Publications

2020
Smile!  Your Boss Is Tracking Your Happiness.
Chip Cutter and Rachel Feintzeig. 3/7/2020. “Smile! Your Boss Is Tracking Your Happiness.” The Wall Street Journal. Publisher's VersionAbstract

Employers are turning to sentiment-tracking software, daily surveys and apps to monitor workers’ mental states. Does this pose a new threat to employee privacy?

Are You Still Prioritizing Intuition Over Data?
Thomas Chamorro-Premuzic. 2/27/2020. “Are You Still Prioritizing Intuition Over Data?” Harvard Business Review. Publisher's Version
Barclays Forced To Stop 'Big Brother' Employee Tracking System After Backlash
Mark Murphy. 2/21/2020. “Barclays Forced To Stop 'Big Brother' Employee Tracking System After Backlash.” Forbes. Publisher's Version
Barclays using 'Big Brother' tactics to spy on staff, says TUC
Kayleena Makortoff. 2/20/2020. “Barclays using 'Big Brother' tactics to spy on staff, says TUC”. Publisher's VersionAbstract
Bank admits using tracking software to log how long staff spend away from their desks.
Barclays Forced To Stop ‘Big Brother’ Employee Tracking System After Backlash
Mark Murphy. 2/2020. “Barclays Forced To Stop ‘Big Brother’ Employee Tracking System After Backlash”. Publisher's Version
The New Analytics of Culture
Matthew Corritore, Amir Goldberg, and Sameer Srivastava. 1/31/2020. “The New Analytics of Culture.” Harvard Business Review. Publisher's VersionAbstract
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.
2019
Biased Algorithms Are Easier to Fix Than Biased People
Sendhil Mullainathan. 12/6/2019. “Biased Algorithms Are Easier to Fix Than Biased People.” The New York Times. Publisher's VersionAbstract
Racial discrimination by algorithms or by people is harmful — but that’s where the similarities end.
A face-scanning algorithm increasingly decides whether your deserve the job
Drew Harwell. 11/6/2019. “A face-scanning algorithm increasingly decides whether your deserve the job.” The Washington Post. Publisher's VersionAbstract

HireVue claims it uses artificial intelligence to decide who’s best for a job. Outside experts call it ‘profoundly disturbing.’

At an Outback Steakhouse Franchise, Surveillance Blooms
Louise Matsakis. 10/19/2019. “At an Outback Steakhouse Franchise, Surveillance Blooms”. Publisher's VersionAbstract
Fried onion meets 1984.
From Your Mouth to Your Screen, Transcribing Takes the Next Step
John Markoff. 10/2/2019. “From Your Mouth to Your Screen, Transcribing Takes the Next Step.” The New York Times. Publisher's VersionAbstract
Improvements in automatic speech transcription are beginning to have a significant impact on the workplace.
Your Employer May Be Spying On You - and Wasting Its Time
Rose Eveleth. 8/2019. “Your Employer May Be Spying On You - and Wasting Its Time.” Scientific American. Publisher's VersionAbstract
New technologies help companies monitor their workers’ every move. But do those data tell them anything useful?
Meet 'Chet'. His Employer Knows What Time He Woke Up Today.
Elliot Bentley and Sarah Krouse. 7/19/2019. “Meet 'Chet'. His Employer Knows What Time He Woke Up Today.” Wall Street Journal. Publisher's VersionAbstract

Follow a day in the life of our fictional American office worker as his company tracks what he does, where he goes and whom he meets.

The New Ways Your Boss Is Spying on You
Sarah Krouse. 7/19/2019. “The New Ways Your Boss Is Spying on You.” The Wall Street Journal. Publisher's VersionAbstract

It’s not just email. Employers are mining the data their workers generate to figure out what they’re up to, and with whom. There’s almost nothing you can do about it.

The Mystery of the Miserable Employees: How to Win in the Winner-Take-All Economy
Neil Irwin. 6/15/2019. “The Mystery of the Miserable Employees: How to Win in the Winner-Take-All Economy.” The New York Times. Publisher's VersionAbstract

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.

Can the Occasional 'Nudge' Make You Better at Your Job?
Laszlo Bock. 4/12/2019. “Can the Occasional 'Nudge' Make You Better at Your Job?”. Publisher's VersionAbstract
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. 
Train Your People to Think in Code
David Waller. 4/11/2019. “Train Your People to Think in Code.” MIT Sloan Management Review. Publisher's VersionAbstract
Taking a code-centered approach to work will benefit organizations in three ways.
Watching the Workers
Tam Harbert. 3/16/2019. “Watching the Workers.” Society for Human Resource Management. Publisher's VersionAbstract
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.

Adam Grant. 2/5/2019. “The Surprising Value of Obvious Insights.” MIT Sloan Management Review. Publisher's Version
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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Artificial Intelligence and Ethics
Jonathan Shaw. 1/2019. “Artificial Intelligence and Ethics.” Harvard Magazine. Publisher's VersionAbstract

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.

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