Could machine learning replace career coaches?

Buried at the bottom of an an HBR post titled 8 Ways Machine Learning is Improving Company Processes, is a little nugget about the ways machine learning might soon affect career planning. Machine learning could help employees in navigate their career development by providing:

Recommendations (that) could help employees choose career paths that lead to high performance, satisfaction, and retention. If a person with an engineering degree wishes to run the division someday, what additional education and work experience should they obtain, and in what order?

Could this be a career coach in the future of work? It’s a fascinating idea and I’d love to see it in practice. We’ve already seen machine learning technology take over some parts of a career advisors job. There’s even a chatbot in development that’s trying to be a career coach (let’s hope they’re better than LinkedIn’s mediocre job recommendation algorithm.) IBM uses AI to guide job seekers through their search.

A good career coach will listen to you, help you work out ideas, guide you through an ambiguous process, support you emotionally, and reflect your own words back to you. Machine learning technology can’t do this yet, in answer to my clickbait title.

But there aren’t enough good career coaches to go around. And few people can even afford a good career coach. Moreover, not every organization offers career coaching that helps employees navigate their next steps. Tools that help people navigate a world full of increasingly ambiguous career paths are mighty helpful.

Like many jobs, career coaches won’t be fully replaced by robots or artificial intelligence anytime soon. There will always be people who prefer working with people over machines. But the role of career coaches will change as new tools and technology emerge. Career coaches need to be aware of these changes. The workplace and available roles are shifting rapidly. Career coaches need to be able to coach their clients through these changes. They need to rethink outdated career advice, especially given that our job search is becoming less human. University career departments in particular need to upskill.

Today’s post is brought to you by my half way mark to 50K words for #NaNoWritMo. I’m deep into a chapter on the future of work for my book and still finding a ton of good content to write about. The challenge of course is to write about it and not just read about it. Reading is not writing, I have to remind myself a bajillion times a day.

If you’re into this type of stuff, subscribe and I’ll send you things about careers, future of work, and probably a bunch of gifs.

Where’s the discussion about employee privacy in the future of work?

In the age of big data, a measure-everything mindset is emerging. Julia Ticona, a sociologist and researcher with the Data and Society think tank in New York, says that the same types of apps that track and keep tabs on restaurant workers or delivery people 24/7 are now migrating to white-collar jobs.

But while service and manufacturing industry workers are more used to overt productivity measurements, such systems are often sold to office workers as opportunities to maximize their own productivity, she explains. “For lower wage folks, it’s about scheduling and hours,” says Ticona. “For the white collar folks, it’s about being the ‘best you.’” The inevitable future of Slack is your boss using it to spy on you

There’s so much in this article about all the ways your employer uses new technology and invasive data collection techniques to spy on you at work.  There’s even an example of a company that tracks their employees outside of work hours. Your workplace is creeping ever closer to the Circle.

So much of the future of work is focused on robots taking our jobs. But that discussion overlooks much of what’s happening outside of robots, mainly the erosion of employee privacy. The idea that companies should have the rights to all data an employee produces in the course of their workday is absurd. Employee surveillance shouldn’t be normalized. Moreover, we need more discussion about the people making decisions about what constitutes worker productivity. Who are they and how are they qualified to make these decisions? You can bet the executives and upper management aren’t being tracked like this.

I disagree that this is all inevitable. We have the power to say no to it. We have the power to teach emerging leaders how to not to use this technology or point out the potential for abuse. Employee privacy shouldn’t be a trade off for a paycheck. Employees have the power to ask questions: How are you using my personal data? What data are you monitoring? What assumptions are you making about my work when you build productivity measuring algorithms?” 

Future employees have the power to ask the right questions during their job interviews. Let’s start teaching people the right questions to ask in an interview for a white collar role. How do you measure success in this role? How do you track worker productivity? How much data do you collect on your employees and what do you use it for?

We’re in the middle of a massive transition to a quantified workplace where leadership wants to measure everything in the pursuit of pure productivity. The people who are impacted most under this system must participate in shaping this transformation and pushing back.

employee privacy

#NaNoWriMo is wrecking my blogging schedule

I’m deep into National Writing Month (#NaNoWriMo) and it’s wrecking my ability to write here. I’m in the middle of writing my second book and so far, I’m 14,000 words in for the month of November. For context, I wrote 9,000 words in all of October. The goal of #NaNoWriMo is to write 50,000 words. I’m a little behind but I’m still shooting for it.

It’s also International Education Week (IEW2018) so I’m busy promoting GlobalMe School and teaching career services how to improve international student career outcomes on one of my other websites. In short, I’m tapped out of words.

On the plus side, #NaNoWriMo month is an excellent tool for aspiring book writers. Things I’ve learned in only two weeks:

  • The only way you will write a book is to put your ass in a seat and write. Truth.
  • Writing without self-editing is the hardest part of this month long exercise. I’ll never make it to 50K words if I edit.
  • Researching writing is not writing your book. Neither is writing about writing a book (which I’m doing now). Writing your book is the only writing that counts towards the goal of publishing a book.
  • Getting comfortable with the rawness of your words and accepting the messiness is part of the process.
  • The world is full of people who say they can write better than (insert book here). Like most things, it’s so much harder than it looks.

So in lieu of a post, here’s an article dump on the most interesting things I’ve read this week about AI and ethics, a subject I’m increasingly more interested in. If I weren’t so brain dead from barfing words elsewhere, I’m sure I’d come up with something clever to say about these. But I can’t. So here we are.

The Newest Jim Crow

Principles for Ethical Machine Learning

China takes facial recognition tech to Africa

This insanely creepy roundup of patents to increase corporate surveillance in your home and I can’t even…

Followed by this tweet by the ever insightful researcher Zeynep Tufekci.