Job Titles of the Future #12 - Professional Selfie Retoucher

According to Business Insider, the reality TV personality Kim Kardashian spends upwards of $100K to keep a 'professional selfie retoucher' on call, who stands (or sits more likely) at the ready, poised to edit, smooth, crop, and apply just the right Instagram filter (I am a 'Hudson' fan myself), to her selfies and other photos prior to posting them to her millions of social media followers.

If it sounds ridiculous, it is because it is ridiculous. But I think at least half of why it is ridiculous is the kind of silly name this job has been bestowed, and the kind of silly protagonist of the story. Kim Kardashian retaining a professional selfie editor to be on call is comical, but what about an author, sports figure, politician, or CEO engaging consulting services to protect, augment, and improve their online personas? Maybe not so silly.

It must be a really big deal, and a important part of her business strategy, for Kim to be seen in a certain manner in her social media posts and activity. She must have figured out what her fans want and expect, and paying $100K to make sure she delivers on those expectations must be worth it to her in the long run.

But in some ways it is not just reality TV stars or athletes or actors that rely on social media image and presence as a big part of their business strategy. Lots of 'normal' people do to. We are all, as long as we participate in blogs or on social media sites like Twitter and Instagram, placing some importance (and risk) in how our intelligence, professionalism, and value are interpreted via our posts and pictures and, yes, our selfies.

And lots of us try to be really careful about what we post. Not just in that 'I better not post that pic of me and the boys doing tequila shots', but also along the lines of 'Does this picture make me look smart/cool/happening/likable/on 'brand'?' You know you think about that. Everyone does. Think about how much you crop and filter and edit those Instagram and Facebook pics before you load them. It isn't just about you wanting to be the next Ansel Adams.

It's just that you and me and almost everyone else makes these determinations and manipulations of our preferred version of reality for ourselves - it's only people like Kim K. who can dish out $100K to worry about that stuff for her.

There have been PR agencies and image consultants and even 'personal brand coaches' (that title just made me gag a little), around for awhile, so the idea of a 'professional selfie retoucher' may not be all that new or novel, and just may be the logical extension or modernization of these roles for the social media age.

But still, something about it, the on-the-nose way it describes the function seems new to me, and thus I officually welcome 'Professional Selfie Retoucher' as Job Title of the Future #12.

Have a great weekend!

The Cold Changes Everything

I have had about 25 or so phone calls this week working on the program for the 2015 HR Technology Conference, (note, registration is officially OPEN, please see www.hrtechconference.com/register.html for more details), and I bet 24 of them have started something like this:

Me: Hi, this is Steve

Person A: Hi, Steve how are you? Are you getting all that snow/surviving the winter/staying warm?

Me: Oh man, it has been brutal. <at this point I go on for a minute or two, lamenting the cold, the snow, the giant icicles hanging off of my roof, the fact I have been stuck in my car a couple of times, my kid's school has been closed due to the -25 wind chill, etc.>

Person A: Wow, that is terrible. It is freezing here too <and then Person A takes their turn listing their tales of excruciating snowy woe>

You get the idea.

For most of the eastern half of the USA, the last six weeks or so have been a relentless, crushing, and demotivating series of snow storms, Arctic cold, and more storms.

This kind of sustained period of misery begins to get to you after a while - you lose energy for the things you want to do (creative work, spending time with family and friends), because you have to expend so much more time and energy dealing with the impacts and exigencies of the weather (clearing snow, chipping ice off of the car windows, sitting in traffic jams or waiting out airport delays).

It's has been bad, really bad - and if you are lucky enough to live and work in a part of the country/world that has not had to deal with this winter then you are really fortunate and smart. Also, I hate you.

I don't have a solution for this, except perhaps to say we ought to do something for our teams and colleagues that have been dealing with this ongoing, frosty nightmare.

Maybe give everyone at work a free 'Snow day' off. Except save it for say Friday May 22 - the last day of work before the long Memorial Day weekend. Your people will appreciate having a snow day that is not, you know, actually snowing and one they can enjoy.

So there it is. I am declaring an official 'Snow Day' on May 22. I will bring the BBQ.

Stay warm out there my friends. 

CHART OF THE DAY: There's Just 5 Million Open Jobs in the USA

Here's your latest Chart of the Day, courtesy of my two favorite online data sources, the Bureau of Labor Statistics, (specifically the Job Openings and Labor Turnover Summary, or JOLTS report), and the FRED data analysis and visualization tool.

First, the chart, then some FREE commentary from your humble scribe:

1. First, the actual numbers - there were 5.028 million job openings in the US on the last business day of December 2014, the highest number since December 2001.

2. The chart shows a pretty much straight up and to the right climb in job openings since early 2009, meaning talk of the recession and the labor market disruptions it caused are really seeming far, far behind us

3. This increase in openings is driving organizations like Walmart to raise wages for many of its workers - for a wide range of industries, and geographies, (including previously 'low worker power' ones like retail), the balance of that power is shifting. 

4. Average weekly earnings for Production and Non-farm employees are climbing as well, not as fast as jop openings, but certainly on the same trajectory.

So what does this mean for you, Mr. or Ms. HR pro?

Probably nothing new, or at least nothing you have not been hearing about and likely experiencing in the last 18 months or so. 

Lots more noise in the system to get your company and your opportunities noticed in a much more crowded market of available jobs.

Many fewer un- and under-employed individuals around that might not always been qualified for your openings, but at least were a source of steady candidate flow. At the depths of the recession, there were about 7 unemployed workers for every job opening. Today that ratio is less than 2/1.

You, having a harder time coming up with explanations/excuses to your leadership and hiring managers who (traditionally) are much slower to accept these changes in the labor market and the ensuing power shifts. I recommend forwarding to them the Walmart story above, with a subject line that says 'See, even Walmart is having a hard time finding and keeping people'.

Long story short, we entering year 6 of an extended recovery/tightening of the labor market. Talent is in shorter supply, opportunities are everywhere, the Dow and the S&P 500 are at record highs, and the people you need to find, attract, and retain are well, harder to find, attract, and retain.

Have fun, it's a jungle out there.

On trusting algorithms, even when they make mistakes

Some really interesting research from the University of Pennsylvania on our (people's) tendency to lose faith and trust in data forecasting algorithms (or more generally, advanced forms of smart automation), more quickly than we lose faith in other human's capabilities (and our own capabilities), after observing even small errors from the algorithm, and even when seeing evidence that relative to human forecasters, the algorithms are still superior.

From the abstract of Algorithm Aversion: People Erroneously Avoid Algorithms After Seeting Them Err:

Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet, when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.

Let's unpack that some. In the research conducted at Penn, the authors showed that even when given evidence of a statistical algorithm's overall superior performance at predicting a specific outcome (in the paper it was the likelihood of success of MBA program applicants that the humans and the algorithm attempted to predict), most people lost faith and trust in the algorithm, and reverted to their prior, inferior predictive abilities. And in the study, the participants were incentivized to pick the 'best' method of prediction: They were rewarded with a monetary bonus for making the right choice. 

But still, and consistently, the human participants more quickly lost and faith and trust in the algorithm, even when logic suggested they should have selected it over their (and other people's) predictive abilities.

Why is this a problem, this algorithm aversion?

Because while algorithms are proving to be superior at prediction across a wide range of use cases and domains, people can be slow to adopt them. Essentially, whenever prediction errors are likely—as they are in virtually all forecasting tasks—people will be biased against algorithms, because people are more likely to abandon an algorithm than a human judge for making the same mistake.

What might this mean for you in HR/Talent?

As more HR and related processes, functions, and decisions become 'data-driven', it is likely that sometimes, the algorithms we adopt to help make decisions will make mistakes. 

That 'pre-hire' assessment tool will tell you to hire someone who doesn't actually end up beign a good employee.

The 'flight risk' formula will fail to flag an important executive as a risk before they suddenly quit, and head to a competitor.

The statistical model will tell you to raise wages for a subset of workers but after you do, you won't see a corresponding rise in output.

That kind of thing. And once these 'errors' become known, you and your leaders will likely want to stop trusting the data and the algorithms.

What the Penn researchers are saying is that we have much less tolerance for the algorithm's mistakes than we do for our own mistakes. And maintaining that attitude in a world where the algorithms are only getting better, is, well, a mistake in itself.

The study is here, and it is pretty interesting, I recommend it if you are interested in making your organization more data-driven.

Happy Tuesday.

US Time Zones, Ranked

A long, long day of scheduling, re-scheduling, and conducting meetings with people all across the USA was the inspiration for the below, your definitive ranking of United States Time Zones:

6. Mountain Time - Not needed, really. How many people do you know who reside in Mountain Time?Click for giant version

5. Alaska Time - Reality TV would be a vast wasteland if not for Alaska. Plus, you can see Russia from there.

4. Central Time - Sort of a middle-of-the-road, not making waves kind of time. Plain.

3. Pacific Time - Has the annoying habit of showing Knicks home games at 4PM.

2. Hawaiian-Aleutian Time - I have never actually used this time zone, but since it has Hawaii, it must be fantastic.

1. Eastern Time - The only US time zone that really counts. Yes, I live in Eastern time, what of it?

Have a great night, (or day if you are on Hawaiian-Aleutian Time).