Welcome to Polymathic Being, a place to explore counterintuitive insights across multiple domains. These essays take common topics and explore them from different perspectives and disciplines and, in doing so, come up with unique insights and solutions. Fundamentally, a Polymath is a type of thinker who spans diverse specialties and weaves together insights that the domain experts often don’t see.
Today's topic looks at the hiring process and the tools, techniques, and tactics that are used in order to screen candidates and how these can lead to actually terrible behaviors. We’ll look at some of the current trends, how technology is being applied that drives this, and recommend a few new ways to look at hiring the right people with the right behaviors.
Picture this: You’re in a meeting with your team, asking for data to make a crucial decision that can affect the long-term success of your business. You want the highest degree of accuracy, you want the truth, and you want to know where you might be missing something.
But what if I told you that your hiring process just might be ruining your chances of success?
Over the past months, I’ve been offering pro-bono coaching for those affected by the tech layoffs through Polymathic Coaching. As I’ve worked with clients on finding their footing, maturing their systems thinking, and highlighting their unique value, it’s my observation that the current automated and AI-enabled applicant tracking systems (ATS) screen on at least two problematic characteristics.
These systems bias against systems thinkers, driving toward very specific, very precise examples on a task-by-task basis and not from a larger systems perspective.
They invite quantified achievement inflation. Quantification is important but it works well in very discrete or very high-level positions. It doesn’t work well in the middle where collaboration and systems thinking are about network effects.
We’ll break each of these down in the next two sections and then weave them back in to rethink how we look at these processes.
Bais against Systems
As we explored in Eliminating Bias in AI/ML, our automation systems are nothing more than reactive, human-coded mathematical bias on top of dirty data. If we aren’t careful we quickly begin a Catch-22 where a system screens for certain, potentially biased, keywords and people learn the algorithm and start to place those keywords into their resumes. To further discern, the coders tweak the algorithm to become more specific, and shortly, the resumes follow suit. Soon you have two problems.
All the resumes look the same
They penalize systems solutions by forcing discrete examples.
The first is a problem within your organization of identifying the true talent. When everyone looks the same, designed to pass an algorithm or specifically align with a job description, are you actually seeing the person? If you are relying on automated filtering systems, you are going to get cookie-cutter applications because your algorithms will downgrade the scores of those who don’t play the conformity game. As we learned in Embrace the Divergents, you are likely screening out those who can actually bring innovation to your organizations!
The second is a problem that penalizes systems thinkers. The website Resume Worded is a great example of this and tells me to remove buzzwords and cliches:
I mean, couldn’t we all do without a lot of buzzwords? The issue is that the buzzwords it wants me to remove happen to be the key descriptors of polymaths and systems thinkers:
Add to that what they call ‘weak' action verbs of “coordinated, supported, facilitated, matured, advised, etc.” and you’ve effectively pushed all of your candidates toward hyper-discrete, task-based results that penalize systems thinking and collaboration. Considering our challenges are becoming increasingly multi-variate, cross-disciplinary, and complex, a push toward the discrete is completely counter-intuitive. This problem is further exacerbated by achievement inflation.
Part and parcel to the bias against systems thinking is the problem with achievement inflation. This starts from the valuable foundation that we should be able to write bullet point descriptions of the work we do that are concise, descriptive, and quantified. The problem comes when it turns into a competition for how far you can push the envelope. It starts with the classic ‘Garbage Collector’ transforming into ‘Sanitation Engineer’ and then continues to grow and evolve.
In full disclosure, I’m very skilled at helping people phrase their resumes better and yet when I look at ATS results and feedback from automated systems like Resume Worded I’m pushed to add even more quantification. (Figure 2) This feedback is from my latest test resume where I have quantified numbers on 22 out of 33 bullet points. And we aren’t talking insubstantial numbers as many programs I’ve impacted have been in the multi-millions of dollars.
Yet if we are going to get into it and really be honest, how much impact did I really have? It’s almost like asking a soldier, whose unit was awarded a Unit Citation to take individual credit for earning that Unit Citation. It’s also fundamentally lazy because when you scan a person’s work experience based on their title, the company, and basic bullets, you can extrapolate the size and scope of impact on well-crafted bullets without actual dollar values listed.
The negative consequence is that it drives the behavior to inflate impact beyond reality, but not quite enough to be called out for it. Do you really want to hire someone who is gaming the algorithm in that manner? Or put a different way, if you use these inflated numbers for screening, but don’t hold the candidate accountable to defend them in the interview, doesn’t this mean they weren’t important for the screen to begin with? Further, if we set the expectation that metrics inflation like this is not only acceptable but leads to preferential review, what does this say about our business?
Another issue is that you might not want the type of candidate who enters your organization based on their skill in gaming the system. Is a candidate that passes through your hiring system going to be honest or are they going to tell you what they think you (the system) want to hear?
So yes, you do want a candidate who can be concise, descriptive, and quantified but you also want that person to be honest, humble, and willing to admit that they were part of a larger effort for that success. I’d also hope you want to build an organization that doesn’t reward inflated claims of success and instead rewards those who contribute collaboratively towards system success.
Back to the thought experiment of the meeting: if you’ve trimmed out your systems thinkers and you’ve hired a team who anticipate and even appreciate the over-inflation of metrics to bolster their status, how well do you think that meeting will provide you the honest input needed for the best decision?
Because what you are likelier to get isn’t the Servant Leadership you say you want, but the over-aggrandizing, braggadocio personality types that are Industrious but unproductive, often representing the Successfully Unsuccessful, and creating the foundation of Functional Stupidity. if these terms all sound familiar, it’s because we’ve covered them in previous essays. It also sets the stage where the real Systems Thinkers, the ones who will address your problems with insatiable curiosity, humility, and intentional reframing, are deprioritized in your hiring systems.
These concerns also highlight the need for systems thinking within the recruiting and hiring process. As a professional recruiting friend of mine, Troy Shideler president of Search Path - Talent Innovation Group sagely stated:
The problem that comes with all automation techniques is that if you solely rely on these tools you are missing the edge cases at best and grossly promoting the wrong people at worse.
The key to note here is that these automation techniques need to be understood from the system perspective for both their strengths and weaknesses and we should balance them in such a way that we don’t create a reactive resume crafting to ‘hack’ the algorithm that results in bad recruiting, and bad candidate behaviors.
Over the past several years I’ve come to see the investment in recruiting as an essential first thought, not an afterthought. Building a white-glove recruiting team both increases your candidate experience as well as creates a flow of skills and behaviors that best align with your organization’s goals.
With this investment should come the intentional design of your recruiting systems to apply a systems thinking mindset. This means transforming sourcing and recruiting from a factory line of automation and rigidity to a dynamic balance of skillsets that can both process the clear cut, as well as the unique and liminal roles. The best recruiters I’ve worked with are very good systems-thinkers who can contextualize, analyze, explore, and promote candidates that wouldn’t pass an ATS screen. They were also able to help craft job postings that explained the nuances of a position and were able to search for candidates based on behaviors, not bullet points. Most importantly, they were also able to find a great candidate and also find the right role for that candidate even if they initially applied to the wrong role!
It’s also wise to recognize the recruiters who viewed a job req like a checklist and who can’t handle any ambiguity in the job description. These types of recruiters may be good for sourcing and recruiting for very basic or very clear-cut roles, but they are highly likely to screen out the systems thinkers while promoting clearly inflated resumes.
Investing in your recruiting and infusing your recruiters with a systems perspective is essential. Understanding that the ATS, AI, and discrete recruiters work well for highly specific and discrete roles is also important. Balancing these together allows you to focus on bringing in the divergents, those systems thinkers, who are more likely to positively transform your products, your processes, and your people.
Further Reading from Authors I really appreciate
I highly recommend the following substacks for their great content and complementary explorations of topics that Polymathic Being shares
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Given your interest in this as a problem, you might be interested in this:
Technology has taken the in-built problems of recruitment - employers not understanding the roles they need or the types of people who succeed in them, employers misrepresenting themselves, candidates misrepresenting themselves intentionally or unintentionally - and supercharged them.
I see a lot of issues at the start of the process - correctly scoping the role and the requirements for a candidate to be successful in it and then converting that into a job ad. Huge opportunities for improvement here. BTW I found this research very interesting: https://www.linkedin.com/business/talent/blog/talent-acquisition/job-description-heatmap