215: Gathering Data in Machine Learning with Abeba Birhane
December 30th, 2020 · 1 hr 1 min
About this Episode
01:41 - Descartes was wrong: ‘a person is a person through other persons’
13:59 - Predicting How People Behave and Act via Machine Learning is Ethically Flawed
- “Measuring” People
- Simon’s Ant
- Collecting Data
- Confirmation Bias
34:21 - Examining Machine Learning Models and Data
- Means Testing
- Generalized Empathy
“When you get rid of what you don’t want, you do not necessarily get what you do want and you may get something you want a lot less. It is that simple…..anyone that ever watches television knows that!” – Russell L. Ackoff
- “Scoring” People Perpetuates Stereotypes
- Acurracy Confirms Bias
50:09 - Important Ideosyncracies and Contaminating Factors
- Seeing and appreciating the potential to be different in every person in every situation.
- The ability to tease apart existing cultural ideas around identity and humanity.
- Taking concepts from different but related fields and seeing their connectedness and bringing them together into a whole that is more than the sum of their parts.
- Seeing consequences that don’t belong to any one cause.
Mando: Cartesian thinking and worldview is embedded in us.
Avdi: “Contaminating factors.”
“Dive into yourself to find yourself.”
Rein: Jainism has gotten this right for centuries.
- The Elephant and the Blindfolded Men Parable
- The Theory of Conditioned Predication or Contigency
- The Theory of Partial Standpoints
Jessica: Giving the Cartesian program credit for what it’s good for: using science as a way to break things down into parts and studying them deeply; we’ve learned a lot.
Abeba: It’s not all bad. BUT, we forget to put the pieces back together and acknowledge reality.
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