Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016. This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S. The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich. Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.

2. Background
ž “Just Correlation” and predictive
analytics in the medical and other
contexts:
—The Age of Big Data
—Data Driven Processes and Results
—Putting the information to use
—Reliance on “mere” correlations

3. Roadmap
ž The rise of “Big Health Data”
ž What does mere reliance on correlation
mean (examples)
— Possible options, alternatives and outcomes
ž Pros and Cons of “Just Causation”
— Reliance on other disciplines.
ž Law and Policy implications and “hooks”

4. “Big Health Data”
ž Health and Medical data held by new
players, because of:
— Definition change
— New practices, sources and business models.
○ At times, these are startups.
ž Change reflected in some new legislation
[GDPR in the EU].
— Regulating health data calls for unique
balancing;;
○ Strong privacy preference vs. public benefits

5. Example (1): Credit Data
ž “all data is credit data, we just don’t
know how to use it yet”.
ž ZestFinance and others – provide
methods for credit ranking of the
“underbanked”.
ž Most likely rely on correlations between
attributes, factors and behaviors – and
rates of payment or default.
ž These insights are used for prospective
credit applicants.

6. Example (2) Health Data & IoT
ž Wearables -­ gadgets affixed to the body
which collect biometric and behavioral data.
— Fitbit products provided to employees (for free!).
ž Possible future uses – calculating insurance
premiums.
— Similar processes carried out by smartphone
applications.
ž Again, firms rely on “mere” correlations
found in the data when making health-­
related recommendation and judgments.

7. What Do We Mean by “Just
Correlation”
ž Five possible variations of Big Data uses – relying upon:
1. Mere Correlations
2. Correlation + Statistical proof of causation.
3. Correlation + Experimental evidence of causation
(natural or artificial manipulation).
4. Correlation + reasonable mechanism hypothesis
5. Correlation + scientifically proven mechanism found.
“Mechanism” – term of art;; an explanation of a phenomenon.
• Provides additional proof as to the existence of a
causal relationship
• Provides scientific knowledge.

8. “Just Correlation” – What Can Go
Wrong?
ž Possible outcomes when a Correlation
between Factor “A” and “B” was found:
(i) A (indeed) causes B
(ii) A does not cause B. The data is wrong.
(iii) A does not cause B. The correlation is
spurious.
(iv) A does not cause B. B causes A.
(v) A does not cause B. C causes both A and B.

9. The Benefits of “Just Correlation”
1. The need for speed.
2. Low costs.
3. Does not compromise precision.
4. Does not steer science towards existing
knowledge and theory
-­ Limited bias against unexplainable findings.

10. Just Correlation: Problems (1)
ž Causation as a “Quality Check”:
— Assists in the removal of noise.
— Protects us from “over-­fitting”
○ Do we need a “mechanism”, or does statistical
causation suffice?
— Mechanisms assist in revealing confounders.
ž Having a theory enables generalization
of findings.

11. Just Correlation: Problems (2)
ž Understanding mechanisms alerts us of
possible side effects.
— Important factor in the health context.
ž Seeking mechanisms leads to positive
externalities – knowledge about nature and
society.
ž In Conclusion: Causation provides important
benefits and is essential in the health context.
— A context-­specific analysis is required to establish
whether mechanisms are always mandated.

12. Legal Hooks and Responses
ž Law should not intervene, because:
— Market still self-­correct if mere correlation is error-­ridden
(but…).
— Intervention might undermine innovation.
— Law should not meddle with science – it might serve self
interests, or get things wrong.
ž But…
— Different rules should be applied when government is
the source of data – could require or restrict uses.
— Specific interventions might be called for to protect the
interests of investors, data subjects and those affected
by the process.

13. Investors
ž Protect investors from the executive’s
reckless conduct – mere reliance on
correlation.
ž But,
— Investors should look after their own
interests.
○ Assure disclosure pertaining to this specific
matter.

14. Data Subjects
ž Prediction often involves personal data
— Compromises privacy rights and involves
balancing.
— Possible questions:
○ Was the data de-­identified?
○ Was consent provided?
○ Should processing be allowed even without
consent?
ž The privacy balance should consider
overall benefits – and these require
causation.
— This balance will impact the legal findings as to
whether data usage should be permitted.

15. Impacted Individuals (1)
ž Correlations lead, at times, to negative
treatment.
— With health data, secondary effects might also follow
(such as stigma).
ž Can those negatively impacted by a “mere”
correlation bring action against a firm? Are
such actions and outcomes “unfair”?
○ If a prediction proves wrong, equality is compromised.
— Equals are not treated equally (FTC report).
— However, private firms are not necessarily subjected
to such a fairness requirement.
○ Protected groups might not be implicated.
○ Mitigation via competition (over time).

16. Impacted Individuals (2)
ž When might the fear of unfair outcomes
render “just correlation” – unjust?
— Government (higher fairness standard)
○ And also highly regulated industries…
○ “Socially meaningful” industries
— Health-­care, insurance, credit.
— Monopoly (no mitigating competition)
— In sum: the higher standard would often
apply in the health and medical context.

17. Thank you!
Comments are welcome:
tzarsky@law.haifa.ac.il