Plausible deniability and fragmentation are the two most powerful forces regularly undermining people’s choice, wealth, and privacy. Like organized crime syndicates that move cash between parties to conceal its true origin, certain data market participants conduct three-card monte schemes through shell-entities to launder consumer data, allowing other industry participants to plead ignorance while monetizing the illegitimately sourced property. A lack of collective representation for consumers ensures companies can continuously abuse the very people who create the data without repercussions. The industry practice is unacceptable and must be confronted directly.
Through their selected marketplace representative, organized consumers can remove plausible deniability and disincentivize companies from transacting with others who traffic non-consensually sourced consumer data.
Let’s examine how the data laundromat works, who it benefits, and examples of collective representation successfully obtaining better treatment for its constituents.
The first step in any laundering scheme is for an entity to gather something that does not belong to them. In this case, certain data aggregators collect extensive records about their users through opaque legal agreements using techniques described in the previous “Death of Consent” article. The collected user data can include behavioral data about how the consumer interacted with a digital service, and identifying information like email addresses, physical addresses, geolocation, and IP addresses. The identifying information can in turn be used by data brokers to match behavioral and demographic data gathered about a consumer from multiple data aggregators.
Next comes a handoff to another party to create a layer of plausible deniability. Data aggregators sell consumer data to data brokers to monetize their information about their users. Although the consumer information might be sold anonymously to the data brokers, companies who gather enough consumer data can pierce the anonymity veil. According to Georgetown Law, “63% of the population can be uniquely identified by the combination of their gender, date of birth, and zip code alone.”[1] When a data broker buys enough consumer data from different data aggregators, the data broker creates comprehensive profiles about consumers. The data broker does this because data buyers pay a premium for comprehensive consumer profiles.
Data brokers that know they are operating unethically or illegally go through a series of laundering steps to create additional layers of obfuscation. The intention is to make it very difficult for regulators or other inquiring minds to trace their acquired data’s origin; one cannot prove information was gained illegally or non-consensually if there are no links to the source of the data. For example, consumer data about Americans may be sold from one data aggregator to a series of entities whose ownership records are muddy and controlled by one or a group of affiliated data brokers. The entities can later transfer the data to an offshore entity, distorting the digital trail further.
After the consumer data is mixed and combined, some data brokers register a shell-corporation within the country where they wish to sell consumer data. Launderers can use their own servers to host the illicitly collected data or rent them from large tech companies. Once their data is hosted, certain large tech companies facilitate transfers to data buyers through data lakes, also called data pools, which are massive collections of consumer data. The operators of the servers and data lakes act as the fence for the stolen property and are financially incentivized to not scrutinize what consumer data is being passed around. They want business to be as frictionless as possible to facilitate the most amount of data to be hosted and transferred to maximize their own earnings.
Based on my direct experience with the service providers described above, their help desks and support functions provide no help for diligent consumers looking for transparency and accountability. They know people have no recourse or leverage, so they take the stance that it is not their responsibility to monitor data sources. Instead, they push the responsibility to the data aggregators and data brokers, ensuring the problem is never addressed. There is no incentive for these parties to care. Another corporate entity, aligned with consumers’ best interests, must be inserted in this process to demand accountability and incentivize compliance.
Collective and active representation manifests equitable treatment
Individually, there is little a person can do to short circuit the laundering machines and cease unfair practices actively harming them. A person cannot effectively battle massive, entrenched corporations whose financial incentives are opposite to their own, and so a person’s choice, consent, and best interests are too easily disregarded. The burden, which is shared among all consumers, must be centralized. Consumers need to organize under one banner so their common interests can be relentlessly pursued by a single representative. Once a consumer selects a worthy representative, there are various tactics that can be employed to secure fair treatment. Successful labor unions and engaged investment managers provide useful blueprints of how they effectively negotiate for their collective in the marketplace.
Labor unions in the United States were created during the Great Depression era to help workers organize and establish negotiation leverage to combat abusive treatment in the workplace. Like people and their digital property today, workers were routinely exploited by unsafe practices and unfair compensation while generating substantial profits for the companies monetizing their labor. While the popularity of labor unions in the United States has subsided, the strongest ones remain active today. In September 2023, one of the largest and oldest American unions, the United Auto Workers (UAW), carefully organized a six-week strike at certain Ford, Stellantis, and General Motors plants in Detroit. The UAW’s stated purpose was to align corporate profitability and employee well-being, ensuring its members’ best interests were represented and ceasing “divide and conquer” strategies designed to diminish its members’ negotiating leverage.[2] Following a series of escalations and high-stakes negotiations, all quarterbacked by the UAW, the union secured a favorable contact for its members. According to NPR, workers gained significant wage increases, meaningful inflation-adjusted cost of living bonuses, and better benefits for newer workers.[3]
Investment funds were created to serve as a financial collective where its members contribute their property, usually cash, and appoint a manager to oversee the pool on its members’ behalf. The manager of the fund is responsible for understanding how conservative or risky its members want to invest, researching potential investments that fit its members’ risk appetite, and investing in the best-qualified opportunities. Some opportunities come from engaging to change corporate behaviors that are actively harming the fund’s members. One such highly engaged investment was conducted by Warren Buffet in the 1970s when the insurance company GEICO was floundering due to mismanagement.[4] Buffet’s intervention helped the company avoid bankruptcy, protecting its members’ investments, by challenging GEICO to rethink its approach with both investors and customers. Buffet’s GEICO investment was one of the most successful of all time and illustrated how an engaged representative can productively cooperate with companies to benefit their collective.
Other opportunities to short circuit the laundering machines come from shining a light on nefarious activities, educating market participants and removing plausible deniability for those associated with schemes harming the public. In 2020, the investment group Hindenburg Research published a series of research reports outlining fraudulent behavior by Nikola Motors and its Chief Executive Officer, Trevor Milton.[5],[6] The group alleged Milton and the company misled the public to inflate Nikola’s stock price and solicit industry partners. General Motors, which previously expressed interest in investing in and contracting with Nikola, could not ignore the toxic cloud surrounding Nikola and broke ties with the company shortly after Hindenburg’s reports were published.[7] The well-documented research by Hindenburg Research helped its members avoid substantial losses. The group’s decision to publish its work removed the industry’s plausible deniability, causing Nikola Motors to become a pariah and stopped Milton’s ability to do more damage to the general public. The reports eventually laid the groundwork for Trevor Milton’s securities fraud conviction several years later.[8]
Choose My Data Union as your representative
My Data Union was designed for its members to force change upon an industry that disregards their concerns and financial interests. Using its member base as leverage, My Data Union can employ the methods used by both engaged labor unions and investment groups. Like a labor union leader, it can negotiate to secure fair terms and compensation for its members, while providing internal representation, education, and transparency throughout the process. Like an investment manager, My Data Union can work with different companies to find mutually beneficial outcomes and publish proprietary research to call out harmful activity. Through these mechanisms, its members can break the data laundering machine, exile bad actors, and receive fair compensation while doing it. Sign up below if you would like My Data Union to represent you.
[1] https://georgetownlawtechreview.org/re-identification-of-anonymized-data/GLTR-04-2017/
[2] https://uaw.org/backgrounder-big-3-bargaining
[3] https://www.npr.org/2023/11/16/1212381342/gm-autoworkers-vote-yes-approve-uaw-contract-ford-stellantis
[4] https://www.geico.com/about/corporate/history-the-full-story
[5] https://hindenburgresearch.com/nikola
[6] https://hindenburgresearch.com/nikola-response
[7] https://www.cnn.com/2020/11/30/business/nikola-gm-badger
[8] https://www.justice.gov/usao-sdny/pr/trevor-milton-sentenced-four-years-prison-securities-fraud-scheme
