1) Masahiro Fukuhara\'s early experiences led him to believe that while other pe
ID: 348813 • Letter: 1
Question
1) Masahiro Fukuhara's early experiences led him to believe that while other people could assess an individual's skills, individuals were not able to change or act on that feedback.A) True B) False
2) Reinforcement machine learning" is an AI capability that would automatically adjust the profile of an ideal candidate based on which past candidates have succeeded (or not succeeded) at the company. A) True B) false
3) One of GROW's vulnerabilities is that evaluators' ratings are all weighted equally. A) true B) false
4) Examples of competencies which could be assessed through GROW include resilience, decisiveness, and having a global mindset. A) True B) false
5) GROW's target clients are HR departments at large companies. A) true B) false
6) The case shares details of how GROW is being used (or tested) at three companies--Septeni Holdings, All Nippon Airways, and Mitsubishi Corporation. Which of the following are insights/benefits enabled by GROW at these three companies? (select all that apply)
A) Screening out candidates that were unlikely to make it to a final interview B) Surfacing candidates which would have been overlooked by the traditional hiring process C) Recruiting peers of "ideal " candidates, even if they did not directly apply D) Reducing the effort of the recruiting process without sacrificing candidate quality
For the exclusive use ot Y. Sherhan, 2018. HARVARD BUSINESS ScHOOL 9-418-020 GROW: Using Artificial Intelligence to Screen Human Intelligence Masahiro Pukuhara, founder and CEO of Tokyo-based people analytics startup Institution for a Global Society (ICS), took a break from poring over data to question how it would best be used. In the seven years since Fukuhara had founded IGS, its solution to evaluate job candidates-"GRow -had grown quickly, and the wide variety of ways clients used it simultaneously excited and concerned him. GROW, an iOS and Android app (Exhibit 1), consisted of two proprietary components: a competency assessment and a personality assessment. To assess competencies, GROW employed a peer-feedback tool to reveal 25 specific competencies which ICS had chosen based on extensive social science rescarch (Exhibit 2). To assess personality, GROW employed a gamified version of the Implicit Association Test (IAT), an established assessment of hidden bias in social psychology, that individuals could play on mobile devices (Exhibit 3). While neither component was particularly novel, what GROW did with them was: it used artificial intelligence(Al) learning algorithms to analyze every speck of assessment data from both the candidates and the evaluators, looking, for patterns to improve its ability to accurately screen candidates over time (Exhibit 4). In place of human "intuition," GROW used "big-data"-disparate data points across many people- to develop a scientific, objective, and constantly-improving engine to recruit, screen and develop human capital So far, it seemed to work. For example, in a test of GROW, one client had both its HR professionals and GROW evaluate the same 200 students. GROW not only surfaced nearly the same top 50 candidates (the two lists of 50 were statistically indifferentiable), but more importantly it did so with specific data-based, competency-based justifications As of June 2017, GROW had 74,000 users, including students at both prestigious and lesser-known universities. Clients included Mitsubishi Corporation, All-Nippon Airways (ANA), Septeni, DeNA, Rakuten, AXA, and many others. Even government entities like Japan's Ministry of Economy, Trade, &e; Industry (METI) and the UAE were getting involved. The widespread interest in GROW was both an opportunity and a challenge. On the one hand, it provided Fukuhara with a growing base of users, data, and institutional support. On the other hand, Fukuhara wondered if he should play a stronger role in strategically focusing the use of GROW where it was likely to have the most meaningful (and least potentially misleading) impact Profeor Ethan Berndtein, Senior Lecturer Paud McKinnon and Paul Yarabe (MBA 201 peepared this case with the ansistaince of Nobuo Suto 82, sol ly the bao kr clam di omim Cases are not intended bo "mo as endorsement, so unos of primary dala, orillustrations of effective or Capyright © 2017 President and Fellows of Harvard College Toorder copies or requnt permission to reproduce militia, call 14005457685 NaokoJinjs, Yukarl Takizawa, and Aliko Kanno in the HRSJapan Reanch Center. t was eviewed and approved before publication by a designabe. Funding for the development of this case was provided by Harvard usiness School and net by the company. company Hits cases are developed erite Harvard Biness School Publishing, Boston. MA.02363 or go to www.hlsp.harvandedu. This publication may not be digitized or odherwise reproducend, posted, or tranomited, without the permission of Harvard Buniness School. br use onty by oueet Shothan in Service Operations Managemn MOT 3121) taught by Cory Libanow, CUNY B Baruch College from January 2018 2018
Explanation / Answer
1) Masahiro Fukuhara's early experiences led him to believe that while other people could assess an individual's skills, individuals were not able to change or act on that feedback.
B) False
2) Reinforcement machine learning" is an AI capability that would automatically adjust the profile of an ideal candidate based on which past candidates have succeeded (or not succeeded) at the company.
A) True
3) One of GROW's vulnerabilities is that evaluators' ratings are all weighted equally.
B) false
4) Examples of competencies which could be assessed through GROW include resilience, decisiveness, and having a global mindset.
A) True
5) GROW's target clients are HR departments at large companies.
A) true
6) The case shares details of how GROW is being used (or tested) at three companies--Septeni Holdings, All Nippon Airways, and Mitsubishi Corporation. Which of the following are insights/benefits enabled by GROW at these three companies? (select all that apply)
A) Screening out candidates that were unlikely to make it to a final interview
B) Surfacing candidates which would have been overlooked by the traditional hiring process
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