Can Yang Solve the Dilemma of Data Surveillance?
Andrew Yang's policy of "Data as a Property Right" and increased privacy protections is a worthy effort for a politician but misses the mark on many fronts.
- By Barry Devlin
- November 19, 2019
Data landed with a thud on the U.S. political scene with October and November policy announcements by Democratic presidential hopeful Andrew Yang, "to ensure every person whose data is used for tech companies to sell ads ... will get a slice of every digital ad."
It is welcome to see data gaining serious attention from thoughtful politicians -- as opposed to its abuse by populists. Yang's proposals are somewhat wider than the headline-grabbing phrase that he "wants you to make money off your data by making it your personal property." That focus spawned a predictable plethora of speculation about how much your data might be worth. The short answer is not a lot, with estimates ranging up to tens of dollars per year -- hardly life changing. We need to think outside the money box. The dangers of data are far greater than its use in personalized advertising.
From Privacy to Discrimination, Data Can Be Dangerous
During the same time frame as Yang's announcements, data privacy made headlines -- again. On November 1, Google/Alphabet announced the acquisition of Fitbit in a $2.1billion deal that immediately raised concerns about Google's use, reuse, and potential misuse of the personal health, wellness, and location data of Fitbit users. On November 11, the Wall Street Journal reported that Google was involved in "Project Nightingale" with Ascension, a Catholic hospital system that operates in 21 states, to analyze some 50 million personal health records. According to a whistleblower who works on the project, full unencrypted personal details, including name and medical history, are visible to Google staff, authorized only via generic HIPAA consent forms signed by patients, often years ago.
These events represent only the latest examples of what might be called casual disregard for people's privacy by businesses that are -- at least in principle -- operating for their clients' good. More worrying is a wide-ranging set of government big data and analytics projects and actions summarized by the U.N. Special Rapporteur on extreme poverty and human rights, Philip Alston: "As humankind moves, perhaps inexorably, towards the digital welfare future, it needs to alter course significantly and rapidly to avoid stumbling zombie-like into a digital welfare dystopia." Despite the reference to extreme poverty in his job title, Alston has not confined himself to the so-called developing countries. He has also released damning reports on data-driven poverty in the U.K. and U.S.
In the world's wealthiest countries, governments are rolling out big data and analytics programs, citing fraud elimination, efficiency, and cost savings as worthy societal benefits. The effects on the poor and digitally illiterate are cruel. In Illinois, for example, the state is pursuing elderly people in an effort to claw back thousands of dollars of alleged benefit overpayments from the 1980s, based on automated, outsourced machine learning analysis of historical data. Using this personal data, available in this instance only to government agencies, Illinois increased four-fold the annual number of debt collection notices sent between 2010 and 2017, with an unknown proportion relating to multiple-decades-old alleged debt.
These stories represent but a tiny percentage of the variety and number of the wide-ranging and distressing effects of current data use and abuse.
Does Yang Have the Answer?
In addressing the societal and individual challenges posed by pervasive collection and analysis of personal data, Yang -- as a former lawyer -- should bring a novel and valuable perspective. Sadly, he starts from the dubious "data as the new oil" metaphor and opts to describe data as "private property" that can be owned and monetized by the general public.
The fundamental difference between a physical resource such as oil (that can be used only once) or land (that can be exclusively owned) and data (that is infinitely and freely replicable) creates significant difficulties for the concept of ownership. The idea of monetizing personal data, although potentially attractive at the election stump, does not even begin to consider the wider privacy and discrimination issues already widespread in our data-obsessed society.
The dilemma faced by Yang and other concerned politicians is twofold. First, no underpinning theory to describe or debate the challenges of pervasive data creation and use is accepted widely by economists, philosophers, or even IT experts. Second, the topic is so far from the education and lived experience of most voters that simplistic stories of monetary gain are likely to be misinterpreted and lead to disillusion when they fail.
In a recent article, I shared the concept of surveillance capitalism as described by Shoshana Zuboff, professor emerita at Harvard Business School. Her thesis is that pervasive data collection, analytics, and prediction drives a new (and dangerous) form of capitalism that cannot be understood with previous models. She posits that we are seeing worldwide, independent attempts to transform the market and society into a completely asymmetrical information environment that offers a small elite -- both political and commercial -- unprecedented power to direct public attention and action with near-total certainty of the outcomes. If Zuboff is even partially correct, Yang's policy statement, based on the old certainties of market capitalism, misses the mark by a country mile.
Whether it be privacy invasion by business or government, anticompetitive behavior, societal destabilization through disinformation, or personal endangerment through lax data security, we need novel and deep thinking about the surveillance data dilemma from all areas of society. Yang's policy contribution does, at least, bring some of the issues to public attention, but he must do better.
Dr. Barry Devlin defined the first data warehouse architecture in 1985 and is among the world’s foremost authorities on BI, big data, and beyond. His 2013 book, Business unIntelligence, offers a new architecture for modern information use and management.