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Taking a Product to Market
Taking a Product to Market


Author(s) Dalton George Jason Ludwig
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Year 2017
Funder NSF
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Notes This case/scenario was developed with support of NSF Award #1338205 Ethics of Algorithms (from NSF's EESE program). The full set of the Ethics of Algorithms cases is available at The principal investigators, Kelly Joyce and Kris Unsworth, conducted fieldwork and interviews with computer scientists and engineers to identify the ethical challenges they face when working with algorithms and big data. Dalton George and Jason Ludwig were research assistants on the project and used the data to develop the case. The research team tested the case in multiple classrooms and revised the case based on instructor and student feedback.
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  • Carolina Salge

    Posted 2 years and 7 months ago

    This case presents a hypothetical scenario in health care. The framing here is on algorithmic design and product recommendation accuracy. Ethics is salient even though one might argue it is also a design choice type of problem. But because this relates to health and insurance premiums the ethical link makes a lot of sense.

    Overall, the case is well presented and it includes a brief ethical analysis by discussing the principle of beneficence and how it relates to decision making. Multiple ethical perspectives are not brought to question though. I think that the case can be much stronger if that is done. Here I will present two different scenarios for further discussion. One relies on the algorithm as the "punisher" since it basically penalizes people for smoking. The other relies on the algorithm as the "extremist" since it ignores every other aspect about a person's health status rather than their smoking behavior.

    Scenario One: The Punisher
    Can we think of punishment for good? In this case, the algorithm, in its current form, exposes the truth about people when it comes to their smoking behavior. In doing so, it purposively places smokers in the high risk group which essentially means they will have higher insurance premiums. At first sight, this is bad, perhaps seen as discrimination. But can we think of such punishment as good? Can we use it, for example, to provoke the duty of self-improvement? In other words, even though you are healthy in any other way the fact that your smoke is so unhealthy (in the eyes of the algorithm and those who developed) that it automatically places you in the high risk group. This classification can annoy some and make them feel like they don't belong — especially smokers who engage in other healthy related behaviors, such as exercising. Yet, such classification can also benefit some and prompt them to stop and think about how unhealthy their smoking behavior actually is. On the bright side, such reflection may motivate them to quit. A pessimist would discourage such utopian ideas and say that punishment actually leads to rebellion. Since smokers now know they are financially penalized for smoking they may actually increase their nicotine consumption out of anger and protest. And we all know that THAT is undesirable. But, is there any other way punishment can be good? What about conscious punishment? Or even the idea of a health tax to improve the life of others and therefore invoke the duty of beneficence?

    Scenario Two: The Extremist
    Here I highlight a few injustices associated with extreme algorithms. The situation remains the same. Smokers pay more for health insurance simply because they engage in smoking behavior. Let me start with a question: How and why is that actually unfair? After all, we know from scientific research that the risk of lung cancer is greatly higher among smokers (see White, 1990 for an example). Well, there are probably a lot of people who, every now and then, smoke but other than that, they are actually quite healthy. For example, they might exercise on a daily basis, eat well, and have good genes. Should they pay more because they smoke, you know, socially or as I wrote before, every now and then? Is smoking behavior really the best proxy we can come up with for deciding someone's health status? Why not examine other healthy related types of data and make a more accurate and fair decision? Isn't that our duty, as citizens, anyway? To be fair and use all the information we have to make a decision? But, the counter-argument here is, well, there are only a few people who smoke and actually engage in healthy behavior. After all, smoking is correlated with drinking and that is also quite bad for you (see Batel et al., 1995; Burton and Tiffany, 1997). On top of that, no algorithm is perfect. Plus, the cost and time to gather all the information is absurd and irrational for the firm, from a business perspective. Why not then follow Simon's bounded rationality approach? Would we really be unethical if doing so? In the end, we know the algorithm has good intentions - smoking is unhealthy and correlated to other unhealthy behavior and its goal is to capture that (i.e., health risk). But, should healthy smokers really pay the same insurance premium, ceteris paribus, as unhealthy smokers? Is that actually fair? What does that say about your business, from a moral perspective? Again, we go back to issues of fairness related to algorithmic extremism.


    Batel, P., Pessione, F., Maıtre, C. and Rueff, B. (1995) Relationship between alcohol and tobacco dependencies among alcoholics who smoke. Addiction, 90, 977-980.

    Burton, S. M. and Tiffany, S. T. (1997) The effect of alcohol consumption on craving to smoke.
    Addiction, 92, 15-26.

    White, T. (1997) Research on smoking and lung cancer: A landmark in the history of chronic disease epidemiology. Yale Journal of Biology and Medicine, 63(1), 29-46.
Cite this page: "Taking a Product to Market" Online Ethics Center for Engineering 11/28/2017 OEC Accessed: Tuesday, September 8, 2020 <>