Week 8: Privacy Policies, Incomplete Contracts, and Power

DSAN 5450: Data Ethics and Policy
Spring 2026, Georgetown University

Jeff Jacobs

jj1088@georgetown.edu

Wednesday, March 11, 2026

Privacy Regulations, Privacy Policies, and You

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Regulations: Comparative Perspective

No single, “universal” data privacy law $\implies$ compare and contrast various country/state/org attempts to tackle data policy issues
Important to retain descriptive/normative distinction! They’ll become harder to distinguish as we discuss:
What are the regulations currently in existence? (Descriptive)
- Do we see a trend? (California Effect vs. Delaware Effect)
What are their drawbacks? (Normative)
- Fundamental problem of contracts
Which drawbacks could be addressed “easily” via policy? (requires understanding processes of policy formation)
Which ones could not? (Prisoner’s Dilemma!)

OECD Guidelines, 1980

“The basis for most modern privacy laws” (Sugimoto, Ekbia, and Mattioli 2016)
Collection Limitation Principle: data may be collected “where appropriate, with the knowledge or consent of the data subject.” (OECD 1980, 14)
Use Limitation Principle: “Personal data should not be disclosed, made available or otherwise used for purposes other than those specified [at time of collection] except with the consent of the data subject” (OECD 1980, 15)

EU Data Protection Directive, 1995

Art. 7: Processing allowed when “the data subject has unambiguously given his [sic] consent.”
Art. 8: Use of sensitive data is restricted, except where “the data subject has given his [sic] explicit consent to the processing of those data.”
Art. 26: Prohibits export of personal data to non-Euro countries lacking “adequate data protection”, except when “the data subject has given his [sic] consent unambiguously to the proposed transfer” (European Union 1995)
Superceded by GDPR in 2018

Distributional Effects

From Demirer et al. (2024)

Easy Mode: Policy Diffusion

From Romanosky, Telang, and Acquisti (2011)

(Policy Diffusion Curve!)

👀

Original (theoretical) “adoption curve”, from Rogers (2003)

Hard Mode: Impact of Policy Diffusion

Result (Note the explicitly-identified independent $\rightarrow$ dependent vars!):

Table 5. Effect of law on identity theft
Dep Var: `log(idtheft)`	Basic	Basic + Controls
`hasLaw`	–0.050* (0.026)	–0.061*** (0.023)
Income per capita		0.000 (0.000)
Unemployment rate		0.003 (0.010)
Log(population)		–0.268 (0.343)
State and time fixed effects	Y	Y
Constant	6.852*** (0.014)	11.248** (5.317)
R-squared	0.848	0.850
***: $p < 0.01$	**: $p < 0.05$	*: $p < 0.1$

The Crux of the Normative Issues

Does Reading = Understanding?

Does reading $\implies$ understanding implications / contingencies / ambiguities?
NLP could (and should!) be helpful (“making privacy policies machine readable […] would help users match privacy preferences against policies offered by web services”), but mostly just reveals how bad the problem is:

Figure 15 from Wagner (2023). “Obfuscatory words” are words like acceptable, significant, mainly, or predominantly, interpretated at the discretion of companies rather than users (see next slide!)

The Intuitive Problem of Contracts

Hard to read, harder to understand, possibly rly bad stuff in them, won’t know until you read + understand
Solution (in theory… in modern liberal market-based democracies): Collective action!
Option 1 (Exit): Find better platform, use it instead $\Rightarrow$ company dies (competitive market)
Option 2 (Voice): Raise a fuss, hoot and holler, make a big stink about it, etc.
- $\Rightarrow$ (2a) Company will change/remove it to avoid embarrassment and/or prevent Option 1 becoming an option
- $\Rightarrow$ (2b) Government intervention (hypothetical functional government)

The Fundamental Problem of Contracts

Just as we can’t observe two simultaneous worlds $W_{X = 0}$ and $W_{X = 1}$ which differ only in the value of $X$,
We can’t foresee all possible contingencies that need to be included in a contract
- (We can try, though! Hence use of obfuscatory words to minimize liability)
So, when a situation arises which is not covered by a clause in the contract, what happens? What principle determines whose interpretation wins out?
- (Hint: It is actually literally my legal middle name…)

…POWER!

Examples from employment contracts (tooting own horn):
In a private, cooperatively-owned, democratic firm, outcome determined by conversation, majority vote, unanimity, etc.
- These technically exist in the US! Employing 2,380 workers, $\frac{2380}{127509000} \approx 0.0019\%$ of US workforce
Otherwise, in a non-unionized private firm (94% of total), the outcome is determined by organizational hierarchy
- This is the case for $\frac{125000000}{127509000} \approx 98.03\%$ of US workforce

Descriptive and Normative Implications

Who has power w.r.t. incompleteness of contracts?
Who ought to have power w.r.t. incompleteness of contracts?
Residual rights of control…

Hart’s Nobel Prize Speech

Complete contracts are contracts where everything that can ever happen is written into the contract. Actual contracts aren’t like this, as lawyers know. They’re poorly worded, ambiguous, leave out important things. They’re incomplete.

A critical question that arises with an incomplete contract is, who has the right to decide about the missing things? We called this right the residual control or decision right. The question is, who has it?

Further thought led us to the idea that this is what ownership is. The owner of an asset has the right to decide how the asset is used where the use is not contractually specified (Hart 2017)

Understanding Rights $\leftrightarrow$ Fighting for Rights

“Hohfeldian” framework (Hohfeld 1913)
A right $r_i$ granted to person $i$ $\implies$ A duty/obligation imposed on everyone in the world besides $i$ (to respect $r_i$)
A duty or obligation $d_i$ imposed on a person $i$ $\implies$ A right granted to everyone in the world besides $i$ (to… be a potential beneficiary of $d_i$)
$\implies$ rough measures of relative power in a contract:

\[ \frac{\text{rights}_i}{\text{rights}_j} = \frac{\text{obligations}_j}{\text{rights}_j} = \frac{\text{rights}_i}{\text{obligations}_j} = \frac{\text{obligations}_j}{\text{obligations}_i} \]

The Adversarial-Sisyphusian Problem of Contracts

Recall Intuitive Problem of Causal Inference: Correlation $\nimplies$ Causation, but can do a bunch of work to overcome
Adversarial-Sisyphusian Problem is one level worse 😱
- IPCI: You vs. discovered correlation (inanimate)
- ASPC: You vs. companies investing resources 💰 into making the problem harder and harder for you
tldr: The moment you ($N=1$, $) finally find and “fix” bad thing, company ($N \gg 1$, $$$) adds more ambiguity to re-enable / sends your data to "new", "different" 3rd-party processor 🥸

References

Demirer, Mert, Diego J. Jiménez Hernández, Dean Li, and Sida Peng. 2024. “Data, Privacy Laws and Firm Production: Evidence from the GDPR.” Working Paper. Working Paper Series. February 2024. https://doi.org/10.3386/w32146.

European Union. 1995. Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of Such Data. OJ L. http://data.europa.eu/eli/dir/1995/46/oj/eng.

Frey, Carl Benedikt, and Giorgio Presidente. 2024. “Privacy Regulation and Firm Performance: Estimating the GDPR Effect Globally.” Economic Inquiry 62 (3): 1074–89. https://doi.org/10.1111/ecin.13213.

Hart, Oliver. 2017. “Incomplete Contracts and Control.” American Economic Review 107 (7): 1731–52. https://doi.org/10.1257/aer.107.7.1731.

Hohfeld, Wesley Newcomb. 1913. “Some Fundamental Legal Conceptions as Applied in Judicial Reasoning.” Yale Law Journal 23. https://heinonline.org/HOL/Page?handle=hein.journals/ylr23&id=24&div=&collection=.

Miller, Klaus M., Karlo Lukic, and Bernd Skiera. 2025. “The Impact of the General Data Protection Regulation (GDPR) on Online Tracking.” International Journal of Research in Marketing, March. https://doi.org/10.1016/j.ijresmar.2025.03.002.

OECD. 1980. OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data. Paris: Organisation for Economic Co-operation and Development. https://www.oecd-ilibrary.org/science-and-technology/oecd-guidelines-on-the-protection-of-privacy-and-transborder-flows-of-personal-data_9789264196391-en.

Rogers, Everett M. 2003. Diffusion of Innovations, 5th Edition. Simon and Schuster. https://books.google.com?id=9U1K5LjUOwEC.

Romanosky, Sasha, Rahul Telang, and Alessandro Acquisti. 2011. “Do Data Breach Disclosure Laws Reduce Identity Theft?” Journal of Policy Analysis and Management 30 (2): 256–86. https://doi.org/10.1002/pam.20567.

Sugimoto, Cassidy R., Hamid R. Ekbia, and Michael Mattioli. 2016. Big Data Is Not a Monolith. MIT Press. https://books.google.com?id=yp9SDQAAQBAJ.

Wagner, Isabel. 2023. “Privacy Policies Across the Ages: Content of Privacy Policies 1996–2021.” ACM Transactions on Privacy and Security 26 (3): 32:1–32. https://doi.org/10.1145/3590152.