Modern Tech Ethics

The lack of certainty surrounding whether a causal relationship exists when large data sets provide information can create ethical dilemmas. Black-box medicine pulls together huge datasets to predict what medicines might work, primarily based on patterns rather than an understanding of disease mechanisms. (Price) The patterns can be based on genetic and biological data, can be beneficial to health, and can lead a hypothesis later tested. That is, research eventually may answer the why and confirm causality. There are vast benefits to black-box medicine but it is unclear how to evaluate the duty of the doctor to a patient for whom the recommendation fails or what to do when the recommendation the algorithm produces violates the known standard of care. Does black-box data equate to the best medical judgment of the doctor or replace it? The algorithm choice does not corroborate a care choice (it does not supplement expertise), it supplants expertise. The level of risk (the side effect profile and risk of foregoing the standard of care based on the condition) is an important consideration. Risk 1: Following the computer-generated advice. Risk 2: Forgoing the standard treatment. Risk 3: Who is responsible if it turns out bad (negligence, malpractice, no consequences)? Risk 4: Can the developers be held responsible? Risk 5: The regulations do not keep up with the technology.

See W. Nicholson Price, III, “Medical Malpractice and Black-Box Medicine,” Big Data, Health Law, and Bioethics, Chapter 20. edited by I. Glenn Cohen, Holly Fernandez Lynch, Effy Vayena, Urs Gasser, Cambridge University Press, 2018

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Data-Generating Patents require a broad ethical approach that incorporates business ethics that foster the spirit behind antitrust law and competition to protect consumers, and privacy ethics. Intellectual property rights are expanding. Data-generating patents can preclude other ways of obtaining, collecting, or generating the same type of data. The data generated is protected as a trade secret. The patents provide a windfall of market share in the data market which is not the market of the technology or biomedical device patented. (e.g., if a patented search engine of social media outlet collects data from millions of people, trade secret law protects the actual data; when a way of testing for BRCa is patented this way as in the Myriad Genetics case, the company has access to the data of those tested, monopolizing some types of data.) In Association for Molecular Pathology v. Myriad Genetics, Inc., the Supreme Court held that natural sequences of DNA (gDNA) were unpatentable but that cDNA is synthetic and therefore patentable. Brenda Simon and Ted Sichelman note that even after losing the patents on most of the products and the impending expiration of others, Myriad continues to use trade secret law to protect its database of patient information. Trade secret law does not have an end date so the ability to create a monopoly, barrier of entry to competing businesses, or to use big data as an advantage in marketing and producing other products is great. The market control can inhibit innovation and access to data for the public good or public health, hurting consumers and the public.

The privacy problem associated with data-generating patents and generally with any products that generate data that is exclusively in the hands of the products’ creator, is that the general public may not be aware of the data’s collection and use. The data is also often separate and distinct from the product. That is, someone may use an Apple watch without realizing what biometric and other personal data including time and place data will then be “owned” or exclusively in the purview of Apple. The audio information collected could track a conversation, its time and place. That audio includes people near the watch, not only the wearer of the watch. Apple’s patents refer to “additional sensor data”, data clusters, and personal characterization data, some of which could provide helpful information to the user, but also could provide traffic data to the government or classify users by habits like staying up late. Tracking routine activities enables companies to have a marketing advantage in products completely unlike the products that led to the data collection.

The ethical issues of monopolistic behavior, privacy breaches, and the marketing advantages are just the surface. In Barcode Me, I explore the concept of being paid for data collected, addressing the ethical issue of corporate profiteering. Additional issues include bias and how goods and services are marketed based on stereotyping, preference assumptions based on behaviors, and how the marketing itself can feed many divides. For example, people eating inexpensive, packaged foods will be marketed more inexpensive, packaged foods and the other things associated with such a stereotype. People eating organic foods will be marketed more of such foods as well as things deemed by big data to go along with those eating habits. In the case of data generated by an expensive product, low-income consumers may be left out altogether, skewing large data sets, leading to conclusions that may affect public health, public policy, corporate behavior, and health care. Furthering the divide in a consumer way can lead to further political and economic polarization, affect health disparities.

The deeper ethical issues of how we want tech companies or discoveries regulated get to the essence of who should benefit from technology and how. Overregulation would deprive the population of the benefits of big data, yet a failure to protect consumers leaves them vulnerable to monopoly behaviors, high prices, stereotyping, and a lack of control over their own data. A multiangled approach could look to using current antitrust laws and to modernizing antitrust laws to solve some of the issues and require products to create better ways for consumers to limit data collection.

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