. Data is public information without any expectation of privacy.

1. Someone else did it. I didn’t actually do anything wrong (e.g., breach 

confidentiality/laws/privacy expectations), so why should I be held accountable? 

(Nike, Wal-Mart, Apple, Kathy Lee Gifford Argument).

2. Everyone else is doing it (Connor Argument; Lance Armstrong c. 2013 Argument)
Difference in Trust

65% of respondents have 

complete or moderate trust in 

insurance companies (EY 2014)

7% of respondents have confidence that 

data aggregators protect their data; 50% 

believe they should not have any data 

(Pew 2014)
2. ANALYSIS OF BIG DATA
Two problems

Policy as Hidden Policy as Quickly 

Replicated
Algorithms as producing an answer

Algorithm

Weights + factors to take into 

consideration

Source Data

Data set including people or 

information on which to make a 

decision

Training Data

Information used to ‘train’ 

an algorithm if using 

machine learning. 

Outcome

Answer or 

decision. 

E.g., Risk 

assessment

Current Approach to 

Algorithmic Decision Making
Algorithms as producing an answer

Algorithm

Rules, policy, principles, ethical 

norms, laws suggesting the relative 

importance of factors to a decision

Source Data

Best estimates of the factors 

available about individuals to 

possibly be used in the decision

Training Data

History of the contextual 

decision as told by individuals 

who tracked and recorded the 

decision. 

Outcome

Best 

approximation 

of intended 

output. 

E.g., Risk 

assessment

Acknowledge Unjust 

Biases Throughout
Algorithms as producing an answer

Algorithm

Rules, policy, principles, ethical 

norms, laws suggesting the relative 

importance of factors to a decision

Source Data

Best estimates of the factors 

available about individuals to 

possibly be used in the decision

Training Data

History of the contextual 

decision as told by individuals 

who tracked and recorded the 

decision. 

Outcome

Best 

approximation 

of intended 

output. 

E.g., Risk 

assessment

Acknowledge Missing 

Questions

What are the 

appropriate 

rules/policies to 

apply in this 

context?

What are the 

ethical norms?

What historical 

reference points 

are appropriate 

& fair for this 

decision?

How is 

effective 

defined for 

this decision?

What factors are 

appropriate & 

fair for this 

context?

Is the outcome 

biased 

unjustly?

What unjust 

biases exist in 

the construction 

of the historical 

data?

What level of 

‘accuracy’ is fair 

for this decision?
Two problems

Policy as Hidden Policy as Quickly Replicated

Less Oversight Greater Impact
3. MARKETING USE OF BIG DATA 

Product, promotion, pricing 
Use of Data

“Accuracy” v. “Good Decision”

 New Information

+ ‘True’ Information

+ More Information

+ Deeper Information

 Biased Information

Pivotal Decisions – allocation of social goods

• Who deserves the insurance

• Who should have access to financial 

protections

• Whose claim should be questioned? 

• Who should take on greater costs for 

the same service 

Digital marketing manipulation (Calo)

“An insurer might target a chain-smoking 

motorcycle buff with an action-packed video 

game designed to help him quit — while 

appealing to his profile as an adrenaline 

junkie.”

https://www.statnews.com/2015/12/15/insurance-big-data/

E.g., False Claim Detection:

http://www.tellius.com/machine-learning-transforming-insurance-industry/
Example

E.g., False Claim Detection:

“With a person-centric [versus claim-centric] approach, the beneficiary’s claim 

history and behavior across multiple sources (such as using a person’s social graph 

to find similar behavior patterns among individuals that he or she is connected to, 

and similar claims that were reported by the same person) are analyzed.”

http://www.tellius.com/machine-learning-transforming-insurance-industry/

“Accuracy” v. “Good Decision”

• Are contacts an appropriate 

factor in determining claims?

• Is the claim decision 

reviewable? Justified?

Pivotal Decisions – allocation of social goods

• Are there biases in the data such that 

some demographics have more 

accurate estimates? 

• Are some groups afforded better 

terms in claims adjustments based on 

this data?

Digital marketing manipulation (Calo)

• Are individuals particularly vulnerable 

during this negotiation or promotion?

• Does the data we use give us an unfair 

advantage? 
IN SUM....
Three Areas of Concern – Big Data

1. Source of Big Data

• Difference in trust between supplier of data and insurance company

• Difference in the importance of trust in their businesses

2. Analysis of Big Data

• Policy as hidden

• Policy as quickly replicated

3. Marketing Use of Big Data

• Pivotal decisions

• Good versus accurate decision

• Consumer manipulation