To complete this Assignment, review the Learning Resources for this week and other resources you have found in the ?online, then respond to the following bullet points in a 4- to 6-

ARTICLE HBR CASE STUDY Trust the Algorithm or Your Gut? A VP decides which candidate to promote. by Jeffrey T. Polzer

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to see her longtime colleague Anne Bank go, she was more consumed with trying to figure out who should replace her.

As a VP of sales and marketing for Becker-Birnbaum International, a global consumer products company, Aliyah knew she needed a talented marketing director to support her division’s portfolio of 34 products. After working with HR to narrow down the list of candidates, she had two finalists, both internal: Molly Ashworth, a brand manager on her team in the cleaning division, and Ed Yu, a rising star from BBI’s beauty division.

Aliyah liked Molly and respected her work. Two years earlier, Molly had spearheaded a new subscription service for BBI cleaning products, which had shown strong growth in the past two quarters. Customers seemed to love the convenience, and the R&D, marketing, and executive teams had gotten excited about the service as a platform to test new offerings. Having mentored Molly through the pitch and launch of the service, Aliyah was intimately familiar with her protégé’s strengths and weaknesses and was certain that she was ready for the next challenge.

But soon after the position had been posted, Christine Jenkins, a corporate VP of HR, had come to Aliyah with Ed’s résumé. Like Molly, Ed had joined BBI right out of business school and been quickly tapped as a high potential. He also had his own BBI success story: As a brand manager in the beauty group, he had revived its 20-year-old FreshFace makeup-removal product line, increasing sales by 60% in three years. Perhaps more important to Christine, he’d been recommended as a 96% match for the job by HR’s new people-analytics system, which she had championed. (Molly had been an 83% match.) The goal of the initiative was to expand the use of data analytics to human

Aliyah Jones was having trouble paying attention to the farewell toasts. Although she was sad

CASE STUDY TRUST THE ALGORITHM OR YOUR GUT? A VP DECIDES WHICH CANDIDATE TO PROMOTE. BY JEFFREY T. POLZER

JEFFREY T. POLZER is the UPS Foundation

Professor of Human Resource Management in the organizational behavior unit at Harvard Business School.

HBR’s fictionalized case studies present problems faced by leaders in real companies and offer solutions from experts. This one is based on the HBS Case Study “Susan Cassidy at Bertram Gilman International” (case no. 417-053), by Jeffrey T. Polzer and Michael Norris.

CASE STUDY CLASSROOM NOTES Companies use algorithms in people-related decisions for many reasons, including consistency, reduced bias, casting a broader net, and efficiency. How might the recommendations an algorithm makes differ from those of a hiring manager who is not using data analytics?

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resources, to inform hiring, promotion, and compensation decisions. Aliyah was glad to see two insiders in contention—she’d come up the ranks herself—but that made the decision harder.

As the COO made a toast to Anne, Aliyah considered her interviews with Ed and Molly.

MEETING ED YU “I’m sorry I’m so late,” Ed said, looking a little discombobulated. “My Uber driver insisted he knew a shortcut from Heathrow—but he was wrong.”

Aliyah couldn’t help drawing an immediate comparison with Molly, who was always steady and calm, but she tried to keep an open mind.

“No problem,” she said. “Shall we get started?”

“Absolutely,” Ed said eagerly. “What interests you about the job?” Ed explained that while he was

proud of the growth FreshFace had seen under his leadership, he was ready for a new challenge. He’d enjoyed diving deep into one product but felt his skills were better suited for a position that would allow him to work across programs and direct a larger portfolio.

Sharp, clear answer, Aliyah thought. “What have you learned in beauty that would apply in cleaning?” she asked.

This was an important question. BBI’s top team had directed the divisions to share more best practices and improve collaboration. In fact, her boss wanted her to work more closely with her peers in other divisions.

Ed explained how he thought his division’s approach to in-field customer research, which he credited with boosting FreshFace sales, could work in cleaning. Partnering with anthropologists was something Aliyah’s team had talked about but

hadn’t yet tried out. He also asked about the

new subscription program, referencing a recent white paper on trends in subscription business models. He’d clearly

done his homework, was smart and ambitious, knew BBI’s business well, and seemed eager to learn. But his answers and even

his questions seemed a bit stiff.

Aliyah didn’t sense the dynamism or entrepreneurial mindset that she knew Molly had. Maybe he’s nervous, she thought. Or maybe that’s just who he is.

Aliyah didn’t doubt Ed could do the job. But she didn’t feel excited about hiring him.

MOLLY’S “INTERVIEW” Setting Molly’s interview up for the same day as Ed’s had seemed like a great idea when she’d suggested it to Christine, and given the noon time slot, it had been only natural to meet at their usual lunch spot near the office. But as soon as Aliyah walked into the café, she realized how unfair these back-to-backs were to Ed.

It was impossible not to hug Molly hello and ask for a quick update on her projects and family. They even ordered the same thing: curried egg salad. But as soon as the waitress left, Molly got down to business: “I know we e-mail 10 times a day, but I’d like to treat this as a formal interview.”

Aliyah smiled. “Of course.” As Christine had advised her to do,

she asked questions that were the same or similar to the ones she’d asked Ed.

“Tell me why you’re interested in this job,” she started. It was awkward. Aliyah knew the answer already, but to Molly’s credit, she proceeded as if they weren’t close colleagues. With each response, she demonstrated deep knowledge of the business, and she had good ideas for collaborating across programs and building on the success of the subscription program. She was as polished and thoughtful as Ed, but she also seemed warmer and more self-aware.

Knocked it out of the park, Aliyah thought, as they walked back to the office. Looking at the smile on Molly’s face, Aliyah knew her protégé was feeling confident that she’d done well.

THE ALGORITHM The day after Anne’s farewell party, Aliyah met with Christine and Brad Bibson, a data scientist on the people analytics team.

“I know you were leaning toward Molly after we debriefed the interviews,” Christine said, “but we wanted to share some more data.”

Unstructured interviews are the default method for most hiring managers, but numerous studies have found them to be poor predictors of actual on-the-job performance.

Managers tend to hire people similar to themselves, studies show. For example, Kellogg School of Management professor Lauren Rivera found that managers prefer recruits who have the most potential to become friends, even over those who are more qualified. Should Aliyah worry that she’s choosing Molly because she likes her?

Does using algorithms for any type of people analytics violate employees’ privacy? New laws—in particular, the EU’s General Data Protection Regulation (GDPR)—are setting limits on what information employers can and cannot collect, and how employees must be notified.

Research shows that hiring managers typically form opinions about a candidate’s personality and competence in the first 30 seconds of an interview.

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CASE STUDY TRUST THE ALGORITHM OR YOUR GUT?

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Brad handed over two colorful diagrams. “These are network analyses of Molly’s and Ed’s e-mail and meeting history at BBI. With their permission and without looking at the content of their e-mails or calendars, we analyzed who they’d been in contact with across the firm over the past six months.”

It was clear from the diagrams that Ed was connected to not just his beauty division colleagues but also key people in other groups. Molly’s network was mainly within cleaning products.

“I didn’t know we were doing this kind of analysis,” Aliyah said.

“We’ve just started looking at networks,” Brad said, “and we think they can reveal useful insights.”

“I know one chart isn’t going to sway your decision,” Christine said, “but better to have the data, right? You wouldn’t launch a new product or a new campaign without data. HR decisions should be approached the

same way.” It was a pitch that Christine had made countless times while stumping for the new initiative. “We’re confident

that decisions made using our algorithms are reasoned, strong, and less biased by personal feelings

toward employees,” she said. Aliyah turned to Brad. “I assume

you agree?” “Of course,” he said, watching for

Christine’s reaction. “But as a data scientist, I also encourage healthy skepticism. Our algorithm is brand- new. We’ve used it to inform three promotion decisions so far, but it’s too early to tell how those people are doing. I don’t want to give the impression that we’re 100% confident.”

Christine looked annoyed. “I appreciate your caution, Brad, but we’ve heard from the hiring managers that the type of recommendations the algorithm provides is changing the way they think about positions and candidates. And we’ve been testing the system for months now.”

Aliyah sighed. “I’d trust the algorithm more if I understood it better.” She knew she wasn’t alone in her hesitation: Christine’s team had gotten a lot of questions about the methodology, despite the companywide training sessions.

“I’d be happy to talk more about how the algorithm works,” Christine replied, “but right now you should focus on the two candidates. The point of the system isn’t to replace your judgment. The aim is to surface qualified people you wouldn’t otherwise know about so you can make a more informed decision.”

“It’ll help you make a less-biased decision too,” Brad chimed in, “by relying more on the data and less on gut instinct.”

Aliyah wondered whether Brad thought she was unfairly favoring Molly. She worried about that herself and cared deeply about making an objective decision. Would trusting the new system help her do that?

“But the algorithm’s not completely neutral either, right?” she said. “You’re still relying on information— performance reviews, résumés—that conceivably has bias baked into it.”

“Fair point,” Christine conceded, “and we’ve worked hard to control for that. But as a data-driven firm, we have to extend our approach to the most important part of our business: people.”

“It feels like you’re pushing Ed for this position,” Aliyah said.

“Remember, I have to take a broader view,” Christine said. “We ran analysis to show which high potentials are at risk of leaving BBI, and Ed was near the top of the list. There is not likely to be an opening in beauty products, and we want to keep him.”

“But what about Molly?” Aliyah said. “She’ll be devastated if she doesn’t get this job, and I’m sure she’d start looking too.”

“Our analysis didn’t flag her as a flight risk,” Brad said. “But you could be right.”

DECISION TIME A week later, Aliyah wasn’t any closer to a decision. She’d been avoiding Molly and had put Brad’s analyses in a drawer. Ed was clearly qualified, and he’d impressed her. But she knew intuitively that Molly was ready for the job.

Did she prefer Molly because of their close relationship? Would Molly stay at BBI even if she was passed over?

Aliyah needed to make a decision. Should she trust the algorithm or her instincts?

Data scientist Cathy O’Neil warns in her book Weapons of Math Destruction that although algorithms are fairly easy to create using historical data and can improve the efficiency of decision making, people often rely on them without understanding the biases they may be propagating.

How does using algorithms to analyze customers differ from using them on employees? Should companies be more cautious in implementing these methodologies internally?

Network analyses can reveal patterns that are otherwise hard to see—for example, by identifying which employees are most central to informal information flows.

Studies have revealed a phenomenon called “algorithm aversion.” Even when data-driven predictions yield higher success rate than intuitive human forecasts, people often prefer to rely on the latter. And if they learn an algorithm is imperfect, they simply won’t use it. Under what conditions would you base a decision on data analysis? 

Along with managers, many applicants are skeptical of algorithms, according to Pew. A majority of Americans (76%) say they would not want to apply for jobs that use a computer program to make hiring decisions.

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