Weekend Roundup: Regulating and Publicly Sharing Data Would Break Big Tech’s Hold on Our Future

Nathan Gardels

Reining in big tech is the next big thing. (WorldPost Illustration)

Like political revolutions, technological revolutions tend to unfold in phases. First comes the liberating breakthrough from the old order, burnished by utopian ideals. Next comes the reaction to abuses that inevitably arise on a new path for which there are no rules, especially for the first movers, who become the new masters. Finally, a new governing order is established that sorts out the mistakes and excesses from the benefits of transformational change and eliminates or tempers them.

The current “techlash” against the biggest players of digital capitalism marks the beginning of this last stage of transformation. The regulatory superpower that the European Union has become was the first to forge a new order with the General Data Privacy Regulation put into law in 2016. In the U.S., both the Federal Trade Commission and Congress have tabled anti-trust proposals to break up the big data monopolies such as Facebook or Google the way big telecom like AT&T was dismantled in the 1970s.

California, the country’s largest state and home to Silicon Valley, recently passed its own data privacy regulations. And in his State of the State address last year, California Governor Gavin Newsom went so far as to call for a “data dividend” to be distributed to the state’s residents for the use of their personal information by big tech.

In The WorldPost this week, economist Yakov Feygin turns his sharp mind toward this challenge.

“Regulating the digital economy will mean not only assuring competition,” he writes, “ but also actively incentivizing ways it serves the common good.” But there is a dilemma, he notes. “On one hand, big data requires scale and uniformity that fits a large network better than many small silos. However, this same effect is also responsible for an inequitable, politically opaque and ultimately inefficient structure for a vital new industry. The good news is that we’ve been here before, and we have the tools to solve these issues.” He cites the example of another general purpose technology from an earlier era, electricity, when the big companies were designated as public utilities and reined in by regulation during the Progressive Era at the turn of the 20th century.

But Feygin goes further. Given the immense potential of a future boost in productivity from the digital revolution, he argues that sharing data through public trusts would be far more valuable than regulation alone or compensating individuals for big tech’s use of their data.

“Our individual information is valuable but not as valuable as the aggregated information of many users,” he says. “‘Big data’ allows developers to sharpen predictive and analytic tools and could eventually be the fuel upon which giant leaps in artificial intelligence is developed.” Here he cites the example of the Defense Advanced Research Project Agency, or DARPA, during the Cold War, which facilitated the sharing of propriety information among companies for the public good.

“The experience of utility regulation and DARPA provide us with a blueprint to tackle a monopoly in the new economy in a way that benefits multiple stakeholders rather than entrenched interests,” he observes. “Public data banks — ‘data utilities’ — should be established by localities, states and the federal government. The goals of these data banks should be to establish a standard for public data, integrate private data with public data to create universal access to all firms and to create an ecosystem in which private companies are dependent on the public data bank and each other to innovate. Such agencies should aggressively solicit the transfer of private data to public uses using contracts and tax incentives.”

This approach already exists, for example in Toronto, where Google is collaborating with the city to create a public data trust accessible by the public. For California, where the political environment is ripe to act, Feygin proposes “a data tax to fund a ‘Data Relations Board’ that would build on existing legislation to build a public data set. The DRB would be able to use tax breaks and other incentives to encourage firms to transfer proprietary data sets into the DRB’s custody for wide use. By establishing a public infrastructure, this new agency would be able to guide the development of the data economy toward a larger public good.”

The widespread sharing of publicly accessible data, Feygin concludes, would ensure that the wealth generated by unleashed productivity would benefit everyone instead of just accrue to the first movers who have captured monopoly rents on the use of our information.

composed by Arswain
machine learning consultation by Anna Tskhovrebov
commissioned by the Berggruen Institute
premiered at the Bradbury Building
downtown Los Angeles
april 22, 2022

Human perception of what sounds “beautiful” is necessarily biased and exclusive. If we are to truly expand our hearing apparatus, and thus our notion of beauty, we must not only shed preconceived sonic associations but also invite creative participation from beings non-human and non-living. We must also begin to cede creative control away from ourselves and toward such beings by encouraging them to exercise their own standards of beauty and collaborate with each other.

Movement I: Alarm Call
‘Alarm Call’ is a long-form composition and sound collage that juxtaposes, combines, and manipulates alarm calls from various human, non-human, and non-living beings. Evolutionary biologists understand the alarm call to be an altruistic behavior between species, who, by warning others of danger, place themselves by instinct in a broader system of belonging. The piece poses the question: how might we hear better to broaden and enhance our sense of belonging in the universe? Might we behave more altruistically if we better heed the calls of – and call out to – non-human beings?

Using granular synthesis, biofeedback, and algorithmic modulation, I fold the human alarm call – the siren – into non-human alarm calls, generating novel “inter-being” sonic collaborations with increasing sophistication and complexity. 

Movement II: A.I.-Truism
A synthesizer piece co-written with an AI in the style of Vangelis’s Blade Runner score, to pay homage to the space of the Bradbury Building.

Movement III: Alarmism
A machine learning model “learns” A.I.Truism and recreates Alarm Call, generating an original fusion of the two.

Movement IV: A.I. Call
A machine learning model “learns” Alarm Call and recreates A.I.Truism, generating an original fusion of the two.

RAVE (IRCAM 2021) https://github.com/acids-ircam/RAVE