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Inclou aquests noms: Cathy O'Neil, 凱西.歐尼爾

Crèdit de la imatge: Source : Author's photo available from https://www.mathbabe.org

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De skjulte algoritmer : teknoantropologiske perspektiver (2018) — Autor, algunes edicions1 exemplars, 1 ressenya

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This review is based on the Blinkist version of the book...thus a summary and my review needs to be qualified as such. Presumably the original full text has much more details and research.....but it also takes much longer to read. Actually, I’ve already read the full book and written a review of it so this is my chance to do a serious comparison of the Blinkist summary vs reading the full book. Meanwhile, here are some snippets that caught my attention:
Research has shown that social media and search engines are especially vulnerable to algorithms that can influence the decisions of unsuspecting users....Researchers Robert Epstein and Ronald Robertson found proof of this after asking undecided voters in the United States and India to find information about a handful of different political candidates.
The catch was that the voters were told to use a specific search engine, unaware that it had been programmed with an algorithm that favoured one candidate over all the others. As a result, the participants showed a 20-percent shift toward voting for the algorithm's preferred choice......Likewise, Facebook surveyed the participants before and after the elections, and the results showed that 3 percent more users turned out to vote than was expected before the algorithm had been adjusted to favour politics.
It is clear that there is vast potential for abuse.....Once people with similar data are grouped together, analysts can create algorithms that made sure these groups received specifics ads that would appeal to their tastes.......So those who showed evidence of having environmental concerns, for instance, were targeted for ads that highlighted Obama’s environmental policies.
Algorithms designed to predict crime also reinforce prejudices.The algorithms rely on historical data to pinpoint where crimes are most likely to occur, and it’s the police who determine which data is fed into the algorithm.....Part of the problem is that the police tend to focus on specific kinds of crimes, such as “nuisance crimes,” which include vagrancy and certain drug-related offenses. Given that crimes like these tend to occur in poor neighbourhoods, the analysis will end up being completely skewed toward these parts of the city.....This also leads to neglect of wealthier neighbourhoods, which become more vulnerable to criminal activity.....In 2009, the Chicago Police Department received a grant to develop new crime-prediction software. They used that money to develop an algorithm that came up with a list of the 400 people most likely to be involved in a homicide....including poor McDaniel who was never charged with any crime but he ended up being red-flagged by the algorithm solely based on the people he follows on social networks and the criminals who happen to live in his neighbourhood.....In short, growing up in a poor neighbourhood is all it takes to get you labelled as potentially dangerous.
For car insurance, algorithms are used to calculate payment amounts based on how many prior accidents a customer has been in as well as their prior credit reports.....This leads to poor drivers with impeccable driving skills having to pay more for insurance than rich drivers.
Some insurance companies are even using algorithms to calculate the likelihood that a customer will shop around for cheaper prices.....if they do then they are offered big discounts. However, if a customer isn’t likely to shop around, his rate can increase by as much as 800 percent......But what Allstate’s algorithm is really doing is taking advantage of poor people without formal education, since this is the demographic that is less likely to shop around for other options.
The job market is also being unfairly influenced by algorithms.....tests have proven to be restrictive for certain kinds of people, especially when it comes to personality tests. Part of the problem is that the companies handling the data can make some troubling mistakes.
When Catherine Taylor applied for a job with the Red Cross in Arkansas, she was rejected and told that it was due to her criminal charge for intent to manufacture and sell methamphetamine. This seemed odd to Catherine since she had a pretty clean record...When she investigated further, she found that those charges belonged to another Catherine Taylor who happened to have the same birthday....
University rankings have negative effects on higher education....It’s no secret that colleges in the United States have gotten quite expensive over the past 30 years, but few people know that one of the main reasons for the increase in tuition is due to one newspaper. In the 1980s, US News and World Report began using an algorithm that ranked the quality of US colleges using data that they believed would determine their success, such as SAT scores and acceptance rates......Suddenly, these ranking became crucially important for all the universities involved.....This scramble for money is largely responsible for tuition going through the roof. Between 1985 and 2013, the cost of higher education increased by 500 percent.
But since US News gave schools with a lower acceptance rate [ie tougher to get into] a better position in the rankings, many schools began lowering their rates and sending out fewer acceptance letters thus rejecting potentially good candidates....even if only some of these high-performing students chose to attend, it would have benefitted the school. Also, the decision to reject high performers out of hand ruined the backup plans of many good students. [They were being rejected because the second tier universities assumed they would never attend anyway but would go to Yale and Harvard] etc.,
Like all the other algorithms we looked at, what started out as a good idea ended up doing far more harm than good..
The key message in this book: Algorithms were initially created to be neutral and fair by avoiding all-too-human biases and faulty logic. However, many of the algorithms used today, from the insurance market to the justice system, have incorporated the very prejudices and misconceptions of their designers. And since these algorithms operate on a massive scale, these biases lead to millions of unfair decisions.
A useful take-away: Write machine-friendly resumes. Most companies today use automatic resume readers. To increase your chances of getting the job, modify your resume with the automatic reader in mind. Here are some simple tips you can always apply: Use simple fonts like Arial and Courier Stay away from images, which can’t be processed by the reader Don’t use symbols–even simple ones like arrows can confuse the reader
What’s my take on the book and how does the Blinkist version differ from the experience of reading the original? Actually, The Blinkist version does astoundingly well. Covered most of the main issues. However, I don’t think it picked up on the overall conclusions very well. They were along the lines of: So are we going to sacrifice the accuracy of the model for fairness (where a few individuals get badly and incorrectly, affected? O'Neil say yes. In some cases we do need to dumb down our algorithms: if we are going to be treated equally before the law or be equally treated as voters. Many of these algorithms cannot deliver justice or democracy.
There are now some moves afoot to audit the algorithms that are being used...and this is really needed.
Four stars from me...she clearly knows what she is writing about.
… (més)
 
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booktsunami | Hi ha 96 ressenyes més | Jul 11, 2024 |
Audiobook. Meh. Other books have done it better.
 
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kylecarroll | Hi ha 96 ressenyes més | Jun 20, 2024 |
A powerful explanation of how algorithms and statistical models are used in various aspects of financial and public life in the United States, although some of them have the potential to cause a lot of harm. I don't think there was much in this book that I hadn't read about before, but it was well written and served as a useful reminder.
 
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mari_reads | Hi ha 96 ressenyes més | May 10, 2024 |
Important subject, but not as deep or engaging as I was hoping for. I found myself skimming chapters more often than I wanted to. It feels like a New Yorker column that got fleshed out to book length - good, but not gripping.
 
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patl | Hi ha 96 ressenyes més | Feb 29, 2024 |

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