How Algorithms Can Fight Bias Instead of Entrench It

Essay by Tobias Baer: “…How can we build algorithms that correct for biased data and that live up to the promise of equitable decision-making?

When we consider changing an algorithm to eliminate bias, it is helpful to distinguish what we can change at three different levels (from least to most technical): the decision algorithm, formula inputs, and the formula itself.

In discussing the levels, I will use a fictional example, involving Martians and Zeta Reticulans. I do this because picking a real-life example would, in fact, be stereotyping—I would perpetuate the very biases I try to fight by reiterating a simplified version of the world, and every time I state that a particular group of people is disadvantaged, I also can negatively affect the self-perception of people who consider themselves members of these groups. I do apologize if I unintentionally insult any Martians reading this article!

On the simplest and least technical level, we would adjust only the overall decision algorithm that takes one or more statistical formulas (typically to predict unknown outcomes such as academic success, recidivation, or marital bliss) as an input and applies rules to translate the predictions of these formulas into decisions (e.g., by comparing predictions with externally chosen cutoff values or contextually picking one prediction over another). Such rules can be adjusted without touching the statistical formulas themselves.

An example of such an intervention is called boxing. Imagine you have a score of astrological ability. The astrological ability score is a key criterion for shortlisting candidates for the Interplanetary Economic Forecasting Institute. You would have no objective reason to believe that Martians are any less apt at prognosticating white noise than Zeta Reticulans; however, due to racial prejudice in our galaxy, Martian children tend to get asked a lot less for their opinion and therefore have a lot less practice in gabbing than Zeta Reticulans, and as a result only one percent of Martian applicants achieve the minimum score required to be hired for the Interplanetary Economic Forecasting Institute as compared to three percent of Zeta Reticulans.

Boxing would posit that for hiring decisions to be neutral of race, for each race two percent of applicants should be eligible, and boxing would achieve it by calibrating different cut-off scores (i.e., different implied probabilities of astrological success) for Martians and Zeta Reticulans.

Another example of a level-one adjustment would be to use multiple rank-ordering scores and to admit everyone who achieves a high score on any one of them. This approach is particularly well suited if you have different methods of assessment at your disposal, but each method implies a particular bias against one or more subsegments. An example for a crude version of this approach is admissions to medical school in Germany, where routes include college grades, a qualitative assessment through an interview, and a waitlist….(More)”.