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This is like machine introduced bias/racisim/castism... we need a new term for that.. and its based on statistically induced pseudo-sciences many times similar

This is like machine introduced bias/racisim/castism... we need a new term for that.. and its based on statistically induced pseudo-sciences many times similar to astrology. This is the kind of AI everyone should be afraid of.

benkuykendall 10y ago
I fail to see what is unscientific about stating conditional probabilities. Astrology is unscientific because the orientation of the heavens is for the most part independent of the observables of a person's life. But the inputs to Berk's system clearly do effect the probability of committing crime. A frequentist would say that a large group of people with this set of characteristics would yield more or fewer criminals; a Bayesian would say we have more knowledge about whether such an individual will commit future crime. These are scientific conclusions. The question "how should we use this data" is a question of ethics, not science.
- eyelidlessness 10y ago
  > I fail to see what is unscientific about stating conditional probabilities. 1. Create conditions which disadvantage and impoverish a segment of society. 2. Refine those conditions for centuries, continually criminalizing the margins that segment of society is forced to live in. 3. Identify that many of the people in that segment of society are likely to be identified as criminals. 4. Pretend that you're doing math rather than reinforcing generations of deleterious conditioning, completely ignoring the creation of those conditions that led to the probabilities you're identifying. And science can't be divorced from ethics. These are human pursuits.
  - vintermann 10y ago
    This looks like sloppy ML. But human judges do all those things already (susbtitute "just applying the law" for "doing math" in 4) and they can't be inspected - their brains are "closed source". Sure, these humans can come up with wordy justifications for their decisions. But there are plenty of intelligent people who employ their intelligence not to arrive at a conclusion, but to justify the conclusion they already arrived at. Legal professionals aren't merely capable of this, they're explicitly trained to be experts at post-hoc justification. And legal professionals basically ignore all criticism not coming from their own professional class. They are rarely taught any kind of statistics in law school. Nobody wants to discuss math in debate club - answers with hard right and wrong answers are no fun to them. Your pessimism wrt. modeling may be justified, but you're not nearly pessimistic enough about people or the legal system.
    - eyelidlessness 10y ago
      I frankly don't understand your response. I described a list of despicable things humans have done, and you're suggesting that I'm not pessimistic about people.
- anupshinde 10y ago
  I see statistics sometimes edges near astrological conclusions. Because sometimes there are many assumptions being made to simplify those equations. Machine learning just amplifies that assumptive behavior unless you have all the possible test-train-data. Predictions from astrology are just probabilistic - lets say the heavenly object positions are metadata. But then people started treating the results as absolute. Now you know why people dislike astrology. Its not okay to assign risk-score because humans tend to show bias given a quantifiable number. And thats just plain bad.
praccu 10y ago
http://www.fatml.org/ http://www.fatml.org/

benkuykendall 10y ago

I fail to see what is unscientific about stating conditional probabilities. Astrology is unscientific because the orientation of the heavens is for the most part independent of the observables of a person's life. But the inputs to Berk's system clearly do effect the probability of committing crime. A frequentist would say that a large group of people with this set of characteristics would yield more or fewer criminals; a Bayesian would say we have more knowledge about whether such an individual will commit future crime. These are scientific conclusions. The question "how should we use this data" is a question of ethics, not science.

eyelidlessness 10y ago
> I fail to see what is unscientific about stating conditional probabilities. 1. Create conditions which disadvantage and impoverish a segment of society. 2. Refine those conditions for centuries, continually criminalizing the margins that segment of society is forced to live in. 3. Identify that many of the people in that segment of society are likely to be identified as criminals. 4. Pretend that you're doing math rather than reinforcing generations of deleterious conditioning, completely ignoring the creation of those conditions that led to the probabilities you're identifying. And science can't be divorced from ethics. These are human pursuits.
- vintermann 10y ago
  This looks like sloppy ML. But human judges do all those things already (susbtitute "just applying the law" for "doing math" in 4) and they can't be inspected - their brains are "closed source". Sure, these humans can come up with wordy justifications for their decisions. But there are plenty of intelligent people who employ their intelligence not to arrive at a conclusion, but to justify the conclusion they already arrived at. Legal professionals aren't merely capable of this, they're explicitly trained to be experts at post-hoc justification. And legal professionals basically ignore all criticism not coming from their own professional class. They are rarely taught any kind of statistics in law school. Nobody wants to discuss math in debate club - answers with hard right and wrong answers are no fun to them. Your pessimism wrt. modeling may be justified, but you're not nearly pessimistic enough about people or the legal system.
  - eyelidlessness 10y ago
    I frankly don't understand your response. I described a list of despicable things humans have done, and you're suggesting that I'm not pessimistic about people.
anupshinde 10y ago
I see statistics sometimes edges near astrological conclusions. Because sometimes there are many assumptions being made to simplify those equations. Machine learning just amplifies that assumptive behavior unless you have all the possible test-train-data. Predictions from astrology are just probabilistic - lets say the heavenly object positions are metadata. But then people started treating the results as absolute. Now you know why people dislike astrology. Its not okay to assign risk-score because humans tend to show bias given a quantifiable number. And thats just plain bad.

praccu 10y ago

http://www.fatml.org/ http://www.fatml.org/