Computer says | 0s, 1s and the science behind laying people off via algorithm

0s, 1s and the science behind laying people off via algorithm

By now, we all know the stat that says most hiring deciders look at a CV for a total of seven seconds before shuffling it into the yay or nay piles.

While that might seem cutthroat and brutal, well, that’s life in the big city, kid. And it’s not for lack of care – after all, the hiring manager will have to work with whomever they bring on board. But the sheer volume of applications/CVs has often meant needing to be brutal when whittling down candidates.

Then came automation, AI and ready-made software that allowed us to automate that, freeing us up for the work of really reviewing candidates “heart and soul”. Some argue that it’s helped (removing unconscious bias, freeing up time) and others argue that it’s hindered (entrenching unconscious bias, taking away personal choice). Either way, it’s probably here, in some for or another, to stay.

And now, 2023 is the year of the AI firing

According to a survey conducted by software firm Capterra of 300 HR decision-makers in the US, 98% of them fessed up to using software and AI/algorithms to help them ‘reduce labour costs’, including choosing who to axe.

But is that really so shocking? It’s 2023, the age of quantum computing is nearly upon us, and we use AI for everything from finding true love to travelling to monitoring our health. And a lot of leaders, particularly in smaller-to-medium-sized companies, may find it helpful to rest in data’s comforting arms for a bit before making the final decision on redundancies.

Because, despite what Shakira claims, hips can lie, but data can’t.


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Bryan Westfall, one of the senior HR analysts at Capterra said that while data can be helpful, no one should rely solely on it, and, of course, we should all recognise it’s limitations.

“The danger here is using bad data,” Westfall told WaPo, “[and] coming to a decision based on something an algorithm says and just following it blindly.”

Westfall writes in his survey analysis report: “Leveraged in the right way, data analytics and algorithms built into HR software can uncover useful insights that can’t be found through instinct or intuition alone—giving HR departments a significant advantage in making the best long-term decisions for their business in tough times. Use bad data, the wrong data, or have a poor understanding of how that data is used, however, and HR departments are all but guaranteed to get suboptimal recommendations from their technology.”

So, what can AI help with when deciding who to lay off?

Any HR professional worth their CIPD qualification already knows how important data is. Things like attendance, time management and performance reviews are dry, and not necessarily indicators of performance (for example, what if someone has a long-term illness, or works late from home most evenings?), but taken alongside personal experience and the trust HR gut instinct, gathering and analysing employee happiness and performance data is really helpful.

One of the things the study authors and other HR tech experts have warned against is using AI to calculate ‘flight risk’ – aka, if someone is likely to leave your company soon. The conventional wisdom, then, would be to include that person on the redundancy list – after all, they’re going to leave soon anyway, right?

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Technically, yes. Ethically, maybe not. Multiple studies show that even very high-performing women and people of colour are higher flight risks, especially post-pandemic. Side hustles turning into careers, a decision to no longer work at a company that rewards people based on whiteness or poshness, a desire to spend more time with family and not be castigated for it at work – all of these are part of flight risk, and the AI isn’t quite there yet, even with the most software, to control for that.

Westfall concludes in his report: “As much as we’ve talked about HR departments becoming more data-driven and tech-savvy, there should always be a place for humans to make the final call in decisions about other humans.

“If something about any tech-enabled recommendations doesn’t sit right with your department, discuss and explore why that is before making any final decisions.”



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