Request For Research Presentations For the PrivacyCon Conference
Abstract: Privacy is concealing certain details about oneself from the world. In economic jargon, it means to "pool" with others. Economists, however, typically to prefer separating to pooling equilibria, as the former tend to lead to more efficient outcomes. This tension between privacy and market efficiency¬-between pooling and separating equilibria-is on full display in the burgeoning privacy law scholarship surrounding big data to make predictions about us. The privacy concerns raised in the big data context in large part have centered on so-called "predictive privacy harms," which arise as big data allows firms to make granular distinctions based on predictive algorithms and to tailor offers to customers, employees, and borrowers accordingly. The increasing use of algorithmic predictions based on big data have led some to call for limits on their use. Although informational asymmetries justified in the name of privacy are likely to give rise to costs, we cannot ignore that privacy itself has intrinsic value; most people are willing to pay to avoid unwanted surveillance, and privacy gives rise to broader social benefits. Indeed, information collection and concomitant algorithmic predictions designed merely to transfer surplus (e.g., "sucker lists") are wholly dissipative. Thus, merely to say that privacy retards information flows is insufficient to blunt calls for restrictions on the use of big data to make predictions about us. Nonetheless, just as policy should discourage dissipative investments in information revelation, it is equally crucial that policy discourages dissipative privacy-strategic concealment of facts relevant to a transaction in hopes of getting a better deal. This paper brings to bear insights from the economics of contracts and torts to develop a positive framework that helps identify dissipative and productive privacy, and that aids in identifying factors that militate for and against regulating big data. Application of the model to the use of big data to effect price discrimination and separation in labor and credit markets suggest-contrary to much of the scholarship-that the poor stand to gain from big data-driven separation. The extant empirical literature tend to support this prediction.