Taxing Big Data: A Proposal to Benefit Society for the Use of Private Information
Ziva Rubinstein
Note

  The full text of this Note may be found here.

31 Fordham Intell. Prop. Media & Ent. L.J. 1199 (2021).

Note by Ziva Rubinstein*

ABSTRACT

[A]

rtificial intelligence, the technology that is currently shaping our world, relies on the data that each individual supplies. In 2017, the Economist magazine asserted that “the world’s most valuable resource is no longer oil, but data.” This assertion is supported by the current data market, which became a hundred-billion-dollar industry in the data broker market alone. However, despite its immense value, individuals are not compensated when their data is collected, shared, or when that data is used to replace them in the job market. Further, companies are legally avoiding taxes on this resource, both during its collection and on the profits it creates.
Prior to the widespread use of AI, society expected their private information to be respected. Before the internet boom, companies were willing to pay the public for their information. When information was supplied, people expected some form of payment in return. Now, payment is unnecessary because our phones automatically give companies all of the data they need to know, and then some. Companies have become more reliant on our information and are constantly collecting it at higher rates, but no longer pay us because they no longer need our consent.
Currently, no legal regime provides solutions or safeguards for this exploitation. This allows companies to accumulate and share mass amounts of personal information, while financially harming individuals in the process. Recognizing the importance of the legislature to combat the resulting harms of emerging innovation, this Note proposes a unique solution which addresses both the exploitation of our data and corporate tax avoidance: a tax on the data itself.

* J.D. Candidate, Fordham University School of Law, 2022; B.E., Biomedical Engineering, Macaulay Honors College at City College of New York, 2017. I would like to thank Professor Shlomit Yanisky-Ravid for her support in writing this Note, as well as for sharing her immense knowledge of emerging technologies. I would also like to extend my sincerest gratitude to the IPLJ Editorial Staff, specifically Sara Mazurek, for their invaluable comments and guidance. Lastly, thank you to my father, Ira Rubinstein, for his constant love and encouragement.