The algorithm will manage you now

“Workers increasingly see assignments and wages doled out by artificial systems rather than human managers, and have to rely on AI, not HR, when things go wrong. According to tech experts, the rise of algorithms is changing not only how we earn a living, but who gets access to jobs and other opportunities — if their data checks out — or not.” – Forbes, Algorithms And ‘Uberland’ Are Driving Us Into Technocratic Serfdom

I rarely link to Forbes pieces because their ad game is excessive (even with my ad blocker) but the quote above captures the workplace transformation quite succinctly. From spying on workers, to replacing managers with AI, to using questionable data and AI insights to determine who gets hired, the world of work is changing in ways that need examining fast.

The Forbes article was referencing the book UBERLAND: How Algorithms Are Rewriting The Rules Of Work, which has just rocketed to the top of my reading list. Until then, I’m definitely looking out for the author on the podcast circuit.

 

It was only a matter of time

Students are looking for ways to beat AI recruiting tools like HireVue. And now coaching services are offering help:

“A start-up called Finito claims it can coach candidates to beat AI for as long as it takes them to get a job — but at a total cost of nearly £9,000. Candidates are steered through interview dry runs and get tips on what skills are needed to get past robot selections, in sectors including finance, public relations and the arts. They then watch footage back to spot foibles that could be flagged up as nerves.”

Add helping students beat the AI recruiting process to the list of things career services needs to upskill.

beat AI recruiting

The algorithm will hire you now

AI Hiring

A snapshot of opinions on HireVue on Reddit

 

It appears the use of AI in the hiring process is finally hitting mainstream awareness. The Wall Street Journal just released a video report about the role of artificial intelligence in the job search. As part of their Moving Upstream series that explores new trends and technologies, the WSJ investigated two companies that use artificial intelligence to decide if you get hired: HireVue and DeepSense.

The video is worth watching, especially if you’re in the job search or working in career services.

The video begins with an introduction to HireVue, a platform that uses machine learning to assess and rank users on their video interview performance. The video provides an overview of the scoring process and the science behind their facial analysis software from HireVue’s chief psychologist. The company uses millions of data points taken from a candidate’s facial expressions, language choice, and tone of voice to measure and determine a candidate’s fit for a job.

There’s a notable part of the video when the journalist asks the psychologist if all interview videos are reviewed by a human. The psychologist chooses his words carefully, noting that recruiters could watch all the videos if they wanted. But we all know that’s not likely. HireVue exists to make the interview process more efficient. Their product is marketed as a way to save time. It’s not efficient if recruiters have to watch every video.

Later in the the video we meet a college student. He estimates that almost half of his interviews have taken place on HireVue. He’s not a huge fan because he thinks it’s hard to show his true self in video interviews.

There’s likely another reason he dislikes it: Interview preparation requires hours of preparation. Thinking on your feet and providing authentic, yet impactful responses, takes a lot of work in the interview process. It’s hard enough knowing you have to impress a human. But knowing a human many never hear your answers is disappointing. It’s the resume black hole on steroids.

The video report includes some welcome skepticism towards new HR tech from Ifeoma Ajunwa, sociologist and law professor at Cornell University. When asked about the validity of microexpressions, she explains:

It’s still a developing science. The important thing is, there is no clear established pattern of what facial expression is needed for any job. Applicants can be eliminated for facial expressions that have nothing to do with the job.”

AI is Changing the Entire Hiring Process

Artificial intelligence isn’t just changing interviews. It’s changing how candidates are hired at every stage of the hiring process. The WSJ video goes on to profile Deepsense, an AI platform that builds a behavioral profile for every person. The company creates a behavioral profile based on social data taken from publicly available data from sites like Twitter and LinkedIn.

The DeepSense AI process

Then they use the data to “run scientifically based tests to surface people’s personality traits.” In a separate article, the cofounder and CEO of Frrole (which developed DeepSense), notes: “One thing people don’t realize is that how little data is required to start making deductions about you, and probably correct enough.”

AI hiring HR Tech

Screenshot of Deepsense dashboard from WSJ video report

Probably correct enough. That’s tough to read when the stakes are so high. The job search is an emotionally exhausting process. Job seekers have families to support, dreams to achieve, health insurance to secure, and bills to pay. They expect to be evaluated fairly and accurately. Probably correct enough isn’t enough in a high stakes situation.

Currently a big five consulting firm is using their service.

The potential for discrimination and bias with new HR technology is high. How do you ensure your public data is correct? How do you challenge the methodology behind the collection/selection of that data? How do you know if you’ve been discriminated against if it’s all done by algorithmic decision?

Beyond the potential for discrimination and bias coded into algorithms, there’s another disturbing bit of information from that video: job seekers may not know they’re being evaluated by an algorithm. As the WSJ reporter notes:

“I go into this knowing something that HireVue acknowledges many job candidates potentially do not. That my responses are being assessed not by human beings, but by AI, analyzing my tone of voice, the clusters of words I use, and my microexpressions.”

Do people know that every post, article, tweet they put on line can now be analyzed and scored as a basis for hiring? These questions, and plenty more, urgently need answers as companies implement new hiring technology.

These are the jobs of the future and they’re already here

What are the jobs of the future and when will they get here? The answer is now.  Mya Systems makes a chatbot that conducts interviews. They work at the cutting edge of Natural Language Processing and are making waves in HR Tech spaces. (full disclosure: I contract with them to design chatbots). They’re also hiring for cutting edge jobs like this one: Language Annotator. It’s a contract role for a current student, ideally someone in the liberal arts!  They’re looking for a student with literature or philosophy background with strong communication skills and an understanding of machine learning. Bonus if they’ve got foreign language skills. This post touches my machine-learning-obsessed-and-liberal-arts-loving soul.

The job:

The jobs of the future are hybrid jobs. Hybrid jobs combine soft skills with digital skills. You’ll find hybrid jobs through out the job listings; popular hybrid jobs right now are product managers and data translators.

These are the jobs we need to train students and alumni for in order to prepare them for an automated workforce. The future of work is already here.

jobs of the future

Your employer is probably spying on you

FAQ from Teramind, a software that records, logs, and monitors employees.

Corporate America enjoys spying on its workers. According to Wired, “94 percent of organizations currently monitor workers in some way.” Even worse, you likely can’t escape it. From The Creative Ways Your Boss is Spying on You:

Try to hide from this all-seeing eye of corporate America—and you might make matters worse. Even the cleverest spoofing hacks can backfire. “The more workers try to be invisible, the more managers have a hard time figuring out what’s happening, and that justifies more surveillance,” says Michel Anteby, an associate professor of organizational behavior at Boston University. He calls it the “cycle of coercive surveillance.” Translation: lose/lose.

Last year I wrote a post called, AI is going to make your asshole manager even worse. Nothing I’ve read since then has convinced me otherwise.

Is it appropriate now to inquire during the interview stage ask what technology the company uses to spy on workers? If not now, when will it be appropriate?

Also, who monitors the executives? Who monitors the monitors?

The quality of your head movements will help determine if you get hired and I’ve got nothing but questions

Yobs.io isn’t the first HR tech company to promise better candidate selection technology through AI and predictive analytics. HireVue has been using algorithms to review and assess video interviews for companies like Unilever and JP Morgan, and they’ve got $93 million in funding to do it. AI technology is rapidly changing the job search.

Yobs.io, however, positions itself as a platform that can identify a candidate’s soft skills and improve team dynamics. Their tech implements “quantiative soft skills analysis in the recruitment.” It claims its platform “determines the emotional state of your candidate which reflect the real-time soft skills that they will take to the job everyday.” Their algorithms analyze facial expressions, word choice and tone, and even head speed to predict candidate success in an organization.

I find it hilarious that employers are banging the drums about the need for employees with soft skills yet they’re increasingly willing to hand over the process of selecting people with those same skills to a machine.

I work on interview chatbots and conversational AI in my contract work. I find it fascinating. I enjoy watching the algorithm improve and seeing its limitations. However, technology that uses personality assessments and predictive analytics to make hiring decisions fills me with questions. They’re questions that I rarely see addressed in tech media or HR industry coverage. They’re questions in need of answers that aren’t marketing copy.

Just look at that engagement level! Source: Yobs.io website

Here’s the ongoing list of questions I never see answers to:

How are companies evaluating whether hires by AI are better than human-led hires? Is this technology trusted for use in all hires, including executive management? Moreover, do the AI engineers have the soft skills they’re designing algorithms for? Does it matter if they don’t? Do the managers who oversee the implementation of this technology also have the soft skills they seek?

Also…

Why should my head speed be part of my interview evaluation? How much weight is my head speed given in the algorithm? What is a quality head speed and how does it affect my ability to do a job that I’ve trained for? Who decides what interview tone is appropriate? Would a monotone AI engineer with an abnormal head speed, a high rate of neuroticism, low rate of extraversion be an acceptable hire (trick question, of course they would, they’re the most in-demand occupation)

And…

Who loses out on an opportunity during the tuning phase of the algorithm? Algorithms don’t work perfectly out of the gate. What feedback loops exist inside the organization’s that use this tech to ensure they’re not getting false negatives? How do HR tech companies who claim to reduce bias prove they actual reduce bias rather than reinforce it?

Humans are flawed. But so are algorithms and even the data we use to build them. Just because it can be measured (head speed) doesn’t mean it needs to be. Asking the hard questions about new technology is important, especially in high stakes situations like job interviews and career progression.

Also, I’m parking this fab find here: Yobs.io uses the big 5 personality traits (OCEAN) to predict candidate fit. There’s a fabulous overview of the Big 5 that includes psych student videos explaining the big 5 concepts. Highly recommend watching these videos, especially when they discuss the person-situation debate.

Your job search is becoming less human. Here’s how to adapt.

Imagine you’re a job seeker looking for work. You submit your resume to a company’s website.

Your resume is scanned by AI that evaluates your resume against the job description. Then it compares your qualifications to a database of current employees’ qualifications. The algorithm also pulls in some publicly available data about you, like your social media profiles. It scores you based on that data and your resume. Your score puts you above the competition. Your resume isn’t reviewed by a recruiter.

Next you get a text on your phone. It’s the company and they’re asking if you have time to answer a few questions. You answer a few basic questions about your professional experience and interest in the role. You realize it’s a chatbot half way through but you’re just happy to avoid the awkward phone interview.

You make the cut again. You receive an automated email with a link to an online video interview platform and instructions. You record your answer to the interview questions. It’s awkward to stare at yourself on the screen. There are no visual or verbal cues to see how your answers land. Your responses are recorded. An algorithm analyzes the video, reviewing your micro expressions and looking at 25,000 possible data points to evaluate your personality and fit within the company. Your video response is scored by the algorithm.

Then you get an email from the recruiter. You’ve passed all the steps. They’d like to invite your for a day in the life experience at their company.

The visit is the first and last opportunity you’ll have to interact with a person in your entire job search.

Back to reality. The scenario above isn’t totally hypothetical. It’s reflective of the current hiring process evolution. Companies are increasingly adopting HR tech that uses AI to automate the hiring process and make it more efficient. For example, here’s what hiring looks like at Unilever:

Candidates learn about the jobs online through outlets like Facebook or LinkedIn and submit their LinkedIn profiles — no résumé required. They then spend about 20 minutes playing 12 neuroscience-based games on the Pymetrics platform. If their results match the required profile of a certain position, they move on to an interview via HireVue, where they record responses to preset interview questions. The technology analyzes things like keywords, intonation, and body language, and makes notes on them for the hiring manager. All of this can be completed on a smartphone or tablet.

If the candidate makes it through these two steps, they are invited to a Unilever office to go through a day-in-the-life scenario. By the end of the day, a manager will decide whether they are the right fit for the job.

A fundamental shift in hiring is under way and it’s powered by machine learning. From resume screening by AI to interview chatbots to predictive analytics that determine who’s most likely to leave a job, the list of startups transforming the hiring process is long. Over half of HR tech investments in 2017 went to companies offering products and services powered by AI. Companies like Entelo, an AI recruiting platform, use machine learning to determine whether you’re a fit for an organization. Entelo’s knowledge base provides a few hints on how the AI will evaluate you:

The shift to automation is making the hiring process less human. As a job seeker it’s not always obvious when AI is used as part of the hiring process. You might not know if your professional qualifications are being evaluated by a human or an algorithm. To stay competitive as the hiring process evolves job seekers need to stay informed and adapt as new HR technology enters the market.

Here’s how to start.

Get curious about HR Tech

Explore the range of new HR technology that’s being used in the hiring process. Get curious about how these tools are used. Then experiment with new HR technology that also helps job seekers. Tools like Jobscan and VMOCK are valuable resources that use machine learning to help your improve your resume. There’s even a promise of a chatbot to help you navigate your career.

Next, research which companies are using machine learning for hiring so you can prepare accordingly. Right now big companies with large resume volumes are the ideal automation customers. Smaller businesses and startups aren’t using them as much yet. Some HR tech products list which companies use their services. Before you apply to a job, email a recruiter or ask a current employee about their hiring process so you know up front whether you’ll be engaging with a machine or a human.

600+ companies in 140 countries use HireVue.

Be prepared to go beyond resumes

The resume isn’t going away any time soon but the application process is evolving to evaluate you on more than your resume. Instead of submitting a resume, candidates are taking part in hiring assessments like Pymetrics, a collection of that neuroscience games that “collect millions of data points, objectively measuring cognitive and personality traits.” Tools like Entelo assess your social media data as part of the application process:

AI Recruiting on Entelo

Creating professional content so the HR bots can find and evaluate you could make you a more competitive candidate than a resume alone. Start by producing small bits of content online. Create a personal website, show off a portfolio online, write short blog posts, or share articles on Twitter related to your professional interests to be seen by the bots.

Ask hard questions about AI and HR technology 

There are plenty of ethical questions we need to ask about AI and reinforcing bias in recruiting. Job seekers can contribute by asking hard questions too. Sometimes it’s as simple as asking how.

How do algorithms score candidates? How are candidates screened out of the process? How do candidates rank if they don’t have online profiles or publicly available data for algorithms to find? How would a candidate beat the AI system? How much do hiring managers trust their AI recommendations and scoring? How do these platforms reinforce existing bias?

Then ask yourself the hard questions: Are you getting all the information you need in the hiring process – company culture, opportunity for growth, management styles – to make an informed decision? Does an automated candidate experience make you more or less likely to want to work for a new company?

Become an actor

One question they get frequently, said Lindsey Zuloaga, director of data science at HireVue, is if an applicant is able to trick the A.I. Her answer: “If you can game being excited about and interested in the job, yes, you could game that with a person as well,” she said. “You’re not going to game it without being a very good actor.”

Employers seek candidates with strong soft skills. As more employers delegate emotional intelligence screening to automated tools you need to ensure you’re expressing that emotional intelligence. Start by recording yourself so you know how you look, talk, and express yourself on screen. Pay attention to your tone, body language, and facial expressions. Learn how to build your soft skills to improve your emotional intelligence. Spend more time interacting with people to improve your communication skills outside of digital environments. You might even want to take some acting or improv lessons to get comfortable showing those necessary emotions.

Cultivate those professional relationships

Will recruiters eschew a recommendation from a human in favor of their AI scoring system? Do AI hiring platforms incorporate internal recommendations into their scoring model? We don’t know. So for now we can assume that internal referrals via professional relationships might be a way to beat the algorithms (or at least, get around it). More importantly those professional relationships take on greater importance the more automated the hiring process becomes. Conversations with people inside of companies give you valuable insights. Discussions with current employers also give you a feel for company culture and management style, making up for the insights you lose in an automated process.

Sharpen your persuasion skills 

We’re not in a fully automated hiring process (yet). Job seekers still have a chance to engage with humans during their search. But the hiring process is evolving and making some career advice outdated. When you finally get in front of an employer it might not be what you expected (i.e. those behavioral interview questions you memorized might not be as relevant in the future). But one thing won’t change: once you engage with a human you still have to persuade them that you’re the best person for the job. Your job search has always been an act of persuasion. That much hasn’t changed. After you learn the new automated systems focus on building your persuasion skills. Reflect on what the companies needs and how you meet that need. Learn how to tell an engaging professional story that connects your interests to your future team’s needs. Show employers your intellectual curiosity and passion as you ask questions about the role. Seek out new conversational opportunities so you get better at engaging with people from different backgrounds.

We all need to pay attention to the way hiring is changing. With millennials looking at a lifetime of job hopping, we’re going to have adapt fast to new hiring processes. The traditional way of doing things won’t always work. As this article so cleverly points out:

“those first impressions so carefully emphasized by career coaches are now being outsourced to artificial intelligence.”

How much should this AI Chatbot Writer job pay?

Hybrid jobs are all the rage currently and are some of the top paying jobs in the market right now. If you’ve got soft skills, business acumen, and technical skills, you’ve got the ticket to a high paying job.

Hybrid roles are super interesting to follow because they are so new. Their descriptions and responsibilities differ from one organization to another. This is particularly the case with AI interaction designers, a emerging job category I’m paying a lot of attention to lately (in part because I’m slightly obsessed with chatbot design as of late.) Diane Kim, who designs the friendly virtual assistant bot at x.ai, summed up this emerging field in her interview with Wendy and Wade, a career advising chatbot:

“The fact that AI Interaction Design is so new gives me the freedom to be experimental. I also have the unique opportunity to be part of defining an entirely new field. This is actually both what is most exciting and most challenging about my job…But it’s challenging because none of us really know what this is yet — we’re all figuring it out together. It’s really different from, say, being a recent grad in your typical UX role for a visual interface, with decades of research and best practices to follow. We don’t have the same industry standards or guidelines yet for conversational design, but the fun part is figuring them out as we go.”

So it’s within that context that I examined this AI chatbot writer role from JustAnswers.

Chatbotjob Chatbotjob

The skill requirements on this role are massive. Let’s break it down.

  • You need quantitiatve and qualitative skills
  • You need to be a seriously good at writing (perfect tone!)
  • You need to understand Sales (identify (and contribute to?) revenue opps!)
  • You need be an experimenter – test and retest
  • You need mad research skills
  • You need the collaboration skills to work with diverse teams
  • You need to understand user experience
  • You need to dive into professional fields that requires years AND be required to anticipate which quesitons users would ask AND write the answers.

This is one hell of a robust skill set. That last ask – expert with diving into deep professional fields like medicine and law – really threw me off. Who is this person? And will you pay them a shit ton of money for this expertise and skill set?

It’s likely this job is like most job postings: crammed with all the ideal things. There is probably flexibility – an applicant doesn’t have to have all those things.

I’m curious about how much this role pays because writing is an underpaid profession. Some managers who don’t write assume it’s easy – after all they write emails and reports! Copy is everywhere and people assume it’s easy to produce. Thoughtful copy – the kind that strikes the perfect tone! – takes time and creativity to produce. People in quantitative fields tend to overlook that.

But bad writing, especially in AI conversation design, leads to awkward interactions with the product. For example this was my recent convo with a new recruiting bot Robo Recruiter:

If writing is underpaid but AI is a hot hot hot field, how much should we be paying our AI chatbot writers?

I’m crowdsourcing your answers below in the comments: how much do you think this job pays? Do you think it pays as much as a machine learning engineer? As a product manager?

Write your answer below.

Then see what Paysa pegs the going salary rate in San Francisco.

 

How to learn about ML/AI if you don’t have tech skills

Art by AI

I’m a liberal arts grad. I love words and language. I teach soft skills. Qualitative data is my jam. I’m also obsessed with machine learning (ML) and artificial intelligence (AI).

In 2015 I tumbled down the AI rabbit hole after discovering a long read on the fabulous site Wait But Why. The site explains complex ideas paired with hilarious stick figures. The two part series on AI, The Artificial Intelligence Revolution, was my gateway article to the world of AI, and later ML as part of AI.

So far my self-directed learning journey has only included reading about AI and writing about its affect on hiring and the future of work. I can’t code in Python (with zero plans to do anything with R). My data background includes data analytics, cleaning data, and putting it into Tableau but nothing close to data scientist. I also have no interest in going that far professionally. As a non-tech person trying to access ML/AI, it’s been a challenge to figure out where I fit in. I’ve uncharacteristically avoided meetup groups or conferences on the subject since I don’t have the tech skills.

Not me.

Last month I changed that. I got tired of reading. I wanted idea exchanges. So I attended a ML/Al unconference in PDX. And hot damn I found my people!

An unconference is the opposite of the standard conference setup. Instead of corporate-sponsored keynotes paired with bland chicken and an abundance of shy speakers who read PowerPoints, the participants chose the content. We pitched and voted on what they wanted to talk about. The result was facilitated conversations about subjects we were curious about and a format that flowed. It was the ideal setup for idea exchange and learning. If you’re conference weary an unconference will restore your faith in professional development.

Many people at the unconference were data scientists or computer scientists, and some working on ML projects. A few were students or job seekers. I met one other person who is like me, a communications expert without a technical background who works for a machine learning platform, BigML (and they’re doing rad stuff).

In our sessions we covered a roving range of topics about ML/AI: novel data sets, making AI more accessible to the masses, establishing trust with users, data security, AI decision making re: self-driving cars and the Arizona accident, becoming a data scientist and machine learning engineer, the future of companies and jobs (my pitch!), learning ML/AI as a new person (do you learn the math, the code, or find a project first? plenty of debate on this!), and plenty more side conversations that spilled out of the main sessions.

As an non-tech outsider it’s a bit intimidating to participate in such a cutting-edge tech space. I think ML/AI people forget that at times. One of the guys I met at the conference noted that when you’re an expert it’s hard to remember how hard it is for others to start in your field. I’ll add that this goes double if you’re in a quant and code heavy field like machine learning. Luckily most everyone at the unconference made it easy to participate (as did the unconference format).

My main takeaway though is that you don’t need to be a software engineer, data science expert, or code wizard to understand ML/AI.

So for all the people who are curious about ML/AI but don’t know how to start engaging in these communities, here’s how. 

Learn the basics: Know the difference between machine learning and AI; understand the difference between Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence; understand the basics of data science. There are no shortage of intro articles and videos on the subject (two examples below).

Here’s a helpful Quora answer about the differences between a data scientist and a machine learning engineer. 

Prior to the uconference I was slightly worried I’d be left out of the conversation if it turned to technical. I prepared by returning to a set a YouTube videos I’d skimmed a while back: Fun and Easy Machine Learning. The YouTube list animates over 15 models to better understand machine learning.

Ignore the math and coding right now: Unless you want to become a data scientist or machine learning engineer, ignore it. You don’t need it to understand the basics or to explore products or impacts of ML/AI. For example, the Fun and Easy Machine Learning series sometimes dives into the math behind the models. Treat it as you would a foreign language; when you don’t the meaning keep moving forward and focus on what you do understand. Fill in the blanks later.

Read everything about ML/AI in the area you’re interested in. ML/AI for non tech people is a huge field. So narrow it down. Start with general articles about artificial intelligence and learn about it’s expected impact. The World Economic Forum has good articles with a global perspective. For business impacts, check out this history of ML/AI technology by industry/verticals. Then head over to CB Insights to study ML/AI companies (and subscribe to their newsletter as they’re cutting edge everything). Then pick an industry that interests you. Either one that you work in or one that you want to work in. Read everything you can about how machine learning is affecting that industry (it’s affecting all of them – right now finance, healthcare, and insurance are some of the industries talked about the most.) Explore products and platforms in that industry that use ML/AI. Read case studies. I study the future of work. So I read everything I can about ML/AI and it’s affect on workers and organizations: McKinsey, AXIOS, MIT, plus I play with HR Tech.

Avoid the hype. It’s easy to get caught up in the shiny promised of AI. Instead, pay attention to counter narratives, often published outside of the tech reporting ecosystem. Find the counter narrative about AI in your field. I read the amazing research and work by Audrey Waters at Hack Education for a counter narrative to AI edtech hype. Explore bias in ML/AI. Understand how AI isn’t neutral and that gender and race bias is coded into AI systems. Weapons of Math Destruction is an excellent book (and 99% Design has a good podcast on it). We need diverse perspectives and people in ML/AI fields to fight these bias, and non-technical people are part of that fight. 

Take a course: FutureLearn, an online learning platform with a name after my own heart, offers an Intro to Data Mining course where you’ll learn the basics of classification algorithms. It’s a smooth intro to applied machine learning. They also offer an advanced course to build your skills further.

Go to an event and talk to people: This is the intimidating part. But get over it, embrace the awkwardness, and commit to asking curious questions. Remind yourself of the things that you know. Write down the things that you want to learn. Talk to people until you get the answers to your questions. Ask people how they got into their work, what impact they’re having, and how they’d explain their work to a non tech person. Tell them you’re curious. Some people will just talk at you. Others will teach you. Keep in touch with the people who teach you and simply move on from the ones who talk at you.

Get a project: This builds on not worrying about the math and coding. Instead, get a project. What problem do you want to solve? What problem does your organization need to solve? What data is available? What data is missing? How could ML/AI solve your problem? Starting there will help you lead you in the right direction. You might not have an answer right away. That’s ok. It make take a while to solve it. But that’s the point. You’re learning. Ambiguity is part of the process. So ask around your workplace. Visit the data science or computer science team in your organization (assuming you have one). Find a data scientist in your network or at ML/AI events and ask them how they’d solve your problem. Ask them to break it down. Ask a computer science student what they think.

Start with curiosity, ignore the part about not having a technical background, and see where it takes you.

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.