Boxes Without Humans: What Will Fill the Gap?

Posted on by Stuart Murray
IMG_3651.JPG
Amazon cat. Photo courtesy of Stephen Woods.

It’s been couple of days since the latest social disruption.  I wonder what’s going to be turned upside-down this week?  Flat on the heels of online shopping ravaging the conventional retail sector, warehouse and trucking jobs might be the next to go.

Amazon.com holds contests every year entitled the Amazon Robotics Challenge, where academics and graduate students compete for prize money to help automate warehouse jobs with robots.  The technology gets a little more clever each year.  “They now use neural networks, a form of artificial intelligence that helps robots learn to recognize objects with less human programming.”  The good news is that there might be lots of work for technologists.

The bad news is that this could take a bite of decent-paying warehouse jobs.  In the US, there are about 950,000 warehouse and storage-industry jobs with an average wage of about $20 per hour.  Those jobs are threatened.

But a more pressing concern would be trucking.  Self-driving cars are already starting to make an appearance on the roads.  For trucking, the change will happen more quickly according to a Guardian article.  The decision to go driverless with trucking is a corporate decision, not a consumer decision like with driverless personal automobiles.  The financial motivation is extremely favorable to use self-driving trucks. “The potential saving to the freight transportation industry is estimated to be $168bn annually… [including savings from] labor ($70bn), fuel efficiency ($35bn), productivity ($27bn) and accidents ($36bn)…”  The trucker’s wage is similar to that of warehouse workers, but there are far more jobs at stake.  There are 3.5 million truckers in the United States, and the drivers themselves spend a lot of money at road stops, hotels, and diners.

Now, if you work in finance or information technology this might not concern you so much.  Technologies are created, investments made, money saved, and we’re better off on average.  But in human resources we know that the unemployed are our people.  We terminate them, we screen them when they apply for jobs, we help them with problems if we know them personally, and occasionally the we ourselves are the unemployed.  We think about them a lot.  We don’t always show it, but frankly we have to care or we die inside.

Thankfully, people have started to talk more openly about the broad-based social disruption that Artificial Intelligence may have.  In the Guardian article on trucking, there are calls for a Universal Basic Income, direct payments to everyone regardless of how well they have fared in a disrupted labour market.  There may be other policy concerns as well, such as improved access to training and education.  Of course, these are government-funded solutions which seem obvious to me.

There is still a persisting risk that the disruption will be misattributed to an outside factor.  If the technology-based job losses are blamed on immigrants, environmental regulations, or the abandonment of tradition, I can’t foresee broad democratic support for government solutions or the embracing of change.  And this spells trouble for the very business interests whose success relies on the rule of law, stable diplomacy, and a diverse workforce who are engaged to stay productive.

Loving Math, Caring About Peers

Posted on by Stuart Murray
Nerd. by David Nichols
Nerd. Photo courtesy of David Nichols

Some time has passed, so let’s calmly reflect on the anti-diversity manifesto that got a software engineer fired from Google in August of 2017.  James Damore, the author, has to be the unluckiest person on earth.  Not only did he lack the genetics and environmental upbringing to be compassionate about the emotions of others (he might be on the autism spectrum), but he also wrongly attributed his career difficulties to the ascent of workplace diversity initiatives.

He delivered his critique via the alt-right media one week prior to the deadly neo-Nazi rally in Charlottesville.  That incident provoked corporate executives, top-ranking generals, and mainstream Republicans to denounce the rally and distance themselves from Donald Trump’s muddled sympathies in the aftermath.  Damore and his fans have left no opening for a nuanced discussion about the effects of diversity initiatives on those with developmental disorders, a potentially meaningful topic of debate.

In this article in the New York Times, author Claire Cain Miller proffers a critique of the role of emotional intelligence in the modern world of information technology.  It turns out that technology has a massive overlap with social and emotional context.

Emotional Intelligence in Workforce Analytics and Computer Programming

For deep evidence, the article cites 2015 research from David Demming that finds job growth and wage growth are highest among roles that use both math skill and social skills.  The idea is that workers “trade tasks” with one another, to allow specialization of talents and improved efficiency when work duties are shuttled back and forth.  Those who trade tasks more effectively through the use of social skills are more productive; hence more jobs and higher pay.

This double-barreled skill set is abundantly obvious to those in workforce analytics.  We spend half our day figuring out cool formulas and novel discoveries.  But the other half of our day is spent interpreting client need, negotiating resource priorities, wordsmithing data definitions, developing interpretations that are suitable to context, and showing compassion while we advance disruption.  However, my field is new.

When computer programming was new it was originally considered highly social work.  There was an abundance of women working in the field.  Through some office-culture twists and turns, things changed.  Boys and men who weren’t as clever at the social skills self-selected into programming.  It worked out for a lot of people.  But there’s a problem; at some point in someone’s career their next chance for a promotion is contingent on social skills.  Those who are lacking in this area see their careers stalled.

Examples abound of coding projects with male-dominated teams who lacked context, who missed an important detail about women’s perspectives.  Apple’s original health app tracked everything except menstrual cycles, the most-tracked health data point amongst women.  Google Plus obliged users to specify their gender and provide a photo, exposing women to harassment.

The Times article also cites research from 2010 by Stanford sociologist Shelley Correll showing that gender stereotypes about skills and performance are a kind of self-fulfilling prophecy.  It’s not true that women are naturally bad at math, but it is abundantly true that women who are told they are bad at math will under-perform and rate themselves more harshly.  The struggle about this stereotype has played out in dramatic ways over the years.

How to Improve Workplace Culture to Ensure Equality for Women

In terms of what to do about this, Correll advises that we:  1) ensure there are no negative gendered beliefs operating in the organization, 2) ensure performance standards are unambiguous and communicated clearly so that sexism does not fill the vacuum, and 3) hold senior management accountable for gender disparities in hiring, retention, and promotion.  That third item is metrics-based accountability, which means that business performance, diversity, and workforce analytics are fundamentally entwined.

The times article notes that “one way to develop empathy at companies is by hiring diverse teams, because people bring different perspectives and life experiences.”  While we might perceive that equity and inclusion efforts come from an activist base, there is a corporate interest in fostering inclusion.  High-performance workplaces need an environment where tasks and diverse views are shuttled back and forth, with ease and good manners.

As for white white males who desperately struggle with emotional intelligence, their voice will have to wait another day.  And probably wait for another leader.

Humanity Is In-Scope For All New Projects

Posted on by Stuart Murray
Public domain from USDA
Photo in public domain.

Are we still living in the information age?  Maybe not.  Here’s an interesting article from Harvard Business Review on the Human Economy, from November 2014.  Societies have evolved from agrarian economies to industrial economies to service economies to knowledge economies.  At this point, there’s such a glut of food and goods and services and information, the next bottleneck is humans.  Businesses that do well in this new era will be those with a greater sense of humanity.  Those that have a poor sense of humanity shall be sunk!

“In the human economy, the most valuable workers will be hired hearts.”  It is what makes us different from robots and artificial intelligence (i.e. emotions) that will make us special as employees.  Many of the examples in the article are from the perspective of a customer whose heart was won over.  Noticeably there’s always and employee whose empathy and enthusiasm closed the deal (so to speak).  The article cites several studies where CEOs place a higher priority on social skills than analytics.

This is what it feels like at my desk.  I spend half of my time getting the formulas right, and the rest of the time helping people become happy about the numbers.  They can’t use workforce analytics unless they trust us.  The analytics influence the decision making.  Clients get better because we help them develop patience.  They come back for more the data is addictive.  We persevere through inevitable errors and mistakes, being honest about having made best efforts.

Beyond customer engagement, employee engagement is increasingly recognized as having a major impact on productivity behind closed doors.  People are not monkeys at a keyboard sending customer service complaints in circles.  Or rather, sometimes it seems that way but it sure looks bad in the news.

At their best, people are the embodiment of the discretionary effort that dictates if a service or product gets to market, and they make every capital investment matter (or not).

Is Workplace Culture the Right Kind of Revolution?

Posted on by Stuart Murray
The wall of plexiglass, by ebt47563 (=)
The wall of plexiglass.  Photo courtesy of ebt47563.

Exactly how do you change organizational culture?  This is a good HBR article from June 2017 about attempts to change corporate culture from the bottom-up.  It’s a story about Dr. Reddy’s, a global pharmaceutical company based in India and led by G.V. Prasad.  The authors are Bryan Walker from IDEO and Sarah A. Soule from Stanford Business School.

Dr. Reddy’s process of culture change began with significant ground research to find out what their staff, providers, and investors needed when dealing with customers.  They brought their goals down to four simple words that brought it all together: good health can’t wait.  Instead of selling the slogan through posters and speeches they chose to demonstrate their purpose through actions.  The initiative named projects in packaging, sales, and internal data to advance the new vision.  There were some immediate impacts.  One scientist broke a number of company rules and produced a new product in 15 days, having prioritized new efforts to match the vision.

The comparison to social movements is important, because movements start with an emotion rather than a call to action.  Movements start small, “with a group of passionate enthusiasts who deliver modest wins.”  Momentum builds through networks, penetrating power structures and leadership.

There are also “safe havens,” places where activists can behave differently from the dominant culture and discuss their goals.  In innovative organizations, research labs are often built as separate mico-organizations that cultivate change as prep-work for the larger organization.  This story resonates with me, because disruptive workforce analytics will occasionally fall of deaf ears.  The analysis needs to be created in a manner which partially ignores pre-existing agendas or presumptions of how things would normally be done.  The decision of whether to apply new ideas might belong within a more formal process, but when experimenting with messy new ideas, to be sequestered is ideal.

Beyond the HBR article two additional models are appropriate to discuss the nature of change.

Innovating Technology and Trends Through Social Networks

The first model is the diffusion of innovations as described by Everett Rogers.  In this model, there is a small avant-garde of weirdos who just get into stuff that is new and interesting.  That crowd of innovators will not have the full opportunity to make money or make it big.  But their new findings diffuse through social networks, based on peoples’ network connections and their readiness to consider new ideas.

There are several hold-outs, such as the laggard crowd who resists change until it is impossible to do so.  The biggest difficulty is the early-stage challenge of “Crossing the Chasm” where the new idea has won-over a small crowd of early-adopters who are about 13.5% of the population.  The challenge is that sometimes there’s something about the new idea that doesn’t mesh with the next crowd, the early majority.  Some examples might be that the new technology has a difficult user interface, or the social trend is incompatible with the conventional lifestyle of those in the burbs.

In my opinion, the classic example of this challenge is the hands-free bluetooth headset that you see people wearing when they’re talking on the phone while walking down the street.  The technology has been in public for more than fifteen years and our first instinct is still that we want the caller to get professional help.  And that’s even when you’re not also angry at them about a misunderstanding.

Using Social Disobedience Tools to Change Workplace Culture From Within

Another compelling cultural change model is the Spectrum of Allies model from George Lakey of Training for Change.  Lakey is highly experienced in training social justice activists in civil disobedience.  I attended a couple of workshops with Lakey when I was part of the labour movement, and his spectrum model is eye-opening.

The key diagram is a semi-circle, kind of like a half-order of a large pizza with six or eight slices.  The idea is that everyone can be categorized according to their level of enthusiasm for, or resistance to, an agenda or new idea.  Then you lay these wedges out in order, with the most supportive categories on the left and the most resistant on the right.  Your goal is to shift all of society one wedge to the left.  That is, the biggest hold-outs still get your attention, you’re just trying to convince them to become only moderately opposed to your goals.  Those in the middle, you can tip towards you slightly.  Those who are with you from the start, those are your strongest advocates and you can start giving them more work.

What really holds the model together is that you are shifting the entire social culture towards your way of thinking, resulting in culture change.  Everyone is a big deal, everyone receives the attention they deserve.  It’s very different from that us-against-them stuff that we’re accustomed to seeing during elections.  And it is very different from the notion that the main difference in the key players is their place on the org chart.

What this means for workforce analytics, is that you will require several different vehicles to bring meaningful information into human resource decision-making.  While there will be those who are hungry for the information, there will be others who need to simply be sold on the notion that it is not a threat.  While innovative findings might be compelling amongst an in-crowd, getting the information through cliques and interests will require bridging links and data translation.  You can build new ideas in self-imposed isolation, but at some point you step into public and advance it your ideas through the audience.

But before you step out, please put away your Bluetooth headset.

Peeking Into the Future of Job Elimination

Posted on by Stuart Murray
Google Glass. Byi Karlis Dambrans.
Google Glass.  Photo courtesy of Karlis Dambrans.

There is increased speculation that artificial intelligence (AI) will increasingly replace the work of humans over the medium to long term.  Already, AI is performing well at the world-class tournament levels in such games as Chess and Go, the latter of which was a major breakthrough.  What about actual jobs?

At University of Oxford, a survey from the Future of Humanity Institute asked several leading experts how long it will take for machines to outperform humans.  Here is the average forecast for a couple of skill sets:

  • 2023 – Folding laundry
  • 2027 – Truck driving
  • 2031 – Retail sales
  • 2049 – The writing of best-selling books
  • 2053 – Surgery

In the long game, they think all human tasks will be out-performed by machines in 45 years.  All human jobs would be replaced in about 125 years.  So we’re kind of safe for a decade or so.  However, there are major concerns about what this change will mean for humanity, as this change may increase economic inequality.

In my opinion, as this relates to workforce planning, the challenge seems most interesting in the transition period.  That is, people will get new jobs designing new technologies, and people will make themselves more productive by using technology in the workplace.  But there will be more frequent changes, more dramatic changes, and things will happen more quickly.

These changes mean that human resources will be the key party delivering change management, knowledge management, hiring, learning and development, and employee communications.  The pace at which people adapt to change will determine success in investment decisions and the retention of engaged customers.  But only if you get the metrics right.  Anything else, and your organization is sunk.

What’s Fair in a Winner-Takes-All Economy?

Posted on by Stuart Murray
Medal in pieces, by kcxd
Medal in pieces.  Photo courtesy of kcxd.

Do you know how you’re going to benefit from this screwy economy?  There are lots of opportunities to win or lose.  How do you make sure you get something out of this world?  How do you make sure you land one of the good jobs, and avoid the pitfalls of a fiercely competitive market?

Workforce Data and Inequality by Education

Jeffrey Sachs, the noted economist from Columbia University, wrote a brief overview of the jobs market in the US.  Unemployment is near a ten-year low in the United States.  This is getting into the news because it kinda looks like Trump’s policies are having a positive impact.  But it’s tricky.

What is interesting is the great divide between those with a university degree and those without.  Sachs diverts our attention to the more-accurate employment-to-population (EP) ratio which is currently 60.1% “meaning close to three of every five adults is working, still down sharply from the peak rate of 64.6% in early 2000.”

The EP ratio is varied by education level, at 72.1% for those with a degree and 54.9% for those with only a high school diploma.

Sachs notes that job losses in the past decade have been mostly caused by automation and not globalization.  He cites the self-serve kiosks at McDonald’s as an example.  Policies aimed at saving and creating jobs through restrictions of trade and immigration are “doomed to fail.”

Sachs recommends that America prioritize quality education and job skills, preserving and improving the vital role of public schools.  He also recommends America boost the incomes of lower-skilled families through government transfers to break the multi-generational cycle of poverty that discourages skill attainment by the kids.  Sachs also points to countries that provide a higher quality of life through better government spending in health care, child care, and other programs, which boost the quality of life regardless of job success.

Public Policy and the Political Divide

I personally agree with Sachs, but I lean that way anyway.  I’m well-educated, I come from a union family, and I live in an urban area.  I also work in the public sector, and I’m Canadian.

I think the hardest thing for liberals to understand is the opinions of those who are locked-down by hardship and who decline this “expert” help.  It’s true at the aggregate level that we should ensure people get help.  But at the individual level, people who have actually been poor perceive that overcoming hardship is a case of perseverance, getting out to look work, staying away from high-risk habits, and keeping an eye out for the occasional physical threat.  If there are millions of people who have this individual opinion, it bundles together into a block of pro-market voters.  The prescription of public-policy solutions seems policy-correct but off-target in the messaging.

Meanwhile, new technologies will disrupt every sector and make billions for those who create or innovate these new solutions, leaving everyone else in the dust.

It’s hard to make sense of it all.  To understand this, you need to know that there are secret whispers amongst all economists that “everything causes everything else” in a big hot mess.  It’s still possible to have a thriving economy for strange reasons.  Trump could still cause the economy to grow by accident.  To what we attribute our personal success or failure is frothy right now, and it’s reasonable to not take anyone’s judgments to seriously, including your own.

The simple question we should ask when a few big winners bring in the gold, is will you get your cut?  The winners think they get to keep it all.  We’ll see.