Values in design and mathematical impossibility
by Sebastian Benthall
Under pressure from the public and no doubt with sincere interest in the topic, computer scientists have taken up the difficulty task of translating commonly held values into the mathematical forms that can be used for technical design. Commonly, what these researches discover is some form of mathematical impossibility of achieving a number of desirable goals at the same time. This work has demonstrated the impossibility of having a classifier that is fair with respect to a social category without data about that very category (Dwork et al., 2012), having a fair classifier that is both statistically well calibrated for the prediction of properties of persons and equalizing the false positive and false negative rates of partitions of that population (Kleinberg et al., 2016), of preserving privacy of individuals after an arbitrary number of queries to a database, however obscured (Dwork, 2008), or of a coherent notion of proxy variable use in privacy and fairness applications that is based on program semantics (as opposed to syntax) (Datta et al., 2017).
These are important results. An important thing about them is that they transcend the narrow discipline in which they originated. As mathematical theorems, they will be true whether or not they are implemented on machines or in human behavior. Therefore, these theorems have a role comparable to other core mathematical theorems in social science, such as Arrow’s Impossibility Theorem (Arrow, 1950), a theorem about the impossibility of having a voting system with reasonable desiderata for determining social welfare.
There can be no question of the significance of this kind of work. It was significant a hundred years ago. It is perhaps of even more immediate, practical importance when so much public infrastructure is computational. For what computation is is automation of mathematics, full stop.
There are some scholars, even some ethicists, for whom this is an unwelcome idea. I have been recently told by one ethics professor that to try to mathematize core concepts in ethics is to commit a “category mistake”. This is refuted by the clearly productive attempts to do this, some of which I’ve cited above. This belief that scientists and mathematicians are on a different plane than ethicists is quite old: Hannah Arendt argued that scientists should not be trusted because their mathematical language prevented them from engaging in normal political and ethical discourse (Arendt, 1959). But once again, this recent literature (as well as much older literature in such fields as theoretical economics) demonstrates that this view is incorrect.
There are many possible explanations for the persistence of the view that mathematics and the hard sciences do not concern themselves with ethics, are somehow lacking in ethical education, or that engineers require non-technical people to tell them how to engineer things more ethically.
One reason is that the sciences are much broader in scope than the ethical results mentioned here. It is indeed possible to get a specialist’s education in a technical field without much ethical training, even in the mathematical ethics results mentioned above.
Another reason is that whereas understanding the mathematical tradeoffs inherent in certain kinds of design is an important part of ethics, it can be argued by others that what’s most important about ethics is some substantive commitment that cannot be mathematically defended. For example, suppose half the population believes that it is most ethical for members of the other half to treat them with special dignity and consideration, at the expense of the other half. It may be difficult to arrive at this conclusion from mathematics alone, but this group may advocate for special treatment out of ethical consideration nonetheless.
These two reasons are similar. The first states that mathematics includes many things that are not ethics. The second states that ethics potentially (and certainly in the minds of some people) includes much that is not mathematical.
I want to bring up a third reason, which is perhaps more profound than the other two, which is this: what distinguishes mathematics as a field is its commitment to logical non-contradiction, which means that it is able to baldly claim when goals are impossible to achieve. Acknowledging tradeoffs is part of what mathematicians and scientists do.
Acknowledging tradeoffs is not something that everybody else is trained to do, and indeed many philosophers are apparently motivated by the ability to surpass limitations. Alain Badiou, who is one of the living philosophers that I find to be most inspiring and correct, maintains that mathematics is the science of pure Being, of all possibilities. Reality is just a subset of these possibilities, and much of Badiou’s philosophy is dedicated to the Event, those points where the logical constraints of our current worldview are defeated and new possibilities open up.
This is inspirational work, but it contradicts what many mathematicians do in fact, which is identify impossibility. Science forecloses possibilities where a poet may see infinite potential.
Other ethicists, especially existentialist ethicists, see the limitation and expansion of possibility, especially in the possibility of personal accomplishment, as fundamental to ethics. This work is inspiring precisely because it states so clearly what it is we hope for and aspire to.
What mathematical ethics often tells us is that these hopes are fruitless. The desiderata cannot be met. Somebody will always get the short stick. Engineers, unable to triumph against mathematics, will always disappoint somebody, and whoever that somebody is can always argue that the engineers have neglected ethics, and demand justice.
There may be good reasons for making everybody believe that they are qualified to comment on the subject of ethics. Indeed, in a sense everybody is required to act ethically even when they are not ethicists. But the preceding argument suggests that perhaps mathematical education is an essential part of ethical education, because without it one can have unrealistic expectations of the ethics of others. This is a scary thought because mathematics education is so often so poor. We live today, as we have lived before, in a culture with great mathophobia (Papert, 1980) and this mathophobia is perpetuated by those who try to equate mathematical training with immorality.
Arendt, Hannah. The human condition:[a study of the central dilemmas facing modern man]. Doubleday, 1959.
Arrow, Kenneth J. “A difficulty in the concept of social welfare.” Journal of political economy 58.4 (1950): 328-346.
Benthall, Sebastian. “Philosophy of computational social science.” Cosmos and History: The Journal of Natural and Social Philosophy 12.2 (2016): 13-30.
Datta, Anupam, et al. “Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs.” arXiv preprint arXiv:1705.07807 (2017).
Dwork, Cynthia. “Differential privacy: A survey of results.” International Conference on Theory and Applications of Models of Computation. Springer, Berlin, Heidelberg, 2008.
Dwork, Cynthia, et al. “Fairness through awareness.” Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. ACM, 2012.
Kleinberg, Jon, Sendhil Mullainathan, and Manish Raghavan. “Inherent trade-offs in the fair determination of risk scores.” arXiv preprint arXiv:1609.05807 (2016).
Papert, Seymour. Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc., 1980.