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QUESTION
Algorithms and Decision Making
Algorithms can aid in decision making. In the Harvard Business Review case Trust the Algorithm or Your Gut?, company VP Aliyah Jones reviews an algorithm to help make a decision on which candidate to promote.
To complete this Assignment, review the Learning Resources for this week and other resources you have found in the Walden Library or online, then respond to the following bullet points in a 4- to 6-page paper:
Introduce the topic of algorithms in the selection process. How might the recommendations an algorithm makes differ from those of a hiring manager who is not using data analytics?
How might using algorithms to analyze customers differ from using them on employees? Should companies be more cautious in implementing these methodologies internally?
Studies have revealed a phenomenon called “algorithm aversion.” Even when data-driven predictions yield higher success rates than human forecasts, people often prefer to rely on the latter. And if they learn an algorithm is imperfect, they simply won’t use it. Describe a situation where you would base a decision on data analysis.
Should Aliyah Jones choose Molly or Ed? Analyze each alternative solution. Consider the short-term and long-term implications. What are the advantages and disadvantages of each decision? Support your decision with two additional scholarly articles.
Note: You should make a firm case for one of the two candidates with the information in the case. Don’t suggest a committee or new selection tools or a new candidate pool.Outline the next steps of Aliyah Jones. What information should she give the candidates?
References: https://ezp.waldenulibrary.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=edsgea&AN=edsgcl.511506139&site=eds-live&scope=site
https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2018/people-data-analytics-risks-opportunities.htmlARTICLE
HBR CASE STUDY
Trust the Algorithm
or Your Gut?
A VP decides which candidate to promote.
by Jeffrey T. Polzer
REPRINT R1803X
PUBLISHED IN HBR
MAY–JUNE 2018
This document is authorized for use only by Aaron Brumidge in MGMT-6401-1/WMBA-6401-1/MHRM-6401-1-Human Resource Analytics2020 Fall Semester 09/07-12/27-PT2 at Laureate
Education - Walden University, 2020.
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?
FOR ARTICLE REPRINTS CALL 800-988-0886 OR 617-783-7500, OR VISIT HBR.ORG
MAY–JUNE 2018 HARVARD BUSINESS REVIEW 2
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Education - Walden University, 2020.
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.
COPYRIGHT © 2018 HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION. ALL RIGHTS RESERVED.
CASE STUDY TRUST THE ALGORITHM OR YOUR GUT?
3 HARVARD BUSINESS REVIEW MAY–JUNE 2018
This document is authorized for use only by Aaron Brumidge in MGMT-6401-1/WMBA-6401-1/MHRM-6401-1-Human Resource Analytics2020 Fall Semester 09/07-12/27-PT2 at Laureate
Education - Walden University, 2020.
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 brandnew.
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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SHOULD ALIYAH HIRE
Reprint Case only R1803X MOLLY OR ED?
MAY–JUNE 2018 HARVARD BUSINESS REVIEW 4
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Education - Walden University, 2020.
| Subject | Business | Pages | 14 | Style | APA |
|---|
Answer
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Algorithms and Decision Making
The ongoing digital revolution has had a tremendous effect on a significant portion of transformations in the modern world, particularly in business-related operations. Software developers are consistently seeking value-driven solutions by creating and implementing convenient algorithms to enhance human practice (Agarwal et al., 2018). Algorithms are commonly defined as step-by-step procedures that define the execution of processes to attain desired outputs. In the digital world, algorithms are used to instruct information machines such as computers, servers, and other devices on how they should cooperate to deliver anticipated results. The Google Search engine is a worthy example of a system which depends on internal web page crawling processes to showcase user’s search results (Cheng & Hackett, 2019). The past few years have seen the use of algorithms penetrate deep into human resource management. These innovative solutions are now playing pivotal roles in management processes involving the human resource (Agarwal et al., 2018). Common areas of application includes hiring, promotion, restructuring, and layoffs. In the wake of such a realization, the present piece offers a detailed exploration of the use of algorithms in human resource management, and how this trend affects decision-making.
Algorithms in the Selection Process
Innovativeness, productivity, and scalability are among the leading goals of organizations in the highly competitive business world, and they are largely dependent on an organization’s capacity to obtain and retain proficient workers. In an attempt to facilitate such growth, managers are seeking creative means of evaluating employees for selection processes (Agarwal et al., 2018). Algorithms have been used to shed light on useful variables for effective decision-making including revenue generated by employees (individually), risks of turnover, stakeholder reviews, interpersonal networks, and other factors. These key performance indicators can be leveraged to help managers make right decisions when acquiring workers to fill sensitive positions.
It appears wise to clarify that the role of algorithms in the selection process is mainly consultative: this implies that the innovation should not replace human reasoning. Rather, it should be applied to enhance the amount of information available for decision-makers to improve the outcomes (Agarwal et al., 2018). When placed into perspective, a leader seeking to fill a managerial position would be inclined to select a member of his/her specialized team leaders if analytical data on cross-department interactions affirm the potential recruit’s mastery of the organization’s processes. Absence of such a critical information would prevent the leader from noticing the talent in his/her team member. As a matter of fact, the leader might end up hiring an outsider who lacks a strong grasp of the firm’s operations, thus costing the business a lot of revenues.
The preceding example shows how algorithms improve the decisions of hiring managers when they utilize data analytics to make decisions. Hiring managers who disregard data analytics software tend to make bias decisions (Cheng & Hackett, 2019). As implied in the preceding example, the manager might recruit someone based on his/her track record in another company regardless of the fact that such an individual knows nothing about the organization’s operations. Such a decision is bound to cause unwarranted losses since the recruit will need some time to implement potentially erroneous processes before he/she learns fundamental realities of the business/organization. Meanwhile, a veteran employee with equal qualifications would be in a better place to scale the business as early as possible due to familiarity with organizational procedures.
The Need for Caution when Implementing Algorithms Internally
Since the emergence of intelligent systems, organizations have found means of gaining market insights. A significant portion of algorithms in the digital business sphere are designed to analyze customer trends. Some of these analytical tools focus on reviews, customer activity in online stores, social media interactions, and purchase patterns among others. Unlike in the case of external stakeholders such as customers, the use of algorithms internally can be plagued by bias. As controversial as the preceding postulation sounds, it is rightly grounded on the fact that some employees might offer bias information, thus resulting in misleading data analytics outcomes.
For instance, employee reviews on the potential recruit for a promotional position might be compromised by factors such as patronage and favoritism: people are highly likely to favor an individual who they like when given the choice to cast votes between two equally qualified individuals. This scenario is quite evident in the assigned Harvard Business Review case study by Jeffery Polzer: noteworthy is Aliyah Jones’ inclination towards Molly Ashworh regardless of the fact that Ed Yu is equally qualified (Polzer, 2018). When viewed from such a lens, it is agreeable that the support Ed has from fellow employees might be largely attributable to his personal relations.
Considering the scenario described in the preceding paragraph, it is fair to argue that data analytics algorithms should be applied with caution when it comes to human resource management since they lack the neutrality that is often evidenced in customers. Patronage and favoritism can result in bias data analytics outcomes, thus compromising the entire decision-making process.
Algorithm Aversion
Algorithm aversion is a term coined from the skeptical nature of human beings when it comes to decision-making based on algorithms. According to Agarwal and colleagues (2018), people are highly likely to rely on logical forecast when making decisions, especially when an algorithmic systems records an error. Since little can be done to alter people’s perspectives on algorithms, it is fair to recommend the consultative use of this innovation. This implies that data analysis outcomes should be used to shed light on the decision-making process (Cheng & Hackett, 2019). For instance, when a company is recording high turnover rates, interview data regarding employee attitudes towards organizational aspects such as compensation, employment benefits, and workplace conditions can allow an analyst to determine the reason behind high turnover rates.
Reflection on Aliyah Jones’ Dilemma
Now that both data analytics and logic-based decision-making have shown to have advantages as well as disadvantages, one cannot help but acknowledge the dilemma faced by Aliyah Jones: both Ed and Molly are competitive workers whose capacity to lead in the new managerial remain unquestionable. Apart from his competence, Ed has a strong relationship with leaders from various departments (Polzer, 2018). Such a scenario implies that he is in a better position to offer direction and guidance to members of different units since they are familiar with his style. Unfortunately, his rigidity might hinder innovative growth in the long-term. On the other hand, Molly is a composed innovative leader, whose relationship with the top management is quite commendable. This kind of openness is instrumental for a middle-level manager since she is bound to serve as a convenient bridge to enhance progress. Unfortunately, her interaction with employees from other departments is quite limited, so, she is highly likely to face friction when she takes charge of the higher position (Polzer, 2018). Another alarming issue is the fact that her promotion is bound to result in Ed’s resignation, yet he is a valuable leader of the Beauty department. Given both scenarios, it seems fair to recommend Ed for the promotion. Change in leadership often steers friction in the workplace, especially when the team is not familiar with the person in charge. Ed’s bonds with fellow employees across the departments will go a long way in boosting organizational performance.
Recommended Steps for Aliyah
Below is a brief outline of what Aliyah has to do:
- Invite both Ed and Molly for a meeting
- Explain each individual’s strengths and weaknesses
- Key information Aliyah will convey here include each individual’s ability to deliver desired outcomes.
- Explain why Ed has to take the position
- Emphasis should be placed on the value of his networking capabilities.
- Expand Molly’s sphere of influence in the organization by partnering her with Ed in leading cross-departmental projects
- Molly’s unique leadership strength should be acknowledged, as well as how she will compliment Ed in his new position.
References
Agarwal, D., Lahiri, G., Bersin, J., Schwartz, J., and Volini, E. (2018). People Data: How Far is too Far? 2018 Global Human Capital Trends. Deloitte Insights.
Cheng, M. M., & Hackett, R. D. (2019). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 100698.
Polzer, J.T. (2018). Trust the Algorithm or Your Gut: A VP decides which Candidate to promote. Harvard Business Review