Harvard Business Review: Better People Analytics

Better People Analytics

Artificial Intelligence and Ethics

Artificial Intelligence and Ethics

How the Eagles Followed the Numbers to the Super Bowl

How the Eagles Followed the Numbers to the Super Bowl

How People Analytics Can Change Process, Culture, and Strategy

How People Analytics Can Change Process, Culture, and Strategy

University Took Uncommonly Close Look at Student-Conduct Data

Rutgers

Dodgers, Brewers show how analytics is changing baseball

Baseball

Little Privacy in the Workplace of the Future

Little Privacy in the Workplace of the Future

Google's Culture of Self-Surveying

Google

The Resume of the Future

The Resume of the Future

More Academic Articles

What can machine learning do? Workforce implications
Erik Brynjolfsson and Tom Mitchell. 12/22/2017. “What can machine learning do? Workforce implications.” Science, 358, 6370, Pp. 1530-1534. Publisher's VersionAbstract
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.
Small Cues Change Savings Choices
James J.Choi, Emily Haisley, Jennifer Kurkoski, and Cade Massey. 2017. “Small Cues Change Savings Choices.” Behavioral Evidence Hub. Publisher's VersionAbstract

PROJECT SUMMARY

Researchers tested the effects of including cues, anchors, and savings goals in a company email encouraging employee contributions to their 401(k).

IMPACT

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.

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Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them
Berkeley Dietvorst, Joseph P. Simmons, and Cade Massey. 6/13/2015. “Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them.” SSRN. Publisher's VersionAbstract
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.
The Bright Side of Being Prosocial at Work, and the Dark Side, Too
Mark C. Bolino and Adam Grant. 2016. “The Bright Side of Being Prosocial at Work, and the Dark Side, Too.” The Academy of Management Annals. Publisher's VersionAbstract
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.
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More Popular Press

Embedding Ethics in Computer Science Curriculum
Paul Karoff. 1/25/2019. “Embedding Ethics in Computer Science Curriculum.” The Harvard Gazette. Publisher's VersionAbstract
Barbara Grosz has a fantasy that every time a computer scientist logs on to write an algorithm or build a system, a message will flash across the screen that asks, “Have you thought about the ethical implications of what you’re doing?”

Until that day arrives, Grosz, the Higgins Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), is working to instill in the next generation of computer scientists a mindset that considers the societal impact of their work, and the ethical reasoning and communications skills to do so.

“Ethics permeates the design of almost every computer system or algorithm that’s going out in the world,” Grosz said. “We want to educate our students to think not only about what systems they could build, but whether they shouldbuild those systems and how they should design those systems.”

At a time when computer science departments around the country are grappling with how to turn out graduates who understand ethics as well as algorithms, Harvard is taking a novel approach.

 

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How Can Organizational Network Analysis (ONA) Help Improve Company Performance?
Talha Oz. 2018. “How Can Organizational Network Analysis (ONA) Help Improve Company Performance?” Humanyze. Publisher's VersionAbstract

Organizational Network Analysis (ONA) is the set of scientific methods and theories to help understand interactions within an organization. It helps executives and managers to intervene at critical times, increase performance, and reduce costs.

There’s increasing pressure on executives to drive sustained, long-term growth. Yet, they lack the information they need to make informed business decisions and successfully initiate change. As organizations restructure departments to have fewer hierarchical levels, work increasingly occurs between social networks, rather than though prescribed reporting structures. Research shows that employees look to their networks to find information and to solve problems. Communication no longer flows solely from senior management to individual contributors – information moves through social networks, between colleagues and different teams. Organizations can analyze social networks to assess how information flows between teams and to intervene at critical times in order to improve how work gets done.

Key takeaways:

– Explore the benefits of supporting organizational networks
– How network analysis can impact company performance
– How to interpret network graphs
– Business applications of ONA  for human resources, business processes, and corporate real estate decisions

Why it's totally unsurprising that Amazon's recruitment AI was biased against women
Isobel Asher Hamilton. 10/13/2018. “Why it's totally unsurprising that Amazon's recruitment AI was biased against women.” Business Insider. Publisher's VersionAbstract

Amazon admitted this week that it experimented with using machine learning to build a recruitment tool. The trouble is, it didn't exactly produce fantastic results and it was later abandoned.

According to Reuters, Amazon engineers found that besides churning out totally unsuitable candidates, the so-called AI project showed a bias against women.

To Oxford University researcher Dr Sandra Wachter, the news that an artificially intelligent system had taught itself to discriminate against women was nothing new.

 

Read article here.

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Meet Your New Boss: An Algorithm

Meet Your New Boss: An Algorithm

A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay

A.I. as Talent Scout: Unorthodox Hires, and Maybe Lower Pay

The Performance Management Revolution

Performance Management

Amazon scrapped 'sexist AI' tool

Amazon AI

Making it easier to discover datasets

Google AI

HR Must Make People Analytics More User-Friendly

HR Must Make People Analytics More User-Friendly

More Harvard Business Review

HR Must Make People Analytics More User-Friendly
John Boudreau. 6/16/2017. “HR Must Make People Analytics More User-Friendly.” Harvard Business Review. Publisher's VersionAbstract

Managing HR-related data is critical to any organization’s success. And yet progress in HR analytics has been glacially slow. Consulting firms in the U.S. and Europe lament the slow progress. But a Harvard Business Review analytics study of 230 executives suggests a stunning rate of anticipated progress: 15% said they use “predictive analytics based on HR data and data from other sources within or outside the organization,” while 48% predicted they would be doing so in two years. The reality seems less impressive, as a global IBM survey of more than 1,700 CEOs found that 71% identified human capital as a key source of competitive advantage, yet a global study by Tata Consultancy Services showed that only 5% of big-data investments were in human resources.

Recently, my colleague Wayne Cascio and I took up the question of why HR analytics progress has been so slow despite many decades of research and practical tool building, an exponential increase in available HR data, and consistent evidence that improved HR and talent management leads to stronger organizational performance. Our article in the Journal of Organizational Effectiveness: People and Performance discusses factors that can effectively “push” HR measures and analysis to audiences in a more impactful way, as well as factors that can effectively lead others to “pull” that data for analysis throughout the organization.

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The Performance Management Revolution
Peter Cappelli and Anna Tavis. 10/2016. “The Performance Management Revolution.” Harvard Business Review. Publisher's VersionAbstract

When Brian Jensen told his audience of HR executives that Colorcon wasn’t bothering with annual reviews anymore, they were appalled. This was in 2002, during his tenure as the drugmaker’s head of global human resources. In his presentation at the Wharton School, Jensen explained that Colorcon had found a more effective way of reinforcing desired behaviors and managing performance: Supervisors were giving people instant feedback, tying it to individuals’ own goals, and handing out small weekly bonuses to employees they saw doing good things.

Back then the idea of abandoning the traditional appraisal process—and all that followed from it—seemed heretical. But now, by some estimates, more than one-third of U.S. companies are doing just that. From Silicon Valley to New York, and in offices across the world, firms are replacing annual reviews with frequent, informal check-ins between managers and employees.

How We Got Here

Historical and economic context has played a large role in the evolution of performance management over the decades. When human capital was plentiful, the focus was on which people to let go, which to keep, and which to reward—and for those purposes, traditional appraisals (with their emphasis on individual accountability) worked pretty well. But when talent was in shorter supply, as it is now, developing people became a greater concern—and organizations had to find new ways of meeting that need.

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Reinventing Talent Management: How GE Uses Analytics to Guide a More Digital, Far-Flung Workforce
Steven Prokesch. 9/2017. “Reinventing Talent Management: How GE Uses Analytics to Guide a More Digital, Far-Flung Workforce.” Harvard Business Review. Publisher's VersionAbstract

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.”

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