I still give the book Understanding Deep Learning by Simon J.D. Prince a good recommendation, but chapter 21: Deep learning and Ethics was sloppy. It could have been a chapter to really dig in on case studies, but it was just the basic public news story level coverage of bias and such, like:
“In AI, it can be pernicious when this deviation depends on illegitimate factors that impact an output. For example, gender is irrelevant to job performance, so it is illegitimate to use gender as a basis for hiring a candidate. Similarly, race is irrelevant to criminality, so it is illegitimate to use race as a feature for recidivism prediction.”
If they had stuck with “illegitimate”, then it would have been a question of societal choices, but “irrelevant” is a question about data, and your priors shouldn’t be so strong that data can’t move them.
I would like to see a book or course walk through a machine learning problem with the input features being presented as something like car choices: color, style, doors, horsepower, etc. Do lots of analysis over representation, training, and generalization, then swap the feature labels to socially charged ones.
What makes generalization credible in one situation but not the other?
ESTE DEBERÍA SER EL ESCANDALO DEL DÍA Y EL TEMA DE CONVERSACIÓN PRINCIPAL EN TODOS LOS MEDIOS, NO LOS BILLBOARDS ANÓNIMOS...
AHH VERDAD, QUE LA MAYORÍA DE LOS MEDIOS SON CONTROLADOS POR EL PNP.
estoy tratando de no tomarlo muy enserio pero la realidad de que probablemente muchxs de nostrxs nos tengamos que ir de PR en un futuro porque el gobierno está destruyendo todo it’s so fucking sad