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The undesirable deviations and 90th percentile minimax metrics also use a subset S , however, these scenarios do not cover all regions of this S and are therefore less balanced. The undesirable deviations metric considers regret from the median for scenarios for which values of f ( x i ,  S ) are less than the median, resulting in robustness values that have a higher level of intrinsic risk aversion than those obtained using metrics that used information from all regions of the distribution (Table buy cheap real Rieker L9041 Women’s LowTop Sneakers Blue Staub/Azur/Azur/Denim / 42 sneakernews sale online lowest price online clearance with mastercard Z6fi4lg4
). The 90th percentile minimax regret metric corresponds to an even greater level of intrinsic risk aversion, as it is based on a single value that is close to the worst case (90th percentile—see Table 3 ).

A categorization of different robustness metrics in accordance with the final robustness metric calculation ( T 3 ) is given in Table 4 . As can be seen, for some metrics, such as the maximin, maximax, minimax regret and 90th percentile minimax regret metrics, f ( x i ,  S ) and R ( x i ,  S ) are identical (i.e., the robustness metric calculation corresponds to the identity transformation). This is because for these metrics, S consists of a single scenario and there is no need to combine a number of values in order to arrive at a single value of robustness. However, for the remaining metrics, for which S contains at least two values, some sort of transformation is required. Metrics that are based on the mean or sum of f ( x i ,  S ), such as Laplace's principle of insufficient reason, mean‐variance and undesirable deviations, effectively assign an equal weighting to different scenarios and then suggest that the best decision is the one with the best mean performance, producing an expected value of performance. In contrast, in Hurwicz's optimism‐pessimism rule, the user can select the relative weighting of the two scenarios (low and high levels of risk aversion) considered, as mentioned in Section discount codes shopping online Reebok Mens CL Leather Grey Fashion Sneaker Collegiate Burgundy/Cloud Grey/Alloy/Teal Green 125 M US outlet best prices t3EXbH7zWO

Alternatively, some metrics consider aspects of the variability of f ( x i ,  S ). For example, the mean‐variance metric attempts to balance the mean and variability of the performance of a decision alternative over different scenarios. However, a disadvantage of considering a combination of the mean and variance is that the resultant metric is not always monotonically increasing (Ray et al., New Balance 486981 60 Men’s Trainers Multicoloured buy cheap 100% authentic IBQvusB
). Moreover, when considering variance, good and bad deviations from the mean are treated equally (Takriti Ahmed, FRYE womens Sabrina Chelsea Chestnut clearance get authentic excellent cheap price clearance top quality sale manchester great sale glXlBHe7
). The undesirable deviations metric overcomes this limitation, while still providing a measure of variability. Other metrics are focused on different attributes of f ( x i ,  S ), such as the skewness and kurtosis.

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Ryan Brandt thought his criminal history was behind him. A judge in 2005 allowed him to expunge his record for a 1995 battery conviction.

But when he recently applied to work as a manager of an Arby's restaurant in Mariniette, Wis., he claims in a federal lawsuit, he was denied the job because his expunged record came up on a criminal background check.

Brandt eventually settled for lower paying job. "I was trying to get a better job and move up. I thought it was all behind me," he said of his conviction. "That basically cost me a job."

At least 40 states give people like Brandt the right to effectively erase their criminal past by sealing or expunging some, usually minor, prior criminal convictions.

But massive private electronic databases of criminal records, which are not always up to date, make some of those supposedly secret records widely available to employers and landlords.

Public court records, once protected by what the Supreme Court called the "practical obscurity" of the nation's decentralized justice system, are now digitized and sold to private companies. Some of those companies compile them into databases of millions of public records.

According to government reports and consumer lawsuits, those databases often contain inaccuracies, reporting criminal convictions for people who have never been arrested and records that should have been expunged.

Criminal justice experts say it is common for people to lose jobs because of those inaccuracies. "We're building a system that creates a scarlet letter that can never be removed," said Robert Sykora of the Minnesota Board of Public Defense, which oversees the state's public defenders.

There are no comprehensive statistics on how often the reports turn up inaccurate information. But a review of court records by ABC News found dozens of lawsuits, on behalf of hundreds of people, filed in the last two years against the major criminal records database companies, alleging that background checks contain inaccurate information about criminal convictions.

"This is obviously an issue that's becoming more and more significant as it becomes easier to get and exchange data," said Chichi Wu, a senior attorney with the National Consumer Law Center.

In a statement, LexisNexis, the company that conducted Brandt's background check, said that the Fair Credit Reporting Act, the federal law that governs consumer reporting agencies, sets out a "detailed framework" for giving consumers access to their background checks and allowing them to correct inaccuracies.

"LexisNexis follows the framework and requirements set forth by the FCRA," the company said.

Arthur Cohen, the former chairman of the National Association of Professional Background Screeners, said mistakes are uncommon. "If mistakes are made, they need to be corrected," he said. "I don't think this is a rampant problem."

LexisNexis fixed Bradnt's report after he complained, he said. The company also fixed a background check for Daniel Johnson, a retired police officer in Slidell, La., after it allegedly turned up a conviction for a different Daniel Johnson.

But, Johnson said, the correction came only after he had lost a job as a security guard at a grocery store, which he was using to make extra money after his house flooded during Hurricane Katrina.

"It was very embarrassing. I had a clean career with the police department for 25 years," he said. "I never even had as much as a speeding ticket."

There has been a surge in interest in criminal background checks in the last 15 years. Private companies conduct millions of such checks a year. By comparison, the number of lawsuits claiming inaccurate information in the reports is relatively small.

Donna Uzell, the chair of the National Crime Prevention and Privacy Compact Council, which sets policy on the use of criminal records for non-law enforcement purposes, said private background screening companies provide a valuable service for employers and landlords.

"People who are doing background checks are doing so for a reason, for the safety of their constituency or out of concern about potential liability they may incur," she said.

But lawyers and advocates say those companies are not doing enough. The information contained in the reports is generally based on searches of names and other data such as dates of birth, but not on fingerprints.

"These reports are being used by all types of social organizations, the NCAA, little leagues, boy scouts, churches," said Leonard Bennett, an attorney who has filed several lawsuits against criminal records companies. "The impact can be devastating."

Background check companies are regulated by Fair Credit Reporting Act, which sets up a process to correct inaccuracies.

Cohen said that fear of identity theft has led many courthouses to redact personal information, such as social security numbers, from court records, making it more difficulty to conduct checks.

Frank Campbell, a former Deputy Assistant Attorney General in the Department of Justice's Office of Legal Policy, who worked on background screening policy, said employers are not supposed to take adverse action against potential employees until the background check is correct.

"As a practical matter, it doesn't really work that way in the real world," he said.

Campbell said in a 2006 report that the government in some cases should allow access to records in the more comprehensive FBI database, which are based on fingerprint identification.

That may have helped Raymond Phillippi, who said he has not been able to get consistent work in more than a year, since he was rejected for a position as a machinist after a background check turned up a false felony conviction for burglary.

"It's been tough," he said. "I wouldn't hire a felon either. But they should make sure they get their information straight."

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This all sounds ludicrous until we realise that our algorithms are increasingly being made in our own image. As we’ve learned more about our own brains, we’ve enlisted that knowledge to create algorithmic versions of ourselves. These algorithms control the speeds of driverless cars, identify targets for autonomous military drones, compute our susceptibility to commercial and political advertising, find our soulmates in online dating services, and evaluate our insurance and credit risks. Algorithms are becoming the near-sentient backdrop of our lives.

The most popular algorithms currently being put into the workforce are deep learning algorithms. These algorithms mirror the architecture of human brains by building complex representations of information. They learn to understand environments by experiencing them, identify what seems to matter, and figure out what predicts what. Being like our brains, these algorithms are increasingly at risk of mental-health problems.

Deep Blue, the algorithm that beat the world chess champion Garry Kasparov in 1997, did so through brute force, examining millions of positions a second, up to 20 moves in the future. Anyone could understand how it worked even if they couldn’t do it themselves. AlphaGo, the deep learning algorithm that beat Lee Sedol at the game of Go in 2016, is fundamentally different. Using deep neural networks, it created its own understanding of the game, considered to be the most complex of board games. AlphaGo learned by watching others and by playing itself. Computer scientists and Go players alike are befuddled by AlphaGo’s unorthodox play. Its strategy seems at first to be awkward. Only in retrospect do we understand what AlphaGo was thinking, and even then it’s not all that clear.

To give you a better understanding of what I mean by thinking, consider this. Programs such as Deep Blue can have a bug in their programming. They can crash from memory overload. They can enter a state of paralysis due to a neverending loop or simply spit out the wrong answer on a lookup table. But all of these problems are solvable by a programmer with access to the source code, the code in which the algorithm was written.

Algorithms such as AlphaGo are entirely different. Their problems are not apparent by looking at their source code. They are embedded in the way that they represent information. That representation is an ever-changing high-dimensional space, much like walking around in a dream. Solving problems there requires nothing less than a psychotherapist for algorithms.

Take the case of driverless cars. A driverless car that sees its first stop sign in the real world will have already seen millions of stop signs during training, when it built up its mental representation of what a stop sign is. Under various light conditions, in good weather and bad, with and without bullet holes, the stop signs it was exposed to contain a bewildering variety of information. Under most normal conditions, the driverless car will recognise a stop sign for what it is. But not all conditions are normal. Some recent demonstrations have shown that a few black stickers on a stop sign can fool the algorithm into thinking that the stop sign is a 60 mph sign. Subjected to something frighteningly similar to the high-contrast shade of a tree, the algorithm hallucinates.

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