Rousseau once remarked that “It is, therefore, very certain that compassion is a natural sentiment, which, by moderating the activity of self-esteem in each individual, contributes to the mutual preservation of the whole species” (Discourses on Inequality). Indeed, it is compassion, and not “reason” that keeps this frail species progressing. Yet, this ability to be compassionate, which is by its very nature an other-regarding ability, is (ironically) the different side to the same coin: comparison. Comparison, or perhaps “reflection on certain relations” (e.g. small/big; hard/soft; fast/slow; scared/bold), also has the different and degenerative features of pride and envy. These twin vices, for Rousseau, are the root of much of the evils in this world. They are tempered by compassion, but they engender the greatest forms of inequality and injustice in this world.
Rousseau’s insights ought to ring true in our ears today, particularly as we attempt to create artificial intelligences to overtake or mediate many of our social relations. Recent attention given to “algorithm bias,” where the algorithm for a given task draws from either biased assumptions or biased training data yielding discriminatory results, I would argue is working the problem of reducing bias from the wrong direction. Many, the White House included, are presently paying much attention about how to eliminate algorithmic bias, or in some instance to solve the “value alignment problem,” thereby indirectly eliminating it. Why does this matter? Allow me a brief technological interlude on machine learning and AI to illustrate why eliminating this bias (a la Rousseau) is impossible.