Artificial intelligence is going to wreck your carefully planned career

Yesterday I presented to a group of undergraduate students at PSU about FutureMe School and the coming changes to the workforce. As someone who regularly talks about the future of work this was the first time I’ve stood in front of soon-to-graduate students and tell them they’ll need to become lifelong learners because artificial intelligence. It’s a bit of an awkward message to deliver. They’re in their last term, weeks aways from finishing up four years of learning, working, and preparing for their next career move. They are ready to take on the world with their new skills. And I’m telling them they’re going to need to keep learning, upskilling, post-college.

But the students were game for the discussion and asked solid questions about my business plan and online courses.

The experience, however, highlights one of the biggest challenges I have right now. Everyone working in future of work spaces is working to educate employees and students about the coming changes to the workforce. Despite the blazing headlines about robots taking our jobs, the subject (or fear?) isn’t tangible enough to stick. How do we get people to shift from outdated career models and thinking to commit to lifelong learning and upskilling? How do we get people to see how artificial intelligence is changing the workplace and our jobs, if they aren’t yet feeling affecting by the technology?

Predictive analytics and algorithmic decision making happen outside of our view, behind the scenes of our daily lives. Yet we are increasingly influenced by these invisible algorithms from what we see in our newsfeeds to what prices we pay for flights. Algorithms are shaping our workplaces too. From managers that monitor employees using predictive analytics, to algorithms that rank resumes, to smart platforms that determine how we get hired, these technologies shape our career decisions and job search outcomes.

Yesterday I asked if any of the students had experienced an interview using the HireVue platform. One had. I asked if she knew she was being evaluated by algorithms. She responded that she wasn’t, and the audible, “Whaaaat?” and gasps from the audience indicated most students weren’t aware either. Job seekers need to know about the technology that’s being used to evaluate them. 

For yesterday’s talk I put together the resources to help students understand the coming changes, the technology, and how to prepare for an ambiguous career. If you’ve seen the headlines about robots taking our jobs and want to get beyond the headline hype, check out the resources below.

Start with the video below as an introduction to the subject.

BONUS WATCHING: Learn about the digital skills gap

Next, play with this fun tool: Willrobotstakemyjob.com

If you have extra time, dive into this episode, McKinsey Global Institute Podcast: How will automation affect jobs, skills, and wages? It’s a bit dry because it’s consultants talking but it’s worth understanding in depth just how dramatic of a shift is coming to the workforce. Here’s a quote from the episode to put it in perspective:

It’s something that has been a bit of a mantra in the educational field. Everyone is going to have to be a student for life and embark on lifelong learning. The fact is right now it’s still mainly a slogan. Even within jobs and companies there’s not lifelong training. In fact what we see in corporate training data at least in the United States, is that companies are spending less. As we know right now people expect that they get their education in the early 20s or late 20s and then they’re done. They’re going to go off and work for 40, 50 years. And that model of getting education up front and working for many decades, without ever going through formal or informal training again is clearly not going to be the reality for the next generation.

Continuing on that theme is another article by McKinsey, Getting Ready for the Future of Work, which is worth reading if only for this shocking quote right here:

The time it takes for people’s skills to become irrelevant will shrink. It used to be, “I got my skills in my 20s; I can hang on until 60.” It’s not going to be like that anymore. We’re going to live in an era of people finding their skills irrelevant at age 45, 40, 35. And there are going to be a great many people who are out of work.

Then spend some time reading about how artificial intelligence is changing the way we find and get jobs. Start with, AI is now analyzing candidates facial expressions during job interviews. Then read about my experience trying to interview with a chatbot. Finally, put it all together in The grim reality of job hunting in the age of AI.

And if this all has you thinking, holy shit, am I at risk of being irrelevant?!?! read, How to Stay Relevant in Today’s Rapidly Changing Job Market.

While you’re at it sign up for early access to FutureMe School because we exist to smash traditional career narratives and prepare you for this new world of work.

If you’re super interested in understanding AI in depth from a non-tech perspective, and want your mind blown while being entertained by stick figure comics, read with this fabulous introduction to the subject: The AI Revolution part 1 and part 2. They are both long reads so settle in.

Even more reason to make that LinkedIn connection

I’m still on an HR Tech deep dive. This time I found a remarkable platform that takes a proactive approach to employee referrals. Teamable helps employees make referrals and reach out to their contacts for opportunities. They do it by mining current employees’ social contacts and building profiles of potential candidates.

Here’s how it works:

This is even more motivation to connect with people: build relationships and get discovered.

I’m still conflicted about all the HR Tech that creeps on you. There’s a great deal of social scraping going on across HR Tech. But at least this platforms helps existing employees improve their referrals (and get money) and helps people who are actively building relationships get seen and hopefully hired.

Here’s a little more on what Teamable is up to and what they’ll do with their $5 mil round of funding that they don’t need.

BONUS: The founder’s badass bio mentioned rugby and travel, which is basically the greatest:


Rugby and travel also taught me everything I need to know about business.

Navigating AI in the Job Hunt

Just dropping this Guardian article off here: ‘Dehumanising, impenetrable, frustrating’: the grim reality of job hunting in the age of AI

It features plenty of questions we should all be asking about AI in the job search. It also centers the discussion on the maddening experience of searching for work when AI is your evaluator and the gate keeper to getting hired. It’s ironic that organizations want more employees with soft skills yet the recruiting experience is transforming into a less human process. On top of that we’re outsourcing the ability to identify the relevant soft skills to technology that still isn’t very good at them.

This shift has already radically changed the way that many people interact with prospective employers. The standardised CV format allowed jobseekers to be evaluated by multiple firms with a single approach. Now jobseekers are forced to prepare for whatever format the company has chosen. The burden has been shifted from employer to jobseeker – a familiar feature of the gig economy era – and along with it the ability of jobseekers to get feedback or insight into the decision-making process. The role of human interaction in hiring has decreased, making an already difficult process deeply alienating.

Beyond the often bewildering and dehumanising experience lurk the concerns that attend automation and AI, which draws on data that’s often been shaped by inequality. If you suspect you’ve been discriminated against by an algorithm, what recourse do you have? How prone are those formulas to bias, and how do the multitude of third-party companies that develop and license this software deal with the personal data of applicants? And is it inevitable that non-traditional or poorer candidates, or those who struggle with new technology, will be excluded from the process?

Job seekers will be battling the robots on two sides: in the recruiting process and as they advance in their careers. It’s not going to get any easier.

Interview with a chatbot part 2

These past weeks I’ve been deep into the #HRTech world, tweeting frequently into the void, trying to learn more about increasingly opaque data used in smart HR platforms. Throughout the process I’m documenting the variety of hiring technology on the market, from smart platforms to machine learning for automated resume screening to virtual assistants. Along the way I’ve stumbled on loads of chatbots trying to claim a place for themselves in the hiring process. I’ve got a bit of a crush on chatbot technology so I’ve been trying them out. Two weeks ago I pined for Mya but settled on an interview using TalkPush. Today I found Paradox.ai and gave it a go.

Once again, the intro starts easily enough:

Then I was immediately asked contact details. Mind you, I’m just browsing here, not actually ready to apply. I don’t know if it’s me or what but I’m a bit irritated each time I’m asked for contact details right away (side note: I signed away my LinkedIn data for Wade&Wendy access only to be told post-data exchange that I’m on a waitlist, so maybe I’m just tired of having to give up data to engage). But this is all pretend anyway, so I gave them my phone number and then we moved on to my interests.

I thought here that we might talk about what positions are available but the onus was put on me to define what I want. I actually wasn’t sure what to answer. I like that it’s framed that way but wonder how other job seekers perform when asked this question. Admittedly it caught me off guard and I wasn’t sure what to write. I had to think about it which then sent me down a mini-spiral wondering if they evaluated me on how long it took for me to answer.

Moving along:

This is where it got interesting. They’ll find my profile (and I wonder if they’ll find my other social profiles) for me, so I don’t have to submit anything.

I think there was a hiccup when I shared my non-existent most recent role as the interview ended abruptly. I can’t tell if it’s because a. it’s a bot. b. I’m not a fit. or c. this wasn’t an interview.

Then onto the questions from me. The Q&A started a bit rocky but got better:

So throughout this whole process I wondered: how do you know the difference between a bot that helps you explore job opportunities and one that evaluates you? I misunderstood this bot from the beginning.

I went into it thinking the bot was helping me explore options at the company. But it quickly moves into interview territory by asking my experience level. Then it forces me to automatically apply to the position(s?) we discussed as they pull my LinkedIn profile. What if I hadn’t updated my LinkedIn?

Also, what if I’ve already provided my real name and contact, but wasn’t prepared to discuss my experience, what do I do? If I abandon the convo, and return, how does that affect my evaluation? Am I more desirable because I’m returning? Or am I penalized because I couldn’t answer the prior questions?

I’m so curious what happens on the backend when a recruiter receives the data.

While this experience is certainly efficient it’s hard to get a feel for company culture during these interactions. I was generally curious about the companies that they partner with but didn’t get traction there. Asking about the workplace and getting a canned response about “best talent” and “superstars” doesn’t offer much. If Olivia instead shared a video from the team, or a blog post about a day in the life of a marketer at Paradox, or even a personalized message from the founder that wasn’t full of “superstar” startup speak, it’d instantly provide more value. It’d at least add a personal touch.

Interacting with bots has me wondering how we define candidate engagement within the context of chatbots. Olivia engaged with me but she wasn’t engaging (though she was definitely better than previous bots I’ve engaged with). When the novelty of interacting with recruiting bots wears off (it’s still so very new), I wonder how candidates will view the experience.  If there’s a war for talent, how do you expect someone to chose your company if you can’t show off goods? Do bots play a role in wooing candidates? Or are they just there to expedite the hiring process for HR?

And if candidates are expected to show their soft skills, how do employers expect to identify them when the majority of HR tech aims to take humans out of the selection process?

Using Google Chrome makes you a better employee?!

Just need to park a few nuggets somewhere until I can write a full post about this topic. I’m currently researching the use of new talent signals, data scraping, and machine learning in the hiring process. These excerpts come from the Journal of Industrial and Organizational Psychology, in an article titled, New Talent Signals: Shiny New Objects or a Brave New World?

On the use of big data in the workplace: 

So long as organizations have robust criteria, their ability to identify novel signals will increase, even if those signals are unusual or counterintuitive. As an example of an unlikely talent signal, Evolv, an HR data analytics company, found that applicants who use Mozilla Firefox or Google Chrome as their web browsers are likely to stay in their jobs longer and perform better than those who use Internet Explorer or Safari (Pinsker, 2015). Knowing which browser candidates used to submit their online applications may prove to be a weak but useful talent signal. Evolv hypothesizes that the correlations among browser usage, performance, and employment longevity reflect the initiative required to download a nonnative browser (Pinsker, 2015).

On using social media to evaluate candidates: 

“People’s online reputations are no more “real” than their analogue reputations; the same individual differences are manifested in virtual and physical environments, albeit in seemingly different ways. It is therefore naïve to expect online profiles to be more genuine than resumés, although they may offer a much wider set of behavioral samples. Indeed, recent studies suggest that when machine-learning algorithms are used to mine social media data, they tend to outperform human inferences of personality in accuracy because they can process a much bigger range of behavioral signals. That said, social media is as deceptive as any other form of communication; employers and recruiters are right to regard it as a rich source of information about candidates’ talent—if they can get past the noise and make accurate inferences.”

On the use of video interviews for voice profiling:

“Moreover, through the addition of innovations, such as text analytics (see below) and algorithmic reading of voice-generated emotions, a wider universe of talent signals can be sampled. In the case of voice mining, candidates’ speech patterns are compared with an “attractive” exemplar, derived from the voice patterns of high performing employees. Undesirable candidate voices are eliminated from the context, and those who fit move to the next round. More recent developments use similar video technology to administer scenario-based questions, image-based tests, and work-sample tests. Work samples are increasingly common, automated, and sophisticated. For example, Hirevue.com, a leading provider of digital interview technologies, employs coding challenges to screen software engineers for their software writing ability. Likewise, Uber uses similar tools to test and evaluate potential drivers exclusively via their smartphones (see www.uber.com).”

On new technologies barreling ahead without theoretical backing

“The datification of talent is upon us, and the prospect of new technologies is exciting. The digital revolution is just beginning to appear in practice, and research lags our understanding of these technologies. We therefore suggest four caveats regarding this revolution. First, the new tools have not yet demonstrated validity comparable with old school methods, they tend to disregard theory, and they pay little attention to the constructs being assessed. This issue is important but possibly irrelevant, because big data enthusiasts, assessment purveyors, and HR practitioners are piling into this space in any event.”

I’ve said it before but the candidate process is about to get far more opaque.

Interviewing with a chatbot

I’m trying out different HR tech for the first course I’m releasing on futuremeschool.com. Right now I’m examining HR chatbots. Chatbots are becoming ridiculously popular in HR because they save recruiters valuable time. Candidates are comfortable with them too. SHRM reported that 57% of survey respondents confirmed they were fairly or extremely comfortable interacting with AI bots in the recruiting process. In an ideal world the chatbots deliver a premium candidate experience, giving everyone the opportunity to engage with the company.

I’ve been trying to get a look at Mya, the chatbot that interviews you, analyzes and scores your responses, and does the heavy lifting for recruiters. I really really really want that damn bot to interview me in part because I have a crush on its conversational design technology and because I secretly want to be a part time conversation designer.

Natural language processing Mya

But I can’t get Mya to interview me yet as I’m not exactly qualified for their jobs, so no bot access.

Luckily I stumbled on a chatbot for TalkPush, which is a HR chatbot company that makes it easier for recruiters to source. Here’s their pitch:

TalkPush is the first conversation-driven Candidate Relationship Management (CRM) system. On Talkpush, recruiters spend more time talking to qualified candidates, which translates into a better candidate experience and huge reductions in cost-per-hire and time-to-fill”

TalkPush has received about $1 million in funding. Compared to Mya, which has received $34 million and on its Series B funding, TalkPush hasn’t gotten much of that sweet 2 billion dollar plus HR tech funding pie.

Regardless I’m on a mission to try out hiring chatbots and TalkPush made it easy. You can access their recruitment bot from the homepage but you’ll be required to give access to your FB page, an offputting ask since we literally just met (what are you doing with that data?).

However their jobs page gives you access to their bot without having to give over your FB access.

The interaction starts simply enough.

HRChatbotinteraction

HRChatbotInteraction

I was a bit put off by the questions about contact info before I even got to see the jobs. Then again I’d submit this info on a resume, so I suppose it evens out in the end.

There were also a few typos in the exchanges. I thought it reflected poorly on the company since in the US, typos result in the immediate death of your candidacy for employment. Then I found out the company is based in Hong Kong and China, so the conversation is likely written by a non-native speaker. Makes sense now (I see an opp for some talented bilingual interns!)

But then the experience gets a bit lame.

HRChatbotexample

This wasn’t so much a conversation as it is a display of information. After I asked what a (Demo) Job Position is, I got nothing. Radio silence. So I checked in.

HRChatbot5

Aaaaand it seemed like we were back at the beginning. This reminded me of the awkward experience I had with IBM Watson’s chatbot. So I typed in an alternative job. Then we were back on track. Interestingly I was supposed to tell them about my relevant work experience. I wondered what they meant by that. Should I write paragraphs? Is this the place for a short, two-sentence summary of my qualifications, similar to what I’d write in a cover letter intro or say to a recruiter on the phone? Should it be a list of companies? Positions? Software? Skills? Am I being evaluated on the time it takes to answer questions, similar to video interviews? I had no idea. So I just added some fuckery to advance the convo and see the other questions.

HRChatbot hiring future of work

HRchatbot example

As you can see I wasn’t a good candidate. But the questions were interesting reminded me of an initial phone interview.

The flow and expectations in this exchange are a bit problematic though. As a job seeker, I may not have samples of my work or a summary of my work experience on hand during the chat interview. Since I had to put in my contact information before I saw the jobs, it’d be hard to take a break, go get that information, and return to the bot to continue the conversation. Usually a candidate takes a look at a job, builds the required documents based on the job, then returns to submit. This experience was like an application and phone interview in one.

Also the inability to engage at times makes me wonder what candidates should do when a bot fails. Any interaction with an employer should be considered evaluative. This leads to questions about best practices for candidate behavior. What should a candidate do if they’re stuck engaging with a bot? If the chatbot fails, what are the next steps for the candidates? If the chatbot misunderstands their information or can’t answer a question, does the candidates get bumped to a human? Can a candidate press 0 or some magical combo to get a live human?

If I were advising a candidate, I’d tell them to take screen shots and contact a recruiter directly with questions. It’d work in the candidate’s favor maybe. It could show that the candidate is a proactive, problem solving candidate. And it’d (hopefully) help the team improve their bot.

So for all job seekers out there: brush up on your professional written communication skills. You’re going to need them beyond writing cover letters to get past the bots.

So how you feeling about your future career?

“So what should we tell our children? That to stay ahead, you need to focus on your ability to continuously adapt, engage with others in that process, and most importantly retain your core sense of identity and values. For students, it’s not just about acquiring knowledge, but about how to learn. For the rest of us, we should remember that intellectual complacency is not our friend and that learning – not just new things but new ways of thinking – is a life-long endeavour.” Blair Sheppard Global Leader, Strategy and Leadership Development, PwC

60% think ‘few people will have stable, long-term employment in the future’. PwC survey of 10,029 members of the general population based in China, Germany, India, the UK and the US.

74% believe it’s their own responsibility to update their skills rather than relying on any employer.

Source: PWC Workforce of the Future report.

Upward mobility and clear career progression are no longer guaranteed. So how does this shape what we teach students about their careers? Learning to write a resume and taking career assessments seem quite pointless in the face of type of change.

Jobbatical gets international job seekers

I’m spending some time these days trying out a lot of HR Tech. I’m in search of user-friendly, forward-thinking job search tools for my online career courses.

Jobbatical has been on my radar for over a year. Since I teach people how to build global careers, I frequently include their job postings in my weekly newsletter.

There are no shortage of job search platforms for job seekers to use. Nearly all have international location filters. But here’s what makes Jobbatical so good: they understand international job seekers.

Here’s what makes the exceptional:

Visa sponsorship is front and center 

The top question on any internationally mobile candidate’s mind is work authorization. Right after, who’s hiring, they’re asking “Will the company sponsor me?” Jobbatical puts that information front and center. LinkedIn, the global jobs platform, still doesn’t do this.

Company overview with tags for more exploration

The country tags make it easier to explore additional open positions in the country. This easy-to-find feature is ideal for international job seekers who have specific geographic goals. It also facilitates exploration, ideally getting people to spend more time with your content. Given that most people searching for work online are in an exploratory phase, this little feature can make a big impact.

jobbatical geo tag

Introduction to the city and cost of living comparisson

When a job seeker is considering moving their life across the world they need more than a job description. Instead forcing users to find expat information on another site, Jobbatical provides a quick glance at living costs and often a city guide. This makes it easier for international job seekers to envision their new life, not just in a new role but thriving in a foreign country.

Overall, Jobbatical’s UX is solid. They offer international job seekers a seamless yet delightful exploratory search experience. Career platforms, especially the ones that serve universities, should take a cue from these guys.

Hiring practices are about to get even more opaque

All that advice about plugging keywords into your resume to make sure it passes the ATS systems is about to be useless. Here’s an excerpt from AI for Recruiting: A Definitive Guide to for HR Professionals by Ideal.com, a AI-powered resume screening and candidate tracking solution for busy recruiters.

Intelligent screening software automates resume screening by using AI (i.e., machine learning) on your existing resume database. The software learns which candidates moved on to become successful and unsuccessful employees based on their performance, tenure, and turnover rates. Specifically, it learns what existing employees’ experience, skills, and other qualities are and applies this knowledge to new applicants in order to automatically rank, grade, and shortlist the strongest candidates.The software can also enrich candidates’ resumes by using public data sources about their prior employers as well as their public social media profiles.

Now for all the questions: What are the “other qualities” that they measure? How much weight do they give to experience vs. skills? How much data does a company need to use these algorithms effectively? How does a company without loads of data use this technology? Who decides which data to use? Who reviews the training data for accuracy and bias – the company or the vendor? How does this company avoid bias, especially if people who advance are all white men (due to unconscious bias in the promotion process)? What data points are most valuable on candidates social profiles? Which social profiles are they pulling from? Are personal websites included? Which companies are using this technology? Are candidates without publicly available social media data scored lower? Of the companies using these technologies, who’s responsible for asking the questions above?

This technology gives a whole new meaning to submitting your resume into a black hole.

Will black box algorithms be the reason you don’t get your next job?

A good example is today’s workplace, where hundreds of new AI technologies are already influencing hiring processes, often without proper testing or notice to candidates. New AI recruitment companies offer to analyze video interviews of job candidates so that employers can “compare” an applicant’s facial movements, vocabulary and body language with the expressions of their best employees. But with this technology comes the risk of invisibly embedding bias into the hiring system by choosing new hires simply because they mirror the old ones.

– Artificial Intelligence—With Very Real Biases

Beyond bias we should be asking serious questions about the data that these algorithms are based on: what data are they using to determine the connection between facial movements, vocabulary, and body language as predictors of job performance?

More from the article above:

“New systems are also being advertised that use AI to analyze young job applicants’ social media for signs of “excessive drinking” that could affect workplace performance. This is completely unscientific correlation thinking, which stigmatizes particular types of self-expression without any evidence that it detects real problems. Even worse, it normalizes the surveillance of job applicants without their knowledge before they get in the door.