Here’s a plug for a practice I’ve leaned on more and more to make sure I’m getting things done, particularly those important-but-hard-to-sit-down-and-do things.
I experience the textbook case of procrastination due to perfectionism. When I have a vision for how I want something to turn out, it’s often overwhelming for me to start or at least keep it going. The Pomodoro technique totally wipes this out.
Here’s a summary of the Pomodoro technique. Many advocates advise not using the timer on your phone but I always have with no issue. I also don’t stick to the rules about how many Pomodoros to do before taking a break. It’s rare that I find a block of clear time longer than 1-2 hours in my day.
This is why the Pomodoro technique works so well for me:
- Offers a low barrier-to-entry to just getting started. Rather than clearing my desk, reaching inbox zero and having a snack before starting a project (all sneaky ways to procrastinate!), I just start the timer and work. It’s only 25 minutes!
- Captures actual effort exerted. Recently I lamented that a project was taking the whole day. Then I realized I had only done two Pomodoros, with the rest of my time going to two phone meetings, a coffee and lunch. So in reality I had only worked on that thing for less than one hour. A much needed reality check!
I recommend trying it out, especially if you’re prone to procrastination.
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.
Google has great resources for learning more about AI, both for developers and businesspeople: ai.google.
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.
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