Anyway all I'm claiming is that statistical methods are much more accurate than humans. Nothing there disputes that claim. If you want the most accurate predictions possible, you should use an algorithm.
That article implies that humans are somehow fair or unbiased. That is a completely ridiculous claim that has been proven false many times. Human judges give ugly people twice the sentences of attractive people. Judges have been shown to give significantly harsher sentences just before lunch, when they are hungry. Not to mention all the classic biases against gender/race/political affiliation/etc. Studies have shown interviews are worse than useless at assessing how good someone will be as an employee. Instead employers are biased by how much they like the candidate. We should hardly expect traditional parole interviews to be any different.
But almost no one cares about these results. Yet when an algorithm is shown to have a (not statistically significant) bias, people freak out. This, if anything, proves my point that algorithm aversion is a serious problem.
While there are respectable ML folks making those criticisms, the commentary I've read seems more click-bait than science.
ML can be used just as traditional statistics to make causal inferences and predict the effect of intervention. There's nothing about ML that reinforces status quo more than traditional statistics, let alone case studies (aka anecdotes) or "common sense."
Google had an interesting take on how you could control for some dimensions of bias here https://research.google.com/bigpicture/attacking-discriminat...
Yes but that is not how ML is marketed. It's marketed as way better than traditional statistics, and soon even better than humans.
But reality is that a trained system is only as good as the data used to train it.
But ML can improve upon other methods of interpreting that (biased) data. Thus, in some ways better than traditional statistics and non-mathematical human intuition.
It would be nice if you could provide sources for these claims.
https://www.scientificamerican.com/article/lunchtime-lenienc...
http://www.villagevoice.com/news/study-ugly-people-more-like...
(To be fair, the study size was small).
There's a study on attractiveness and juror bias here (it's more complicated than just "ugly people get worse sentences" but some bias does show up for certain juror personality types): http://onlinelibrary.wiley.com/doi/10.1002/bsl.939/abstract
Reference: Flores, Bechtel, Lowencamp; "False Positives, False Negatives, and False Analyses: A Rejoinder to “Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And it’s Biased Against Blacks.”", Federal Probation Journal, September 2016, You can find the article here: http://www.uscourts.gov/statistics-reports/publications/fede...
In fact the ProPublica analysis (written by journalists,not scientists) was so wrong that the authors of the study wrote:
"It is noteworthy that the ProPublica code of ethics advises investigative journalists that "when in doubt, ask" numerous times. We feel that Larson et al.'s (2016) omissions and mistakes could have been avoided had they just asked. Perhaps they might have even asked...a criminologist? We certainly respect the mission of ProPublica, which is to "practice and promote investigative journalism in the public interest." However, we also feel that the journalists at ProPublica strayed from their own code of ethics in that they did not present the facts accurately, their presentation of the existing literature was incomplete, and they failed to "ask." While we aren’t inferring that they had an agenda in writing their story, we believe that they are better equipped to report the research news, rather than attempt to make the research news."
Ouch...