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.
In today's world, scientists in many disciplines and a growing number of journalists live and breathe data. There are many thousands of data repositories on the web, providing access to millions of datasets; and local and national governments around the world publish their data as well. To enable easy access to this data, we launched Dataset Search, so that scientists, data journalists, data geeks, or anyone else can find the datarequired for their work and their stories, or simply to satisfy their intellectual curiosity.
Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they’re hosted, whether it’s a publisher's site, a digital library, or an author's personal web page. To create Dataset search, we developed guidelines for dataset providers to describe their data in a way that Google (and other search engines) can better understand the content of their pages. These guidelines include salient information about datasets: who created the dataset, when it was published, how the data was collected, what the terms are for using the data, etc. We then collect and link this information, analyze where different versions of the same dataset might be, and find publications that may be describing or discussing the dataset. Our approach is based on an open standard for describing this information (schema.org) and anybody who publishes data can describe their dataset this way. We encourage dataset providers, large and small, to adopt this common standard so that all datasets are part of this robust ecosystem.
Without models, making sense of data is hard. Data helps describe reality, albeit imperfectly. On its own, though, data can’t recommend one decision over another. If you notice that your best-performing teams are also your most diverse, that may be interesting. But to turn that data point into insight, you need to plug it into some model of the world — for instance, you may hypothesize that having a greater variety of perspectives on a team leads to better decision-making. Your hypothesis represents a model of the world.
Though single models can perform well, ensembles of models work even better. That is why the best thinkers, the most accurate predictors, and the most effective design teams use ensembles of models. They are what I call, many-model thinkers.
During Jeff Immelt’s 16 years as CEO, GE radically changed its mix of businesses and its strategy.
Its focus—becoming a truly global, technology-driven industrial company that’s blazing the path for the internet of things—has had dramatic implications for the profile of its workforce. Currently, 50% of GE’s 300,000 employees have been with the company for five years or less, meaning that they may lack the personal networks needed to succeed and get ahead. The skills of GE’s workforce have been rapidly changing as well, largely because of the company’s ongoing transformation into a state-of-the-art digital industrial organization that excels at analytics. The good news is that GE has managed to attract thousands of digerati. The bad news is that they have little tolerance for the bureaucracy of a conventional multinational. As is the case with younger workers in general, they want to be in charge of their own careers and don’t want to depend solely on their bosses or HR to identify opportunities and figure out the training and experiences needed to pursue their professional goals.
What’s the solution to these challenges? GE hopes it’s HR analytics. “We need a set of complementary technologies that can take a company that’s in 180 countries around the world and make it small,” says James Gallman, who until recently was the GE executive responsible for people analytics and planning. The technologies he’s referring to are a set of self-service applications available to employees, leaders, and HR. All the apps are based on a generic matching algorithm built by data scientists at GE’s Global Research Center in conjunction with HR. “It’s GE’s version of Match.com,” quips Gallman. “It can take a person and match him or her to something else: online or conventional educational programs, another person, or a job.”