Digital computers have transformed work in almost every sector of the economy over the past several decades (1). We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a “general purpose technology,” like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities (2), there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be “suitable for ML” (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent “end of work” as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.
Researchers tested the effects of including cues, anchors, and savings goals in a company email encouraging employee contributions to their 401(k).
Researchers found that providing high contribution rate or savings goal examples, or highlighting high savings thresholds created by the 401(k) plan rules, increased 401(k) contribution rates by 1-2% of income per pay period.
Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1-3). In fact, our results suggest that participants’ preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants’ preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control - even a slight amount - over an imperfect algorithm’s forecast.
More than a quarter century ago, organizational scholars began to explore the implications of prosociality in organizations. Three interrelated streams have emerged from this work, which focus on prosocial motives (the desire to benefit others or expend effort out of concern for others), prosocial behaviors (acts that promote/protect the welfare of individuals, groups, or organizations), and prosocial impact (the experience of making a positive difference in the lives of others through one’s work). Prior studies have highlighted the importance of prosocial motives, behaviors, and impact, and have enhanced our understanding of each of them. However, there has been little effort to systematically review and integrate these related lines of work in a way that furthers our understanding of prosociality in organizations. In this article, we provide an overview of the current state of the literature, highlight key findings, identify major research themes, and address important controversies and debates. We call for an expanded view of prosocial behavior and a sharper focus on the costs and unintended consequences of prosocial phenomena. We conclude by suggesting a number of avenues for future research that will address unanswered questions and should provide a more complete understanding of prosociality in the workplace.
What happens when Big Data meets human resources? The emerging practice of "people analytics" is already transforming how employers hire, fire, and promote.
in 2003, thanks to Michael Lewis and his best seller Moneyball, the general manager of the Oakland A’s, Billy Beane, became a star. The previous year, Beane had turned his back on his scouts and had instead entrusted player-acquisition decisions to mathematical models developed by a young, Harvard-trained statistical wizard on his staff. What happened next has become baseball lore. The A’s, a small-market team with a paltry budget, ripped off the longest winning streak in American League history and rolled up 103 wins for the season. Only the mighty Yankees, who had spent three times as much on player salaries, won as many games. The team’s success, in turn, launched a revolution. In the years that followed, team after team began to use detailed predictive models to assess players’ potential and monetary value, and the early adopters, by and large, gained a measurable competitive edge over their more hidebound peers.
That’s the story as most of us know it. But it is incomplete. What would seem at first glance to be nothing but a memorable tale about baseball may turn out to be the opening chapter of a much larger story about jobs. Predictive statistical analysis, harnessed to big data, appears poised to alter the way millions of people are hired and assessed.
Uber Technologies Inc. and other pioneers of the so-called gig economy became some of the world’s most valuable private companies by using apps and algorithms to hand out tasks to an army of self-employed workers. Now, established companies like Royal Dutch Shell PLC and General Electric Co. are adopting elements of that model for the full-time workforce.
Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others.
Alongside the excitement and hype about our growing reliance on artificial intelligence, there’s fear about the way the technology works. A recent MIT Technology Review article titled “The Dark Secret at the Heart of AI” warned: “No one really knows how the most advanced algorithms do what they do. That could be a problem.” Thanks to this uncertainty and lack of accountability, a report by the AI Now Instituterecommended that public agencies responsible for criminal justice, health care, welfare and education shouldn’t use such technology.
Given these types of concerns, the unseeable space between where data goes in and answers come out is often referred to as a “black box” — seemingly a reference to the hardy (and in fact orange, not black) data recorders mandated on aircraft and often examined after accidents. In the context of A.I., the term more broadly suggests an image of being in the “dark” about how the technology works: We put in and provide the data and models and architectures, and then computers provide us answers while continuing to learn on their own, in a way that’s seemingly impossible — and certainly too complicated — for us to understand.
A business’s culture can catalyze or undermine success. Yet the tools available for measuring it—namely, employee surveys and questionnaires—have significant shortcomings. Employee self-reports are often unreliable. The values and beliefs that people say are important to them, for example, are often not reflected in how they actually behave. Moreover, surveys provide static, or at best episodic, snapshots of organizations that are constantly evolving. And they’re limited by researchers’ tendency to assume that distinctive and idiosyncratic cultures can be neatly categorized into a few common types.
The modern workplace is awash in meetings, many of which are terrible. As a result, people mostly hate going to meetings. The problem is this: The whole point of meetings is to have discussions that you can’t have any other way. And yet most meetings are devoid of real debate.
To improve the meetings you run, and save the meetings you’re invited to, focus on making the discussion more robust.
When teams have a good fight during meetings, team members debate the issues, consider alternatives, challenge one another, listen to minority views, and scrutinize assumptions. Every participant can speak up without fear of retribution. However, many people shy away from such conflict, conflating disagreement and debate with personal attacks. In reality, this sort of friction produces the best decisions. In my recent study of 5,000 managers and employees, published in my recent book, I found that the best performers are really good at generating rigorous discussions in team meetings. (The sample includes senior and junior managers and individual contributors from a range of industries in corporate America; my aim was to statistically identify work habits that correlate with higher performance.)
So how do you lead a good fight in meetings? Here are six practical tips:
Do you think you know how to get the best from your people? Or do you know? How do investments in your employees actually affect workforce performance? Who are your top performers? How can you empower and motivate other employees to excel?
Leading-edge companies are increasingly adopting sophisticated methods of analyzing employee data to enhance their competitive advantage. Google, Best Buy, Sysco, and others are beginning to understand exactly how to ensure the highest productivity, engagement, and retention of top talent, and then replicating their successes. If you want better performance from your top employees—who are perhaps your greatest asset and your largest expense—you’ll do well to favor analytics over your gut instincts.
Harrah’s Entertainment is well-known for employing analytics to select customers with the greatest profit potential and to refine pricing and promotions for targeted segments. (See “Competing on Analytics,”HBR January 2006.) Harrah’s has also extended this approach to people decisions, using insights derived from data to put the right employees in the right jobs and creating models that calculate the optimal number of staff members to deal with customers at the front desk and other service points. Today the company uses analytics to hold itself accountable for the things that matter most to its staff, knowing that happier and healthier employees create better-satisfied guests.