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Tech giants will battle over your health data

Published in Turning Magazine , AI & Health edition, February.

Artificial Intelligence (AI) promises great benefits for health care.1 The use of machine learning techniques can speed up diagnoses, in some cases increase their accuracy, and ideally would play a role in prevention. But these promising techniques can challenge our privacy because they require large amounts of sensitive patient data to be gathered and analyzed. Additionally, we can expect a surge in tech companies taking interest in the health sector because hospitals themselves are not likely to have the infrastructure and expertise for handling big data. But if the modern-day equivalent of sapientia est potentia (“knowledge is power”) is data est potentia, then we should think through how much power we want to give to the tech giants. If health research is included in the ongoing “datafication” of the world, are we moving towards a situation where academic hospitals need to buy expensive licenses from Google or Microsoft to access large databases for health research? And to what extent do we want tech companies with flexible morals to be in control of our health data?

Imagine that you overhear someone talking about “Project Nightingale.” You might think this is a reference to a new James Bond movie or some CIA operation, but it is not. It is a medical data sharing project of Google and Ascension, which is the second-largest healthcare provider in the U.S. that manages 2600 healthcare locations, including 150 hospitals2. Somewhere in 2018 Google and Ascension made a deal without consulting doctors and patients or providing them an opt-out, that implies that the electronic health records of up to 50 million patients will be transferred to Google, making them available for the development of AI applications3. Last month (November 2019) a whistleblower working on Project Nightingale reported concerns that this immense data transfer - the biggest in health care so far - might be in breach with the relevant rules on data privacy (HIPAA)4, which has spawned extensive media coverage567 and a federal inquiry.8

The transferred records are reported to include names, addresses, family relations, allergies, radiology scans, medication use and medical conditions. A lack of appropriate anonymization leads to the fear that Google employees working on the project might look into these files. A previous data transfer between Google’s DeepMind and the Royal Free hospital in 2017 had been criticised for having an inappropriate legal basis for another reason9. The UK’s national data guardian pointed out that the deal was justified as directly benefiting patient care, whereas instead it seemed to be primarily used for testing DeepMind’s “Health Streams” app.10

Helen Nissenbaum’s concept of “contextual integrity” is useful for understanding privacy concerns about these developments.11 Consider how we disclose personal and confidential information to a doctor so that the doctor can adequately take care of us. Even though confidential information is shared and you give away some control over your data - the doctor perhaps has to get a second opinion from a colleague - you would almost certainly not think of this as a privacy intrusion. But there is an intrusion if your doctor shares that same information with your employer. In other words, the norm for what is an appropriate flow of information heavily depends on the context and cannot simply be extrapolated. Especially AI students should know about relevant notions of data privacy because there is a good chance they will be confronted with personal data in their future jobs. We have to realize that alongside AI’s promises for the benefit of society, AI techniques can also be used to track our behavior and build personal profiles, for example for undesirable surveillance or political microtargeting1213.

Contextual integrity is upheld in the European General Data Protection Regulation (GDPR) through a purpose limitation principle: personal data can only be collected for a legitimate purpose that is stated in advance and cannot be further processed for other purposes (cf. GDPR art 5(1)(b))1415. The ethical concerns about Project Nightingale are thus not only about confidentiality, but also about whether patient consent can validly be extended to the use of data for future machine learning applications. Doctors connected to the Ascension network surely did not explain to their patients: “Anything you say can and will be used (against you?) by Google. You have the right to remain silent.”

Philosopher Tamar Sharon coined the push of tech companies into the health sector the “Googlization of health research”16. This catchy phrase expresses how tech companies are reshaping health research through crowdsourcing data collection, for example by delegating it to the users who gladly track their own health with running apps and smart watches. Apple facilitates medical research by using the iPhone and Apple Watch. This month Google announced they will be buying FitBit for 2.1 billion dollars17. This is a clever move. Whereas Google’s storing of traditional health data from hospitals is under huge scrutiny, we don’t see riots over Google buying FitBit and potentially using it to track your health. This type of technology could allow a form of liquid surveillance that we would find disconcerting if performed by a centralized authority.

We can avoid paternalism by noting that consumers may share personal data to get some utility in return as long as they are aware of this trade-off. They can read the terms and conditions and decide to opt out. But one should at least realize that most people either have no time to read all conditions, do not understand their legal language, or otherwise feel pressured into accepting them because they need the service. Is this type of consent really informed and explicit? In particular people with health problems are more easily nudged into sharing health data. Consequently, the idea of a voluntary trade-off might be a fallacy 18. As Sharon points out, this makes the use of apps to gather sensitive health data “morally dubious” (p.5).

Sharon points out an additional dynamic specific to apps that collect or require health data, namely that they almost without exception promote their service as altruistic: if we all crowdsource our health data for research we can solve nasty diseases together. The cynical but fair point Sharon makes is that people are most willing to share their sensitive data “when altruistic modes of behavior and financial profit-seeking overlap; and this in ways that are often not transparent.” (p.6). Tech companies know how to utilize this psychological mechanism.[*]

Consequently, Morozov argues that when we really care about privacy we should not just come up with stricter privacy laws, but should also offer a robust intellectual critique to battle and limit the “information consumerism” by which consumers sell out for free apps and material benefits19. Consumers should realize they are still paying but with a new currency: their personal data. Consider the Dutch “a.s.r. Vitality program” that offers up to 8% cashback on health insurances if you show you move enough with a FitBit20. Massive use of similar applications will lead to a normalization of self-tracking, to the point where not participating provides you with serious social and financial disadvantages. Consider an analogy: cashless payment is handy for consumers, but its normalization leads to more services refusing cash and thus forcing us to pay cashless if we need that service.

Such a normalization would leave those that are aware that they pay with their privacy behind with a sense of resignation that the battle over personal data has already been lost. Next to laws protecting against personal data being used without consent, we thus need intellectual activism about the “Googlization” of health research to show people what they are selling before we have lost control over our health data.

[*] This paragraph is edited in the printed version in a way that I do not fully approve of

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  11. Nissenbaum, Helen. (2010). Privacy in Context - Technology, Policy, and the Integrity of Social Life. ↩︎

  12. Zuiderveen Borgesius, F. J., Möller, J., Kruikemeier, S., Ó Fathaigh, R., Irion, K., Dobber, T., … de Vreese, C. (2018). Online Political Microtargeting: Promises and Threats for Democracy. Utrecht Law Review, 14(1), 82–96. DOI: ↩︎

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  15. See page 77 of Chris Jay Hoofnagle, Bart van der Sloot & Frederik Zuiderveen Borgesius (2019) The European Union general data protection regulation: what it is and what it means, Information & Communications Technology Law, 28:1, 65-98, DOI: 10.1080/13600834.2019.1573501 ↩︎

  16. Sharon, T. (2016). The Googlization of health research: From disruptive innovation to disruptive ethics. Personalized Medicine, 13(6), 563-574. DOI: ↩︎

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  18. Turow, Joseph & Hennessy, Michael. (2015). The Tradeoff Fallacy: How Marketers are Misrepresenting American Consumers and Opening Them Up to Exploitation. SSRN Electronic Journal. 10.2139/ssrn.2820060. ↩︎

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