HR data and analytics drives profits but at what cost?

By Neil Patrick

HR analytics can punish your employees, but you won’t worry about that if you want to win.

In his 1976 book, 'Computer Power and Human Reason: From Judgment To Calculation', author Joseph Weizenbaum laid out the case that while artificial intelligence may be possible, we should never allow computers to make important decisions, because computers will always lack human qualities such as compassion and wisdom. Weizenbaum made a crucial distinction between deciding and choosing. Deciding is a computational activity, something that can ultimately be programmed. But it is the capacity to choose that ultimately makes us human. Choice, however, is the product of judgement, not calculation.

Stephen Hawking went even further when he said,  "...the development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded."

I cannot say if this distopian vision will or will not ever manifest. But it is plain to anyone that we are racing down this path with scarcely any care. We are already seeing the first applications of big data and AI based workforce decision and management systems. HR leaders like it because it promises to solve several of their most longstanding and vexing problems.

HR has been fed up forever about not being taken seriously. HR big data and analytics promises to be their saviour. It suggests that HR can transition from being perceived (wrongly in my view) as fluffy and utilitarian to having a proper seat at the leadership table, because like its rivals in finance, sales and marketing, it can now deploy hard ‘scientific’ data to back up its proposals.

It also promises a happier, more engaged workforce. One in which every twist and turn of employee sentiment can be quantified and responded to. If the data says people are feeling worse about something, HR can know this quickly and help rectify the problem.

I wish this were true. But I fear the opposite. That’s because every technological advance includes the option of being deployed for good or evil.

What HR may not like so much is that HR data delivers an extremely useful tool for business leaders to push and punish people. It’s a deal with the devil, in which HR’s quest for happy, engaged workers, risks being hi-jacked by the rest of the business to brutally force up productivity and drive down cost.

Incidentally, my argument skims over the very real practical questions around HR data and its inherent unreliability as Marcia LaReau has convincingly described here in her post, ‘To a Hammer, Everything is a Nail’.

Business has some critical problems today. Growth and profitability are chief amongst these. And countless studies show there is little correlation between hard profit and employee satisfaction. Sure there are plenty of examples of firms growing successfully who also invest in their people. But when we look at the most established large firms who are making the most money, most care much less about their people.

This is actually a very simple economic truth to understand. In an open competitive market, whoever gets the most work done for the lowest cost, wins. And if that means some people suffer, then so be it.

Every employee survey I have ever seen identifies that a person’s manager is the single greatest determinant of job satisfaction. It’s not pay, it’s not perks, it’s not flexible hours. It’s the person who manages you. If they are inspiring, caring, transparent, supportive, their staff will enjoy their work.

But here’s the problem. Managers that display these qualities are becoming an endangered species. Because their bosses usually don’t care very much about strategic HR. They do care about smashing their immediate revenue and profit targets. HR gets people hired for them, sorts out people issues and keeps them out of court. Everything else is fluff.

It’s that simple. Good leadership (not data) delivers happy and productive teams.

But we also know that good leadership is a frustratingly elusive and expensive resource to acquire and maintain. One accidental bad hire of a psychopathic manager and the whole of an organisation’s carefully nurtured culture can be demolished in a few months.

So if good leadership is expensive and scarce, but data is cheap and plentiful, the choice becomes a no-brainer.

What is becoming visible now is that there are firms who take HR data very seriously. And what we can also see is just how punishing and dehumanising the application of HR data can be in practice. To verify this, all we need to do is examine the firms where HR data is most developed and embedded in the day to day operations of the organisation.

And right now, probably the most advanced organisation in this field is Amazon. In January 2019, Amazon became the world’s third most valuable company by market capitalisation, after Apple and Microsoft.




Yet in some US states, nearly one in three Amazon workers are on food stamps. For Amazon, this is even better than paying people almost nothing. It is the transference of part of Amazon’s wage bill to the taxpayer.

In Amazon warehouses, every second of people’s work is measured and evaluated. They may walk over twenty miles on a day’s shift. Their productivity is tracked and ranked against their peers, with whoever is at the bottom of the table likely facing disciplinary actions and threats. A toilet break can cost you your job if it exceeds a tightly prescribed time allowance. Many describe it as a daily hell, which they endure only because they have few other options.

Welcome to the brave new world of HR big data. It’s being corrupted from the get-go. And if you’re an HR leader, be careful what you wish for.

AI in recruiting really means ‘abdicated intelligence’

By Neil Patrick

What is advocated and marketed as technology-enabled recruitment processes increases the difficulty in finding and retaining great people. The best people are made not found. We don't need to get better at ranking people by increasingly tightly defined data points, we need to take ownership of our responsibility (and self-interest) to find good people and make them great. 

Artificial intelligence is the big topic in almost every professional field right now. From drones in farming to robot surgeons, the overriding narrative is about the tasks which AI will enable us to perform better, faster and cheaper.

HR and recruitment are no different. The application of AI is spreading like wildfire as new tools are developed which expand the range of tasks that AI performs to assist recruitment management processes.

Until now, it was relatively simple to integrate the digital world with our own professional world. Have a LinkedIn profile which is properly constructed. Expand our professional networking onto one or two social media platforms. Write some commentaries or blog posts. By these means, anyone checking us out could easily discover our credentials.

Some people understandably chose not to participate or did so in the most cursory way possible. They had no wish to participate in the online race. They had fears about privacy. They didn’t understand how social media worked. They had better things to do with their time. All these were legitimate grounds to not participate. But not anymore because…

This is now all set to change

The next wave of AI and big data is going to transform the processes of hiring way beyond anything we’ve seen to date. Traditional recruitment is a gruelling, complex process for employers and recruiters alike. Recruitment teams have to be heavily incentivised to commit to the heavy workloads involved. And this costs money. A lot of money. But AI will streamline and speed up these processes. It will be able to identify suitable candidates in a few seconds. It will message the chosen few and chatbots will perform initial screenings. Candidate selection decisions will be made on the basis of data and scoring algorithms rather than fallible human interactions. Very little human intervention will be needed.

Recruitment costs will fall even more. Hiring efficiency and speed will increase. Hiring choices will be validated and justified by the ‘scientific’ methods involved.

At least that is the vision. The reality is more worrying.

Why it’s flawed

Data is not science. The principal predictors of job performance cannot be discovered by algorithms. The first attempts to automate the selection process created a bigger mess than before. Online job boards and applicant tracking systems (ATS) drove application numbers sky-high and candidate quality tumbled. But acquiring 500 applications cost around $50. Using a professional head hunter costs about $30-$40,000 per hire for professional vacancies. These economics ensured that automated recruitment processes took hold and continue to grow in usage.

Nick Corcodilos explains in this video why the application of data driven metrics to recruitment ensures that employers miss many of the best candidates for any given role:





I agree with everything Nick says here. This process is flawed. It cannot find the best people because the available data points cannot determine that they will perform well on the job. And because quite a few of the very best people choose not to present themselves online in their professional capacity. Yet for all its weaknesses, automated recruitment is only going to expand because the cost differential is so compelling.

In a strong and growing economy, organisations can invest in quality processes. In an economy which is uncertain and faltering, when profits and growth are elusive, focus inevitably shifts to cost reductions. Cost trumps quality in such times.

Your career is at risk if you choose not to participate

It seems logical to me that the current and anticipated applications of technology and artificial intelligence in recruiting will continue to erode the quality of hiring decisions made. This may deliver short term cost gains, but will push up long term costs as turnover rises and employee performance falls.

Yet this is not my greatest worry. My fear is that the relentless advance of this technology will create a new underclass of smart, educated and capable people who have chosen for legitimate reasons not to present themselves online. These people will become completely invisible to the data capture bots. And that invisibility will slowly but surely eat away at their employment opportunities.

There’s not a ‘talent shortage’, there’s a leadership vacuum

 
For organisations, the deployment of recruitment AI encourages organisations to abdicate their responsibility to create and nurture their own human talent pool. This creates a downward spiral of ever increasing data point discrimination reinforcing the mythology of what employers disingenuously call their ‘talent shortage’.

And if the belief in the pseudo-scientific reliability of these systems persists within management, we will see the abdication of leadership’s responsibility for taking good people and helping them become great. Instead, the tools will be adjusted to cure perceived shortcomings, when the real shortcomings are rooted in the mistaken faith in progress through technology.

Artificial intelligence is well named. Because it’s not real intelligence…

PS. My good friend Marcia LaReau at Forward Motion Careers has a great post here about what jobseekers can do to avoid becoming a victim of this situation. 

Computers are learning to read our minds and why this changes everything

By Neil Patrick


In the digital age everything is reduced to numbers. Big data counts everything that can be counted.

But big data conceals as much as it reveals. Big data scientists argue that their algorithms enable fine judgements to be made across billions of pieces of data. Yet there is idiocy embedded within many algorithms.

Many of us find ourselves enraged by the clumsy ways simplistic counting algorithms determine who gets noticed online and who doesn’t.

This quantitative analysis is the strength of big data and currently its main weakness. But most algorithms cannot count the things that are often most important, i.e. the qualitative aspects. Like value, trust, goodwill, personality.

But this is about to change…

IBM Watson has already built artificial intelligence that does exactly this. It’s called Personality Insights. Follow this link and you can try it out. Just load a thousand or so words of a blog post or something you have written and the programme will analyse and quantify your personality.

I put it to the test with one of my own blog posts. This is what it said about me:

You are inner-directed and skeptical.

You are empathetic: you feel what others feel and are compassionate towards them. You are philosophical: you are open to and intrigued by new ideas and love to explore them. And you are calm-seeking: you prefer activities that are quiet, calm, and safe.

You are relatively unconcerned with tradition: you care more about making your own path than following what others have done. You consider independence to guide a large part of what you do: you like to set your own goals to decide how to best achieve them.

I could pick faults with this analysis maybe. But broadly speaking, I was impressed by how well it managed to interpret who I am and how I think.

The Personality Insights programme isn’t the end of the road. It’s the start of it. AI is going to become perfectly capable very soon of understanding the very essence of who we are, how we think and what motivates us.

Imagine the implications of how when sooner rather than later, software such as this is incorporated into social media platforms, into blog platforms, into LinkedIn?

The currently clumsy counts of likes, followers, shares etc could be supplemented by rich data which permits far more nuanced metrics about us.

The way will be open for platforms to group, sort and rank us in ways that they have never been able to do before.

And the gamers who exploit the clumsy metrics will be put in their rightful place.

But only if the platform owners recognize and seek to rectify the simplistic and unhelpful ways they use big data to judge who is important and who isn't.

We have to hope they use this power for our good and not just theirs.