Ethics and open science

Read before class on Wednesday, April 22, 2020


This looks like a lot, but most of these are quite short.

Keep in mind throughout all these readings that an “algorithm” in these contexts is typically some fancy type of regression model where the outcome variable is something binary like “Safe babysitter/unsafe babysitter,” “Gave up seat in past/didn’t give up seat in past”, or “Violated probation in past/didn’t violate probation in past”, and the explanatory variables are hundreds of pieces of data that might predict those outcomes (social media history, flight history, race, etc.).

Data scientists build a (sometimes proprietary and complex) model based on existing data, plug in values for any given new person, multiply that person’s values by the coefficients in the model, and get a final score in the end for how likely someone is to be a safe babysitter or how likely someone is to return to jail.

  1. Miguel A. Hernán, “The c-Word: Scientific Euphemisms Do Not Improve Causal Inference from Observational Data,” American Journal of Publich Health 108, no. 5 (May 2018): 616–19, doi:10.2105/AJPH.2018.304337.↩︎

  2. This concise booklet is the result of DJ Patil’s call for ethics in the previous post.↩︎