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.
On the day I met Brett Ostrum, in a conference room in Redmond, Wash., he was wearing a black leather jacket and a neat goatee, and his laptop was covered with stickers that made it appear you could glimpse its electronic innards. That was logical enough, because those circuits were his responsibility: He was the corporate vice president at Microsoft in charge of the company’s computing devices, most notably Xbox and the Surface line of laptops and tablets.
It was early 2018, and things were going pretty well for him. Despite Microsoft’s lineage as a software company, and as a brand not exactly synonymous with good design, it was making the most of its late start in the hardware business. Mr. Ostrum and his team were winning market share and high marks from critics.
But he saw a problem on the horizon. It came in the form of extensive surveys Microsoft used to monitor employees’ attitudes. Mr. Ostrum’s business unit scored average or above average on most measures — except one. Employees reported being much less satisfied with their work-life balance than their counterparts elsewhere at the company.
As the head of People Operations at Google, Laszlo Bock ’99 applied data analytics to human resources questions that have long been answered with hunches. His company Humu is now extending that approach for other organizations by providing AI-generated prompts to their employees. In a conversation with Yale Insights, Bock said that behavioral science, combined with an understanding of human feeling and careful attention to privacy, can help organizations run better.
Employers are monitoring their workers more often and using more tracking tools than ever. What's surprising is that a growing number of employees don't mind.
Advancements in technologies―including sensors, mobile devices, wireless communications, data analytics and biometrics―are rapidly expanding monitoring capabilities and reducing the cost of surveillance, and that's prompting more employers to use these tools.
In 2015, about 30 percent of large employers were monitoring employees in nontraditional ways, such as analyzing e-mail text, logging computer usage or tracking employee movements, says Brian Kropp, group vice president of HR practice for Gartner, a research and advisory firm. By 2018, that number had jumped to 46 percent, and Gartner projects it will reach well over 50 percent this year.
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.
ON MARCH 18, 2018, at around 10 P.M., Elaine Herzberg was wheeling her bicycle across a street in Tempe, Arizona, when she was struck and killed by a self-driving car. Although there was a human operator behind the wheel, an autonomous system—artificial intelligence—was in full control. This incident, like others involving interactions between people and AI technologies, raises a host of ethical and proto-legal questions. What moral obligations did the system’s programmers have to prevent their creation from taking a human life? And who was responsible for Herzberg’s death? The person in the driver’s seat? The company testing the car’s capabilities? The designers of the AI system, or even the manufacturers of its onboard sensory equipment?
“Artificial intelligence” refers to systems that can be designed to take cues from their environment and, based on those inputs, proceed to solve problems, assess risks, make predictions, and take actions. In the era predating powerful computers and big data, such systems were programmed by humans and followed rules of human invention, but advances in technology have led to the development of new approaches. One of these is machine learning, now the most active area of AI, in which statistical methods allow a system to “learn” from data, and make decisions, without being explicitly programmed. Such systems pair an algorithm, or series of steps for solving a problem, with a knowledge base or stream—the information that the algorithm uses to construct a model of the world.
Ethical concerns about these advances focus at one extreme on the use of AI in deadly military drones, or on the risk that AI could take down global financial systems. Closer to home, AI has spurred anxiety about unemployment, as autonomous systems threaten to replace millions of truck drivers, and make Lyft and Uber obsolete. And beyond these larger social and economic considerations, data scientists have real concerns about bias, about ethical implementations of the technology, and about the nature of interactions between AI systems and humans if these systems are to be deployed properly and fairly in even the most mundane applications.
Consider a prosaic-seeming social change: machines are already being given the power to make life-altering, everyday decisions about people. Artificial intelligence can aggregate and assess vast quantities of data that are sometimes beyond human capacity to analyze unaided, thereby enabling AI to make hiring recommendations, determine in seconds the creditworthiness of loan applicants, and predict the chances that criminals will re-offend.
But such applications raise troubling ethical issues because AI systems can reinforce what they have learned from real-world data, even amplifying familiar risks, such as racial or gender bias. Systems can also make errors of judgment when confronted with unfamiliar scenarios. And because many such systems are “black boxes,” the reasons for their decisions are not easily accessed or understood by humans—and therefore difficult to question, or probe.