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.

LinkedIn’s mediocrity is killing me

I’m a LinkedIn power user. At Yale SOM I lived on LinkedIn: reaching out to global employers, training global executives how to be thought leaders, and teaching students how to search alumni and track opportunities. These days I use it only slightly less with more focus on building partnerships and teaching students in my online courses how to use it.

So I say this with much love and experience: LinkedIn is so ridiculously mediocre.

I can’t for the life of me understand how a company with so many users and Microsoft-backing still spends so much time trying to get me to spam my inbox.

Yet when I get those connections, LinkedIn makes it ridiculously hard to organize and keep up with those connections.

My connections are all parked in a feature-poor list. If I’m looking to connect with someone working in fintech in Seattle, the sort feature offers little to help me find them (when’s the last time you remembered a conference contact by their first name?) Even the search feature doesn’t work properly:

Results of my Seattle search, where I’d wager 25% of my professional contacts reside

Yet when I want to search alumni from my school, I get this incredible, visual, search feature.

Why isn’t this feature replicated for contacts? If the point of LinkedIn is to stay connected to your contacts, why don’t they make it as easy as possible to find and visualize your contacts? (side note: What is the point of LinkedIn?)

Also, it’s worth noting that this is the result after they redesigned it to be more user-friendly.

Then there are all the attempts to get you to upgrade.

Not sure that’s the best way to motivate me to use premium.

LinkedIn is also pushing the online learning opportunities. There too I find their suggestions and course-dump lacking.

 I have zero connection to digital arts or animation.

LinkedIn has all the resources, deep data, and millions of users. Yet these are the results.

LinkedIn, hopelessly mediocre.

AI is going to make your awful manager even worse

Before you continue reading post-click bait title reflect on the last bad manager you had. Remember how they made you feel. Remember the things they did that made your life miserable. Remember the incompetence. Remember that managers don’t get promoted to management because they’re good managers.

I know, it’s not pleasant. I’ve have some pretty awful managers too (but I’ve also had a billion jobs so it’s inevitable).

Ok. Now read on.

HR tech is hot. Nearly $2 billion in investment hot. And AI is hotter than bacon. So combining HR tech and AI is a sizzling idea (still with me?).

Enter all the startups ready to make managers lives easier/employees lives more miserable with algorithms to solve all the HR problems. The Wall Street Journal takes a peak into the future of management in How AI is Transforming the Workplace.

“Veriato makes software that logs virtually everything done on a computer—web browsing, email, chat, keystrokes, document and app use—and takes periodic screenshots, storing it all for 30 days on a customer’s server to ensure privacy. The system also sends so-called metadata, such as dates and times when messages were sent, to Veriato’s own server for analysis. There, an artificial-intelligence system determines a baseline for the company’s activities and searches for anomalies that may indicate poor productivity (such as hours spent on Amazon), malicious activity (repeated failed password entries) or an intention to leave the company (copying a database of contacts).Customers can set activities and thresholds that will trigger an alert. If the software sees anything fishy, it notifies management.”

Now remember your asshole manager. Imagine if they had access to this tool. Imagine the micromanagement.

Brutal.

(Side note: I wonder if employees get access to their bosses computer logs. Imagine that!)

Let’s keep going.

Another AI service lets companies analyze workers’ email to tell if they’re feeling unhappy about their job, so bosses can give them more attention before their performance takes a nose dive or they start doing things that harm the company.

Yikes.

It’s hard not to read that as an unhappy worker is somehow a threat to the company. Work isn’t all rainbows and unicorns. We can’t be happy 40 hours a week even in the best of jobs. Throughout our work lives we deal with grief, divorce, strained friendships, children, boredom, indecision, bad coworkers, bad bosses, bad news, financial stress, taking care of parents, etc etc etc. And sometimes that comes out in the course of our days spent buried in emails. The idea of management analyzing your emails on the watch for anything that isn’t rainbows ignores the reality of our work lives.

What data is the algorithm built on? What are the signs of unhappiness? Bitching about a coworker? Complaining about an unreasonable deadline? Micromanaging managers? What’s the time frame? One day of complaints or three weeks? Since algorithms take time to tweak and learn, what happens to employees (and their relationships with management) who are incorrectly flagged as unhappy while the algorithm learns?

Moreover, what do those conversations look like when “unhappy” employees are being called into management’s office?

Manager: Well we’ve called you in because our Algorithm notified me that you’re unhappy in your role.

Employee:

Manager: Right… so … can you tell me what’s making you so unhappy?

Employee: I’m fine.

Manager:

Not according to The Algorithm. It’s been analyzing all your emails. I noticed you used the word “asshat” twice in one week to describe your cubicle mate. Your use of the f word is off the charts compared to your peers on the team. You haven’t used an exclamation point to indicate anything positive in at least three weeks. The sentiment analysis shows you’re an 8 out of 10 on the unhappy chart. Look, here’s the emoji the algorithm assigned to help you understand your unhappiness level.

Employee: It’s creepy you’re reading my emails.

Manager:

Now remember, you signed that privacy agreement at the beginning of your employment and consented to this. You should never write anything in a company email that you don’t want read.

Employee:

 

And do companies who purchase this technology even ask the hard questions?

The issue I have with this tech, apart from it being ridiculously creepy, is that it makes some seriously bad assumptions. They assume:

  • All managers have inherently good intentions
  • All managers are competent
  • All organizations train their managers on how to be effective managers
  • All organizations train their managers on appropriate use of technology
  • Managers embrace new technology

Those are terrible assumptions. Here’s a brief, non-exhaustive list of issues I’ve had with managers over the past ten years:

  • Managers who can’t define what productivity looks like (beyond DO ALL THE THINGS)
  • Managers who can’t set and communicate goals
  • Managers who can’t listen to concerns voiced by the team (big egos)
  • Managers who can’t understand lead scoring and Google analytics (from the CEO and VP of sales and marketing no doubt)
  • Managers who can’t  use a conference call system (technology-am-I-right?!)
  • Managers with no interpersonal communication skills and lack of self-awareness

Maybe we can all save ourselves by adding a new question when it’s our turn to ask questions in the interview:

“Tell me about your approach to management. What data do you use to ensure your AI technology accurately assesses employee happiness?”

Maybe I’m just cynical. Maybe it’s because I’ve had a few too many bad managers (as have my peers.). Maybe I just feel sorry for good employees struggling under bad management. And maybe organizations should get better about promoting people who can manage (i.e. people with soft skills) instead of those who can’t before this technology is adapted.

Anyhow, to wrap up, this whole post has my feeling so grateful for the good managers I’ve had. The ones who got it right. Who listened, encouraged, and provided constructive feedback on all my work. And though I’m sure they’re not reading this post, a shout out to my favorite, amazing managers from two very different jobs: Kirsten and Cathy. They didn’t need an algorithm to understand their team performance and employee happiness. They had communication skills, empathy, and damn good personalities that made working for them a delight.

Can Artificial Intelligence find me a job?

Imagine if LinkedIn had a smart technology that guided you through each step of your job search. Imagine if it could accurately match you to jobs based on your background, conduct a skill gap analysis, and recommend courses to make you more qualified for a job. Imagine if it could pair you with a mentor and recommend conversational topics and questions based on mutual interests.

Admittedly, that’s all a bit of a wish list. But my hopes were up when I saw a IBM College tweet about a new service with Watson. For job seekers interested in working at IBM, Watson will help provide “job recommendations that match your skills and interests.” Watson, the do-it-all cognitive technology, is dipping its non-existent toes into career coach waters. As a career coach who’s spent years helping people figure out which jobs are right for them, I had to give Watson a try.

Interacting with Watson starts off easy. Like any good coach, Watson gives you options. It offers the option to explore common questions, answer questions about your experience, or upload your resume to let Watson recommend opportunities for you. I chose the easiest option, the resume upload, because it’s the laziest.

Seconds later, Watson had a list of job recommendations and the initial recommendations were in line with my background. It recommended three job categories at IBM to explore: Marketing, Consulting, and HR. Each category contained 50 jobs. Watson ranked each job by best match, with an icon indicating how well I matched the job opportunity and an info box showing which skills made me a match for the job. Unfortunately, the job opportunities ranged greatly in experience level, education and responsibilities. Oddly internship opportunities ranked high in my results, though I’ve been out of grad school for 8 years and have 10 years of relevant experience. I assumed Watson would only recommend relevant jobs related to my years of experience.

Feeling mildly overwhelmed with 150 matched opportunities, I returned to the beginning to answer questions so Watson could get to know me better. Watons’s questions were related to my work experience, skills, and passion. After answering all of them, Watson recommended a new category to explore: Design and Offer Management. It was a happy discovery. I’m obsessed with UX and immediately found a cool job for a User Experience Designer for Bluemix Garage, their innovation and transformation consultancy which does work with startup communities around the world. Dreamy.

Watson made discovering opportunities relatively seamless because I didn’t have to have to experiment with keywords or job titles to find jobs that may be a good fit, a challenge most job seekers struggle with. Watson also shows which of your skills matched you to a job. Compare that to LinkedIn’s job recommendations which are frequently odd and a mystery, and suddenly Watson seemed quite helpful.

Unfortunately Watson’s helpful magic stalled as I moved from recommendations to interactive chat. As I explored recommended categories, Watson encouraged me: “While you explore these jobs, feel free to ask me any questions you may have about IBM. For example, I want to know about… to learn about company culture, locations and more.”

So I inquired. “Do you have jobs in Portland?” Watson displayed jobs in my category that were tagged “multiple cities” as well as San Francisco and Austin, etc. Was Watson making a hipster connection? I’m still not sure if they have jobs in Portland.

Then I thought of questions a college student might ask. I asked, “what jobs do you have for college graduates?” Watson replied: Based on your resume, it looks like you would be interested in these job categories. You can learn more about these categories or explore opportunities in each category.

I tried to get more specific. “What internships do you have for college graduates?” Watsons repeated the same answer as above. “What is the best way to get a job at IBM?” Same reply, Based on your resume… I asked “How long is the hiring process?” I was directed to a web page on the recruiting process which had little detail. I asked if IBM has MBA-level jobs. The response was “Super, I found opportunities in one job category” The category: Project executive. I tried to clarify, “Are there roles for MBAs in strategy?”

Watson’s response: “Sorry that area is out of my expertise.” Watson apparently hasn’t met IBM’s MBA team.

With daily articles on artificial intelligence and the power of machine learning appearing in my newsfeed regularly, it’s easy to get caught up in the hype of cognitive technology. I made a lot of assumptions as I started to interact with Watson, all driven by hype. I assumed Watson would show me unique opportunities based on my specific questions. I assumed it would offer insights about working at IBM beyond a link to a corporate webpage. I assumed it understood job seekers better. Watson isn’t there yet. To be fair, this service is in its infancy. IBM notes that Watson is learning and can’t answer all the questions.

I remain optimistic though. Artificial intelligence applied to the job search is a potential that is too good to ignore. Some companies already see the future. WayUp just raised $18 million for their platform which uses machine learning to improve job matches between students in students and employers. Looking beyond improved matching, a smart service that helps people navigate the job search – an anxious, joyless, and time consuming process that everyone dislikes – is enticing. There are not enough human career coaches to assist people through the coming workforce disruption. People need guidance as they think through retraining options and upskilling. A smarter Watson could serve as a virtual career coach and support system to help people navigate an increasingly ambiguous future of work.

I look forward to that day.