How AI will impact the future of work

All February I’ve studied how AI can influence Human Resources, but a parallel and very interesting topic is how AI will impact the future of work. Here’s a prediction from HR Technologist on some ways AI will change the workplace:

  1. Recruiting. I’ve looked at this extensively this February as this is professional background.
  2. Internal communications and interactions across languages. I recently spent a work from home day alongside a friend in technical customer service. She was responding to questions from the product team in Japan and using Google Translate as the intermediary. As she said, it wasn’t perfect but it got the job done and she was able to resolve their issue across languages. This is becoming a built-in feature for employee collaboration.
  3. Streamline training and onboarding. AI can provide coaching tips in real-time. Think Gmail message auto-complete for work performance.
  4. Offer more robust problem-solving support. Beyond simplifying, AI can offer a wider view of potential solutions and approaches.
  5. Drive productivity. AI can automate tedious and repetitive actions of the workplace – meeting scheduling and review, answering common questions.
  6. Push for new regulations. Many of the areas that AI will touch are not well regulated. This will need to change as workers engage with it regularly.

AI and the candidate experience

It’s exciting to see the ways that AI can enhance recruiting capabilities, but just as important – and potentially more so – is how it impacts the job candidate’s experience. An article from last year on CNBC highlighted one candidate’s feelings of distance when interacting with an AI recruiting tool, in this case Hirevue: “It felt weird. I was kind of talking into the void.”

Anecdotally, I’ve heard a variety of reactions, more increasingly positive. As interacting with AI tools becomes a more common experience during the recruiting process, people become more comfortable with the idea that part of the process will be automated. I also suspect some generational differences; Millennials notoriously hate phone calls, and many of these AI solutions approximate a text conversation or a video chat.

A great approach to exploring these solutions is to look at the data. Are candidates less likely to continue with the recruiting process when presented with an AI tool? Do they rate their experience lower once these solutions are put in place? Individual companies can track this for themselves with some simple data collection.

Public policy and AI

One of the largest AI conferences is NeurIPS, which happens annually in December. I spent some time browsing the presentations from the 2018 conference and found an interesting presentation by Edward W. Felten from Princeton University titled, “Machine Learning Meets Public Policy: What to Expect and How to Cope.”

Felten kicks off his talk by highlighting the messaging that people in public policy are hearing about AI, and overwhelmingly, it is a warning to put regulations in place. People like Henry Kissinger and Elon Musk have already sounded the alarm to policy makers.

His thesis is that the best policies will come out of technical people partnering well with policy makers, with both sides trusting the others’ expertise. This comes from being engaged and constructive in the policy making process over time.

It was interesting to see push back on this thesis by some attendees. One counterpoint was that many industries have self-regulating bodies, such as FINRA, and that this could be an option for machine learning. However, Felten pushes back on this because self-regulating bodies work well when they have public accountability, and they are easily replaced when not accountable.

Responsible AI

Ethical concerns need to be at the forefront when implementing AI tools. Thankfully, people are discussing this and organizations have drafted best practices of responsible, ethical AI. Given the multitude of applications of AI, there are many issues to consider when thinking about responsibility. Focusing on HR, some clear concerns emerge:

  • Inherent bias in the training data. AI learns from the data it is fed. Amazon ran into this issue when exploring AI for recruiting.
  • Transparency in the hiring process. Companies need to be able to explain why they selected a subset of candidates for interviews, why they discarded some applications without viewing them… When AI is deployed, companies will still need to explain these decisions and will need to understand what the algorithm is targeting.

Google AI drafted recommended practices for building AI that captures guidelines for building software with specific guidelines for machine learning:

IBM Watson, five years later

In 2014 I wrote about my excitement over a demo of IBM Watson. I have recently heard about some familiar consumer tools that are using Watson and I revisited my post to see what’s changed over five years.

There are great case studies on IBM Watson here. It’s no surprise that this technology has gotten a lot of traction over the last five years.

One caveat I had when reviewing this tool in 2014 was that the data is only as good as how it’s communicated, and this still stands. Many analytics tools are doing a much better job of showing the quality of the underlying data and how reliable the predictions are, but there is still a level of understanding that is required when handling big data sets.

Fairness in AI

Google has great resources for learning more about AI, both for developers and businesspeople:

Through this site, I watched a lecture by Margaret Mitchell on fairness in AI. There are many stories about unintended bias in AI tools. A recent article about Amazon’s challenge with this made a lot of noise in the HR community. There are different types of human bias that can manifest in data:

  • Reporting bias: People report what they find interesting or notable so data doesn’t reflect real-world frequencies
  • Selection bias: The training data for machine learning systems is not a random sample of the world but instead things we find interesting
  • Overgeneralization: Conclusion is made based on limited information or information not specific enough
  • Out-group homogeneity bias: We assume people in groups we don’t interact with every day are more similar to each other than those in our in-group
  • Confirmation bias: Tendency to search for, interpret and favor information that confirms our own pre-existing beliefs and hypotheses
  • Automation bias: Preference for suggestions from systems, as if they are somehow more objective than other sources, like humans.

There are methods for designing in fairness in machine learning but this must be intentional – AI is not inherently unbiased.

Sourcing with AI: HR tech companies in the space

And now, the convergence of my two areas of interest: recruiting and AI. The companies in this space, many of which are startups, are finding novel ways to apply AI to the recruiting process. I’ll break these out into a few categories, reflecting the stages of recruiting: sourcing, assessment and candidate experience. Today I’m highlighting sourcing.

Sourcing is a natural fit for AI because it’s an expensive activity for recruiting organizations and there is so much data available on potential candidates.

In traditional talent sourcing, a recruiter (or sourcer) looks far and wide across a population to find relevant talent for an available job. Once a qualified person has been identified, the recruiter then attempts to engage this person to see if they will consider the job. There are a few obstacles here: first, the pool of potential talent may be very large and difficult to comb through; second, it may be difficult to identify best-fit candidates, and third, it may be difficult to engage or find people willing to engage.

AI is a great fit for AI because it’s a data-rich activity. Across the web, there’s social media profiles, participation on forums, articles and white papers. Content that could flag someone as a relevant fit for a job is nearly limitless. And within companies, there is plentiful data as well. Data on existing employees can suggest what skills work well for roles, and efficient mining of previous job candidates can lead to a future hire for a different job.

Here are a few companies applying AI to sourcing activities:

  • LinkedIn: A leader in recruiting technology. LinkedIn Recruiter is a popular tool for recruiting organizations and AI is at the heart of the recommendations to recruiters when they are searching for new talent pools
  • Entelo: A startup that applies predictive analytics to identify those most receptive to a job opportunity
  • Restless Bandit: Analyzes resumes within a company applicant tracking system to match top candidates to open roles

Fun AI: Iconary

I’m ending the week on a light note with an AI game I found. Iconary is a Pictionary-like game developed by AllenAI in Washington. I had a lot of fun drawing and guessing and it’s surprising to see how closely my perception and guesses match my AI opponent, Allen.

This is the kind of thing that is so tricky for AI – reading meaning into symbols. AI can recognize a tree as a tree, but can it recognize a group of trees as a forest? This one can!

Here’s a great write-up on the game and the impressive AI behind it on TechCrunch.

How AI can revolutionize HR

My #AIFebruary project is focused not just on learning about artificial intelligence, but also its applications in my field, HR and recruiting. With that in mind, I enjoyed these thoughts from the Forbes Human Resources Council from July. Members were asked what a future with AI might look like in our field. Some top answers:

Enhance efficiency. Stacey Browning, President of Paycor, advocates for humans and technology working together to scale a high-touch and responsive recruiting process.

Automation and a human touch don’t have to be mutually exclusive. Strategically combining them can deliver unrivaled results. In recruiting, automation’s infinitely scalable levels of efficiency mean that, regardless of the volume of candidates, each receives a timely correspondence. For candidates, being kept in the loop with a thoughtful and sincerely worded email is what makes the difference.

Stacey Browning

Reduce bias. Sherry Martin of the Denver Public School system highlights how assessments can be analyzed for bias in language and outcomes and adjusted over time to minimize adverse impact, ideally leading to a wider variety of job candidates.

Simpler sourcing. Sourcing is a popular aim for up-and-coming AI tools, and Heather Doshay at Rainforest QA talks about the impact of improving the ability to match candidates to jobs.

Sourcing is a time-intensive pain point for most talent professionals, and providing well-matched candidates to companies would significantly speed up the top of the recruiting funnel and increase the quality of hires.

Heather Doshay

Replace administrative tasks. This comment from John Feldmann at Insperity Jobs groups together the time-consuming but critical tasks that are part of nearly every recruiting process.

AI will be valuable in automating repetitive recruiting tasks such as sourcing resumes, scheduling interviews and providing feedback. This will allow recruiters and HR managers the opportunity to focus on strategic work that AI will most likely never replace, such as connecting with top talent, providing a more personalized interview experience and establishing training and mentoring programs.

John Feldmann

Stay compliant. Compliance is a critical concern in recruiting and Char Newell thinks that AI could help automate this aspect of the workload for recruiting organizations.