January 10, 2019 at 6:12 pm
Eric M Russell - Thursday, January 10, 2019 1:01 PMskeleton567 - Thursday, January 10, 2019 11:00 AMOne case that hits all of us is in insurance risk calculations. Even though you have not had a claim in years or decades, your premium is going to be based on claim history in your area, your age group, etc. I relocated from a rural area to a metropolis, and my premium increased five times in the first six renewals, even though there still have been no claims, because I am now perceived as being a greater risk due to location instead of events. The bias is evident in the selection of criteria on which decisions are made.Obviously this can also be a good thing, for instance, when you doctor asks you about any family history of heart attack, stroke, cancer. But do you want your health care decisions based on how well you distant cousins pay their medical bills?
The chances are significantly greater in a metropolitan area that another driver will rear end your car and then either drive away or file a bogus lawsuit, leaving YOUR insurance company to pay the bill for an accident that was never your fault in the first place. You've simply moved into a more expensive risk group in general.
So my record of good driving, and my history of having ALREADY paid for covering my risk is now worthless. My coverage is only good as long as I don't use it. Whoop-ti-doo! That's REALLY 'artificial'.
Rick
One of the best days of my IT career was the day I told my boss if the problem was so simple he should go fix it himself.
January 10, 2019 at 9:54 pm
Rod at work - Thursday, January 10, 2019 2:56 PMEric M Russell - Thursday, January 10, 2019 2:16 PMPersonally, I wouldn't work for an organization that pre-screens candidates based on Credit / BMI / Social Media score or an AI. That doesn't serve the interests of qualified candidates or qualified hiring managers.I agree with you. However, playing devil's advocate for a moment, I can understand why large companies do use some sort of AI. If you're getting thousands of resumes (CV) a day hiring the personnel staff to go through them by hand becomes very daunting, if not impossible. You've got to do something to try and cut down the mountain of resumes to be reviewed.
How about knowledge then? 😉
--Jeff Moden
January 11, 2019 at 6:51 am
skeleton567 - Thursday, January 10, 2019 6:12 PMSo my record of good driving, and my history of having ALREADY paid for covering my risk is now worthless. My coverage is only good as long as I don't use it. Whoop-ti-doo! That's REALLY 'artificial'.
In terms of risk; living in a zip code with a higher prevalence of bad drivers (careless drivers, uninsured drivers, driving while on the phone) is equivalent to living in a hurricane hazard zone. The bad drivers themselves pay the highest premiums, but many of them simply choose to drive uninsured.
"Do not seek to follow in the footsteps of the wise. Instead, seek what they sought." - Matsuo Basho
January 11, 2019 at 7:15 am
Jeff Moden - Thursday, January 10, 2019 9:31 AMMy point is that the AI/Machine learning cited me as a potential buyer AFTER I "bought" a vehicle without understanding that I just "bought" a vehicle and I'm not going to be in the market for at least another 36 months when the lease runs out. That's means that the failed miserably in identifying the nature of the source of data they're using. It was never a "lead" because it arrived already frozen. It's just stupid.
Ah, this is because cookies on ads that you see are all not tied to the conversion you made. In meaning, ads are broken down into clicks, impressions, and floodlights. The clicks and impressions generate the cookies where you wear "searching" for a car. The floodlight, which can also generate a cookie, happens when you land on the website and browse around for goods. When you make a purchase on the site, this leads to a conversion floodlight.
Thus, when you are just browsing, Advertiser A, B, and C will see you looking. When you make the purchase on a website, only Advertiser C may know, but A and B will not know because you did not purchase off their ads. Thus, A and B will continue to advertise to you where C may remarket/retarget you.
Being you made the purchase likely at a dealer and not a website, A, B, and C prob still don't know you converted. They just have your clicks and impressions. I would imagine it's extremely hard to tie a car purchased offline to online ads on top of the fact advertisers do not tell other advertisers you purchased.
January 11, 2019 at 7:19 am
Rod at work - Thursday, January 10, 2019 2:56 PMEric M Russell - Thursday, January 10, 2019 2:16 PMPersonally, I wouldn't work for an organization that pre-screens candidates based on Credit / BMI / Social Media score or an AI. That doesn't serve the interests of qualified candidates or qualified hiring managers.I agree with you. However, playing devil's advocate for a moment, I can understand why large companies do use some sort of AI. If you're getting thousands of resumes (CV) a day hiring the personnel staff to go through them by hand becomes very daunting, if not impossible. You've got to do something to try and cut down the mountain of resumes to be reviewed.
The problem with AI and Data Science is that it relies on quantitative analysis, meaning metrics that are easily available and can be measured numerically. But when evaluating people, it's qualitative attributes that matter most. Rather than relying an algorithm to whittle down a list of 1,000 candidates, you may have better luck simply filtering on keywords like "SQL Server", manually reviewing the 100 most recently submitted resumes, and then lining up in person interviews with the 10 candidates that look the most promising. The DBA team themselves can set aside some time to review resumes. They know what to look for and have a vested interest in getting someone who's a good fit.
"Do not seek to follow in the footsteps of the wise. Instead, seek what they sought." - Matsuo Basho
January 11, 2019 at 7:43 am
patrickmcginnis59 10839 - Thursday, January 10, 2019 10:02 AMThe same happened to me but its completely reasonable to expect this in my case, I was most certainly tracked by searches pertaining to vehicles as I was shopping around. The actual purchase I would imagine should NOT show up in the same datasets as it should instead be limited to the companies that were actually involved in the purchase. Given that the purchase of a new vehicle is significant enough that folks might put effort over a period of time evaluating vehicles, it makes sense that ad folks who see these analytics would roll the dice and hope to hit me up as its statistically likely that I have not immediately purchased a vehicle within such a narrow timeframe relative to initiating my web searches regarding this purchase.
Yeppers. It would not be in the same dataset for sure. We have no idea you made the purchase. We just know you searched for it.
Even if you did make a purchase for a brand and the advertiser knew you made the purchase, it doesn't mean the brand has more than one advertiser working with them, where you still get ads for that brand when you know THEY know you made the purchase. They just may have 2 advertisement companies handling different channels of marketing and not talking to one another on who purchased (i.e.: social media advertising versus Google search).
January 11, 2019 at 7:53 am
Jeff Moden - Thursday, January 10, 2019 9:54 PMHow about knowledge then? 😉
What percentage of jobs at large companies are filled by the screening process?
412-977-3526 call/text
January 11, 2019 at 8:05 am
robert.sterbal 56890 - Friday, January 11, 2019 7:53 AMJeff Moden - Thursday, January 10, 2019 9:54 PMRod at work - Thursday, January 10, 2019 2:56 PMEric M Russell - Thursday, January 10, 2019 2:16 PMPersonally, I wouldn't work for an organization that pre-screens candidates based on Credit / BMI / Social Media score or an AI. That doesn't serve the interests of qualified candidates or qualified hiring managers.I agree with you. However, playing devil's advocate for a moment, I can understand why large companies do use some sort of AI. If you're getting thousands of resumes (CV) a day hiring the personnel staff to go through them by hand becomes very daunting, if not impossible. You've got to do something to try and cut down the mountain of resumes to be reviewed.
How about knowledge then? 😉
What percentage of jobs at large companies are filled by the screening process?
Heh... according to what I've seen, the screening process is actually failing. Many throw the baby out with the bath water while letting the poop stick to the sides of the tub. 😀
--Jeff Moden
January 11, 2019 at 1:17 pm
Eric M Russell - Friday, January 11, 2019 7:19 AMThe problem with AI and Data Science is that it relies on quantitative analysis, meaning metrics that are easily available and can be measured numerically. But when evaluating people, it's qualitative attributes that matter most. Rather than relying an algorithm to whittle down a list of 1,000 candidates, you may have better luck simply filtering on keywords like "SQL Server", manually reviewing the 100 most recently submitted resumes, and then lining up in person interviews with the 10 candidates that look the most promising. The DBA team themselves can set aside some time to review resumes. They know what to look for and have a vested interest in getting someone who's a good fit.
I agree with you, Eric. However, I'd like to point out that filtering for "SQL Server" is just manually doing what an AI would likely do.
Kindest Regards, Rod Connect with me on LinkedIn.
January 11, 2019 at 1:22 pm
Jeff Moden - Friday, January 11, 2019 8:05 AMHeh... according to what I've seen, the screening process is actually failing. Many throw the baby out with the bath water while letting the poop stick to the sides of the tub. 😀
Really? Interesting. I wonder why they stick with it, then?
Kindest Regards, Rod Connect with me on LinkedIn.
January 11, 2019 at 1:37 pm
Rod at work - Friday, January 11, 2019 1:22 PMReally? Interesting. I wonder why they stick with it, then?
Most likely because Jeff measures "success" based on different criteria than the recruiter 😛
Jeff's version: more QUALIFIED candidates filling the position.
HR's: fewer open slots (whichever way makes that happen). If they're not qualified we will get them weeded out later.
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Your lack of planning does not constitute an emergency on my part...unless you're my manager...or a director and above...or a really loud-spoken end-user..All right - what was my emergency again?
January 11, 2019 at 1:55 pm
Matt Miller (4) - Friday, January 11, 2019 1:37 PMMost likely because Jeff measures "success" based on different criteria than the recruiter 😛
Jeff's version: more QUALIFIED candidates filling the position.
HR's: fewer open slots (whichever way makes that happen). If they're not qualified we will get them weeded out later.
Couldn't have said it much better except that they frequently don't weed out the mistakes they've made.
--Jeff Moden
January 11, 2019 at 2:07 pm
What's really sickening is that some companies now require people with a Masters Degree in computer science to be even a Database Developer and think that's the right kind of filter and then wonder why they still have poor code and performance problems. That filter (or any filter that requires alphabet soup after a person's name) would leave folks like myself and Grant Fritchey and several other "SQL Ninjas" on this very site, that likely could smoke the competition, out of the picture.
--Jeff Moden
January 11, 2019 at 2:20 pm
Rod at work - Friday, January 11, 2019 1:17 PMEric M Russell - Friday, January 11, 2019 7:19 AMRod at work - Thursday, January 10, 2019 2:56 PMEric M Russell - Thursday, January 10, 2019 2:16 PMPersonally, I wouldn't work for an organization that pre-screens candidates based on Credit / BMI / Social Media score or an AI. That doesn't serve the interests of qualified candidates or qualified hiring managers.I agree with you. However, playing devil's advocate for a moment, I can understand why large companies do use some sort of AI. If you're getting thousands of resumes (CV) a day hiring the personnel staff to go through them by hand becomes very daunting, if not impossible. You've got to do something to try and cut down the mountain of resumes to be reviewed.
The problem with AI and Data Science is that it relies on quantitative analysis, meaning metrics that are easily available and can be measured numerically. But when evaluating people, it's qualitative attributes that matter most. Rather than relying an algorithm to whittle down a list of 1,000 candidates, you may have better luck simply filtering on keywords like "SQL Server", manually reviewing the 100 most recently submitted resumes, and then lining up in person interviews with the 10 candidates that look the most promising. The DBA team themselves can set aside some time to review resumes. They know what to look for and have a vested interest in getting someone who's a good fit.
I agree with you, Eric. However, I'd like to point out that filtering for "SQL Server" is just manually doing what an AI would likely do.
I once got an email about a job as a waiter, because they had filtered on "Server" and found my resume which mentioned "SQL Server."
Drew
J. Drew Allen
Business Intelligence Analyst
Philadelphia, PA
January 11, 2019 at 3:03 pm
drew.allen - Friday, January 11, 2019 2:20 PMRod at work - Friday, January 11, 2019 1:17 PMI agree with you, Eric. However, I'd like to point out that filtering for "SQL Server" is just manually doing what an AI would likely do.I once got an email about a job as a waiter, because they had filtered on "Server" and found my resume which mentioned "SQL Server."
Drew
I wonder how many Starbucks baristas have been offered a job as a web developer, because they're resume mentioned "Java beans", "Espresso", and "cookies"?
I'll bet more than a handful. :unsure:
"Do not seek to follow in the footsteps of the wise. Instead, seek what they sought." - Matsuo Basho
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