|Title:||Disclosing Big Data|
|Citation:||Mattioli, M. (2014). Disclosing Big Data. Minn. L. Rev., 99, 535.|
|Link(s):||Definitive , Open Access|
|Key Related Studies:|
|About the Data|
|Data Description:|| This study draws on a set of interviews and surveys that the author conducted with informaticists, data scientists, lawyers, and business professionals working in big data across different industries.
The author assembled a listing of all big data companies, initiatives, and projects described in national newspapers, books, journals, and online press published since 2009. The author interviewed individuals at these organizations by telephone. All interviews lasted at least forty-five minutes, and some lasted hours. The author does not state how many interviews he conducted.
|Data Type:||Primary data|
|Secondary Data Sources:|
|Data Collection Methods:|
|Data Analysis Methods:|
|Cross Country Study?:||No|
|Government or policy study?:||No|
|Time Period(s) of Collection:||
This Article reveals that the law is failing to adequately encourage producers of “big data” to disclose their most innovative work to the public. “Big data” refers to a new industrial and scientific phenomenon that holds the potential to transform diverse industries — from medicine, to energy, to online services. At the heart of this phenomenon are innovative and complex practices by which experts shape featureless digital records into valuable information products. The fact that these big data practices are unlikely to be disclosed to the public is worrisome for familiar reasons: the law generally prefers to induce technological disclosure in order to serve the goal of promoting progress. Beyond this general concern, the nondisclosure of big data practices threatens innovation in unique ways that are particularly insidious. The cause of this problem, and possibly its resolution, lies in the interplay between big data and intellectual property law — a nexus that scholars have not explored until now.
Main Results of the Study
Main results of the study:
- Big data practices do not fit neatly into the traditional intellectual property paradigms of patent or copyright.
- Existing intellectual property policy does little to meaningfully encourage the disclosure of big data practices.
- A variety of forces, both legal and economic, are powerfully pushing data producers toward nondisclosure.
- Big data practices are highly subjective, and difficult to uncover through reverse engineering. As a result, big data practices lend themselves toward secrecy.
- Findings varied across different types of big data practices: filtering non-relevant data (i.e., "noise") from large datasets; identifying and correcting errors based on estimates or guesses; "masking" data in order to preserve the anonymity of individuals; and classifying data.
Policy Implications as Stated By Author
- There is a need to encourage greater disclosure of big data practices. There may be many ways to further this goal, such as new rulemaking within federal agencies, or perhaps a legislative change to intellectual property law.
- This article explores the latter possibility by presenting a dataright as an exploratory device. By offering big data producers something new and valuable-an exclusive right to limit downstream use of their data-this new intellectual property right could encourage valuable technological disclosures that would otherwise remain shrouded in secrecy.
- This solution would carry substantial drawbacks however: it would encourage disclosure only in settings where data producers value data exclusivity more than they value secrecy in their methods. Moreover, this solution would entail significant new costs. Whether these costs would be outweighed by the plan's benefits would be a productive starting point for future discussion.
- In light of big data's growing economic and social importance, policymakers and the public should be concerned with how the legal system will influence the production and use of this valuable new resource. Currently, the intellectual property system is not well configured to meet its goal of encouraging technological disclosures in this new frontier.
Coverage of Study
|Level of aggregation:||Individual|
|Period of material under study:||Not stated|