Bias-variance tradeoff
https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff
Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction
Francesco Gagliardi
http://www.aiimjournal.com/article/S0933-3657(11)00034-0/abstract
keyword: Bias-variance dilemma
Can Computer Programs Be Racist And Sexist?
September 26, 2016
http://www.npr.org/sections/alltechconsidered/2016/03/15/470422089/can-computer-programs-be-racist-and-sexist
“The systems are of a sufficient complexity that it is possible to say the algorithm did it,” Christian Sandvig* says. “And it’s actually true — the algorithm is sufficiently complicated, and it’s changing in real time. It’s writing its own rules on the basis of data and input that it does do things and we’re often surprised by them.”
*a professor at the University of Michigan’s School of Information
What Makes Algorithms Go Awry?
June 07, 2015
http://www.npr.org/sections/alltechconsidered/2015/06/07/412481743/what-makes-algorithms-go-awry
algorithms, like humans, can make mistakes.
Last month, users found the photo-sharing site Flickr’s new image-recognition technology was labeling dark-skinned people as “apes”
How to limit human bias in computer programs
We can test it under many different scenarios. We can look at the results and see if there’s discrimination patterns. In the same way that we try to judge decision-making in many fields, when the decision making is done by humans, we should apply a similar critical lens — but with a computational bent to it, too.
The fear I have is that every time this is talked about, people talk about it as if it’s math or physics, therefore some natural, neutral world. And they’re programs! They’re complex programs.
They’re not like laws of physics or laws of nature. They’re created by us. We should look into what they do and not let them do everything. We should make those decisions explicitly.
related:
May 14, 2012
https://franzcalvo.wordpress.com/2015/03/09/algorithms-extending-the-power-of-the-human-mind
Dec 20, 2012
https://franzcalvo.wordpress.com/2013/12/25/big-data-has-spawned-a-cult-of-infallibility
How Twitter’s Trending Algorithm Picks Its Topics
December 07, 2011
http://www.npr.org/2011/12/07/143013503/how-twitters-trending-algorithm-picks-its-topics
Sometimes a topic that seems hot doesn’t trend, leading some to charge Twitter with censorship. But the complex algorithms that determine trending topics are intended to find what’s trending in the moment, and not what’s been around for a long time.
“It’s a curated list,” Gillespie says. “It’s a list that will never show us if something that they or their publishers had classified as adult would ever show up there.”
Everything from restaurant reviews to your friend’s baby pictures to your local news is getting served up to you by an algorithm. As much as programmers may think their algorithms will deliver objective results, those calculations may be just as biased as a real human being.
related:
https://franzcalvo.wordpress.com/2015/01/20/emergent-weirdness-in-ai
July 9, 2014
https://franzcalvo.wordpress.com/2015/07/14/in-google-newsroom
http://www.npr.org/2011/04/22/135625124/google-tweak-s-algorithm-to-spot-content-farms
Daniel Kahneman on ‘Emergent Weirdness’ in Artifical Intelligences
Nov 28 2011
http://www.theatlantic.com/technology/archive/2011/11/daniel-kahneman-on-emergent-weirdness-in-artifical-intelligences/249125
Emergent weirdness is a good bet.
Only deduction is certain.
Whenever an inductive short-cut is applied, you can search for cases in which it will fail.
It is always useful to ask “What relevant factors are not considered?” and “What irrelevant factors affect the conclusions?”
By their very nature, heuristic shortcuts will produce biases, and that is true for both humans and artificial intelligence, but the heuristics of AI are not necessarily the human ones.
heuristics
related:
December 7, 2011
https://franzcalvo.wordpress.com/2015/03/09/twitters-trending-algorithm
http://www.nytimes.com/2015/07/10/upshot/when-algorithms-discriminate.html
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