Topic grow using artificial intelligence to screenhuman intelligence

Former approach that enterprises used to recruit before the applicability of GROW 131.2.. The approach that enterprises used to recruit after the applicability of GROW 142.. Former appro

UNIVERSITY OF ECONOMICS AND LAW

I INTRODUCTION 1

1.1 Former approach that enterprises used to recruit before the applicability of GROW 131.2 The approach that enterprises used to recruit after the applicability of GROW 14

2.1 Former approach that enterprises used to recruit before the applicability of GROW 142.2 The approach that enterprises used to recruit after the applicability of GROW 14

4 Summary 17

2.1 The conflict between expanding the use of GROW or only focusing on the areas where

it is likely to

2.2 AI can lead to some errors during the process of evaluating the competency and personalities of

2.3 The interaction between the HR department of a company and the candidates is

5.1 First issue - The conflict between expanding the use of GROW or only focusing on the

5.2 Second issue - AI can lead to some errors during the process of evaluating the competency and

TABLE OF FIGURES

Table 1 Summary of GROW application 11

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Table 25 Recommendations summary of fifth issue 43

PREFACE

Competencies and personality are two distinct but related concepts that are often used in the context ofassessing individuals in various domains, including education, employment, and personal development Competenciesrefer to the specific skills, knowledge, and abilities that a person possesses in order to perform a particular task or roleeffectively Personality, on the other hand, refers to the set of traits, characteristics, and behaviors that make up anindividual's unique psychological structure Competency and personality assessments have become increasinglypopular in both education and recruitment settings There are several reasons for this demand, such as identifying thestrengths and weaknesses of candidates, making informed decisions, enhancing development, improving fit, andreducing bias in the recruiting process However, recruiting talented employees can be challenging for anyorganization and some common problems are the competition with other businesses and finding qualified candidatesthat best suit the recruiting firms’ job requirements That is why GROW was created as it is a software application thatuses artificial intelligence (AI) to screen human intelligence It is designed to help organizations and businessesidentify the best candidates for a job, particularly those who have the necessary cognitive abilities and problem-solving skills In this case study, objectives, key issues and recommendations are discussed and proposed to remedyand improve the current state of GROW

1 Introduction about IGS and Masahiro Fukuhara

1.1 IGS

Institution for a Global Society (IGS) is a platform for the development of individuals, society, andglobal citizenship to analyze people and teams in HR and Education, using data-driven insights to helpcompanies make better decisions about hiring, employee engagement, and team building Its solutions includeGROW, which uses an installed AI engine to scientifically measure competency and disposition, provide avisualization of ability, and create job matches for new graduates based on their calculated compatibility Thecompany has been recognized for its innovative approach to HR and was named one of Japan's 50 mostdisruptive startups by Disruptor Daily in 2018

Capital: JPY 391 million (as of December 31, 2022)

Founder: Masahiro Fukuhara

Established in: May, 2010

Location: Head Office 150-0022, 4F, 1-11-2 EbisuMinami, Shibuya-ku, Tokyo 150-0022, Japan.〒

1.2 Masahiro Fukuhara

Masahiro Fukuhara is a founder and CEO of Tokyo-based people analytics startup Institution for aGlobal Society (IGS), which he started in 2010 Prior to founding IGS, Fukuhara was a Managing Director atasset management firm Barclays Global Investors (BGI) where he made investment decisions based oncomputer-driven models He has nearly two decades of working experience in data analysis and machinelearning, came up with the idea of using artificial intelligence learning to build an application to help changepeople's behavior for the better and to assist HR in recruitment

Concerning Fukuhara’s academic level, he earned his Bachelor's degree in Economics from KeioUniversity and MBA from INSEAD He also holds a Master's degree (with Honors) in International Financefrom Grandes Ecoles HEC and a Ph.D from Tsukuba University Graduate School of Business Sciences He iscurrently a Project Professor at the Economics Department at Keio University, a Visiting Professor at TokyoUniversity of Science, and a Project Professor at Hitotsubashi University Graduate School

2 Introduction about GROW

2.1 The journey of establishment - GROW’s history

Before starting IGS, Fukuhara was a managing director at asset management firm Barclays GlobalInvestors (BGI) where he made investment decisions based on computer-driven models

The mantra at BGI was “quantify everything,” and it instilled Fukuhara with the belief that quantitativejudgment would result in greater risk-adjusted returns Following Black Rock’s acquisition of BGI, and sensing

an opportunity to pursue his curiosities for himself, Fukuhara left the firm and founded an educational venture,IGS At first, Fukuhara ran IGS as a small private cram school in a bustling part of Tokyo In early 2015,Fukuhara began holding weekly brainstorming sessions with former colleagues, friends, and researchers toflesh out a tool that can assist candidates in identifying their key strengths and weaknesses while at the sametime helping human resources in hiring candidates based on required personality traits and competencies.Masahiro Fukuhara proposed the idea: instead of using human “intuition,” GROW used “big data” in order todevelop a scientific, objective, and constantly improving engine to recruit, screen, and develop human capital

2.2 Details of GROW

2.2.1 What is GROW

GROW is an artificial intelligence platform and mobile app developed by Tokyo-based peopleanalytics startup IGS to recruit and analyze employees GROW is designed to help organizations screen jobcandidates based on their human intelligence, competencies, personality traits, and other factors Theplatform uses a combination of Artificial intelligence learning algorithms and natural language processingtechniques to evaluate the assessment data from the evaluators and candidates and provide insights that canhelp organizations make more informed hiring decisions

From the 7-year milestone of establishment, GROW has taken steps to develop and successfully gain

a number of trusted customers, since then there have arisen many new problems that need to be solved suchas:

o Building a team of specialists to perform management and supervision work for future marketexpansion goals.

o Setting security policies to avoid leaking customer information, especially to third parties that cancause damage to both parties

o IGS started providing GROW as a “Software as a Service” tool for interested companies by using

a B2B2C strategy and began providing GROW to HR functions in organizations

Number of users and visits:

o Starting in 2015 with around 2000 users, until June 2017, GROW had 74,000 users and clients,including students at prestigious universities

o Looking at the modest figure of 2,000 participants in December 2016 and the growing number to74,000 users in 2017, it can easily be seen that the strategic path chosen by GROW is perfectlysuitable, and the team's output will aim to complete and correct outstanding shortcomings.Key partners:

o Funding organizations and investors: IGS’s education arm,

o Marketing support: Asahi Shimbun, a prominent media company,

Customer segmentation:

o Customers include Mitsubishi Corporation, All Nippon Airways (ANA), advertising companySepteni, DeNA, Rakuten, AXA and many others Even government organizations such as theMinistry of Economy, Trade & Industry of Japan (METI) and the United Arab Emirates start useGROW in a variety of different ways to manage talent recruiting, screening, hiring, placement,and development

2.2.3 How GROW collects, gathers data and applies AI in its platform:

GROW collects and gathers data in several ways, and applies AI technologies to analyze theinformation

Self-Assessment: GROW uses self-assessment surveys to gather information about individuals' goals,challenges, and progress The platform also collects data on individuals' skills, strengths, andweaknesses

IAT personality assessment: is an implicit association test developed to assess personality GROWcopyrighted technology uses finger movement recognition to assess one's personality based on the theory

of Big Five personality traits GROW is the only assessment solution available on smartphones that

offers bias-free assessment methods previously used by international organizations AI is used to analyzeand visualize the results of IAT personality assessment.

Goal Tracking: GROW allows individuals to track their progress towards their goals The platformcollects data on individuals' activities, such as the number of times they complete a task or the amount oftime they spend on a particular activity

Feedback: GROW collects feedback from individuals and teams through surveys and assessments Theplatform uses this feedback to help individuals and teams identify areas for improvement Candidateswill receive a secured Uniform Resource Locator (URL) code with their personal assessment record, thisdocument can be used after the assessment has ended

Performance Data: GROW collects performance data from various sources, such as social media, fitnesstrackers, and productivity apps This data is used to provide personalized recommendations andcoaching

2.3 How GROW works

2.3.1 Overview

User creates a GROW account and completed an in-app tutorial, she or he could evaluate thecompetencies of another user (classmate, coworker, acquaintance and so on), complete a self-evaluation, orcomplete the IAT IGS used its AI algorithms to analyze the resulting data, from both candidates andevaluators, in order to develop and customize HR-related services for clients and users

The primary functions of GROW are:

Powering GROW: GROW uses a powerful AI engine to analyze candidate data and generate insights.The platform is designed to be highly scalable and can handle large volumes of candidate data.Competencies assessment: GROW employed a peer feedback tool to reveal 25 specific competencieswhich IGS had chosen based on extensive, social science research The platform uses AI learning

such as emotional intelligence, adaptability, and resilience The platform uses natural languageprocessing techniques to analyze candidate responses and provide insights into their personalitytraits

AI ratings and evaluator: Finally, GROW generates AI ratings and evaluations for each candidatebased on their competencies and personality traits These ratings can be used to compare candidatesand make more informed hiring decisions

Candidate's user flow

Business Process Modeling Notation (BPMN):

IGS recommends setting a group of five members for an in-house high performers assessment, theassessment course can easily be sent to members using a URL link by sending directly to the member'smailbox Candidates should access the provided URL and log in to the assessment course consisting of theIAT personality assessment, competencies self-assessment, and competencies peer assessment conductedwithin the group Once the high performer's data has been collected, IGS will conduct data analysis in order

to define five or six competencies considered high-performance factors within the organization This will beused in further recruitment processes The hiring company will distribute the assessment course torecruitment candidates who will then ask friends or colleagues for peer assessments Based on this data, IGS

will provide data-driven recommendations for talent acquisition that a minimum of five business days arerequired to prepare the assessment course.

Figure 3.

Step-by-step workflow

Figure 4.

Using GROW

Start the GROW app, users can create a GROW account and complete an in-app tutorial which wouldthen allow him or her to evaluate the competencies of other users whether that be a classmate, a co-worker,

an acquaintance, whoever it may be complete a self-evaluation as well as complete an IAT

Figure 5.

Competency Evaluation in GROW

The competency evaluation within the GROW app, the first list is a competency chart selected fromhundreds of competencies and skills reported in the social science literature based on several criteria, acorrelation to relevant job functions is being accessible by peers In the second chart, a request can bechosen to know the form of "gifts" - the four-point scale to evaluate users makes the evaluation easier.

Figure 6.

Implicit-Association Test (IAT), GROW’s Personality Assessment

An implicit association test is a personality assessment The IAT test is connected to personalitytraits, which is used to review attributes and biases people have users progress through a series of implicitassociation tasks These are measured based on where users drag certain attributes to the correct attributes aswell as the user swiped the movable attribute to predict personality traits This allowed GROW to use itsmachine learning to reveal anomalies and patterns of swiping behavior and better predict personality.

Figure 7.

How the Artificial Intelligence in GROW Works

In artificial intelligence within the GROW app, the users must receive an average of four to fiveevaluators In addition, in the self-evaluation in order for the AI algorithm to use the data in severalmanners, AI uses many data points to determine the likelihood and evaluation was genuine and valid

3 Summary about GROW

3.1 Overall

Table 1.

Summary of GROW application

General information

Genre A gamified mobile consumer app

Distribution channels Mobile application (IOS & Android App)

Key partners Funding organizations and investors: IGS’s education arm,

Marketing support: Asahi Shimbun, a prominent media company,

Clients: All Nippon Airways (ANA), Septeni, DeNA, Rekuten, AXA, Government entities: Japan’s Ministry of Economy, Trade & Industry (METI), the United Arab Emirates,

The relationship among

users

HR-related for clients and users

Key activities Gamified personality assessment

Feedback peer assessment systemCandidate’s assessmentIdentifying potential talent among candidates Providing performer analysis

Unique Selling Proposition

(USP)

Application that can identify potential talent and top talent among candidates

3.2 Summary of GROW’s functions

Table 2.

assessment tool to reveal 25 specific

competencies Request can be chosen to know the form of "gifts"

- the four-point scale to evaluate users

proficiency in these competencies through self-assessment

3 Weighting HR and Candidates GROW assigns weights to different

competencies Users received ratings from multiple evaluators

Helps organizations prioritize candidates who have the skills and experience that are most important for the position

evaluator HR and Candidates GROW generates for each candidate based on their

competencies and personality traits

Compare candidates and make more informed hiring decisions

II HOW BUSINESSES USE GROW

1 Septeni

Septeni is a Japanese digital marketing company that provides a range of services related to onlineadvertising, including search engine marketing, display advertising, social media marketing, and mobileadvertising The company was founded in 1997 and is headquartered in Tokyo, Japan Septeni has been recognizedfor its innovative approach to digital marketing, and has won numerous awards for its work in the field

1.1 Former approach that enterprises used to recruit before the applicability of GROW

Employees are invited to their company's Tokyo office for multiple rounds of interviews, during whichtime groups of applicants are observed working together to complete Septeni and other typical team-based jobtasks Employees have only one chance in a group interview before the company makes a hiring decision.Weaknesses and challenges of the approach:

o Causing the waste of money and time because employees have to go directly to Septeni forinterviews

o Employees are not willing to invest time and money to join because Septeni is just a mid-sizedcompany

GROW collects evaluations about candidates during their study and work at the university from otherstudents and people around the candidate through a peer-feedback mechanism.

GROW also implements an AI algorithm based on data on information and interview results aboutemployees from previous years, thereby predicting and finding suitable employees to pass Septeni's interviewround

The result of using Grow:

Lower the whole business processing process by 90% while maintaining candidate quality

When compared to the past, the proportion of applicants that accept employment offers has grown by 4times

Two times as many qualified candidates are coming from outside of Tokyo

Radically boosting Septeni's brand recognition among potential customers and students

2 All Nippon Airways

All Nippon Airways (ANA) is a Japanese airline headquartered in Tokyo, Japan ANA was founded in 1952and has grown to become a major player in the global airline industry It is the largest airline in Japan by passengernumbers and revenue ANA operates a wide range of domestic and international flights to destinations throughoutAsia, Europe, North America, and Oceania In addition to its passenger services, ANA also operates a number ofsubsidiaries, including ANA Cargo, ANA Wings, and Peach Aviation, a low-cost carrier

2.1 Former approach that enterprises used to recruit before the applicability of GROW

ANA selects candidates by hand from the numerous applications they get to create the upcoming seniorleadership team

GROW's AI engine creates a "confidence score" based on all information the IGS gathers about the student andtheir assessors to determine how accurate the IGS is in the outcomes ANA then shows the evaluation results(point chart) for each candidate.

Figure 8.

Point chart of ANA

(The candidate's place in the screening process is shown by the "Total Score" on the x-axis, the

"Confidence Score" on the y-axis, and the color of the dot

Candidates with a "Total Score" of less than -5 and a "Confidence Score" of less than -4 are not likely to

be Finalist green dot, therefore ANA may utilize GROW to screen out those who will not advance to the finalinterview stage.)

The result of using GROW:

o Enhancing capacity to identify brilliant applicants for the recruiting process with high accuracy

o Enhancing capacity to choose pupils, creating, and fine-tuning their evaluation standards

o Targeting more precisely the group of students with high potential in the recruitment process

3 Mitsubishi

Mitsubishi is a Japanese multinational conglomerate with a wide range of businesses, including automotivemanufacturing, electronics, finance, and more The company was founded in 1870 and is headquartered in Tokyo,Japan Mitsubishi has a long history of innovation and has contributed to the development of various industriesaround the world Apart from the automotive industry, Mitsubishi is also involved in a variety of other businessessuch as electronics, heavy industry, and finance The company produces a wide range of products such as airconditioners, power generation systems and industrial machinery.

3.1 Former approach that enterprises used to recruit before the applicability of GROW

At first, Mitsubishi used the conventional method for hiring The need for top-notch human resourcesgrew as Mitsubishi sought to enhance its business strategy However, the business had several challenges inrecruiting excellent candidates and effective management teams

Weaknesses and challenges of the approach: Mitsubishi had difficulty in recruiting a large number of

high-quality candidates when their demand for employees increased during the development of business

3.2 The approach that enterprises used to recruit after the applicability of GROW

IGS and Mitsubishi collaborated to develop an algorithmic model of the ideal Mitsubishi worker Based

on the premise that individuals who were the excellent candidate's age might also be the best match for theorganization, IGS located persons who were peers of the ideal people and who had five or more abilities incommon with those of the company's ideal candidates Thereby, Mitsubishi could find excellent candidates

The result of using GROW:

o Finding out the ideal candidates who have not filled out the company's recruitment form and invitingthem to a meeting to encourage them to join your company

o Reaching out to new talented candidates who do not have much information about Mitsubishi

III OBJECTIVES

Helping businesses assess the ability and personality of candidates: Big data will help meet this expandingneed by providing pre-hiring assessment and sourcing tools for the requirement Also, incorporating assessmenttools helps lower the number of applicants for a given position and raises the general caliber of the hiredindividuals.

Making recruitment fair: By using modern AI technology, errors resulting from personal bias areunavoidable because GROW uses peer feedback to analyze candidate data during the process However,misjudging a candidate's qualifications will result in ineffective hiring So, limiting bias in the evaluation processhelps ensure the quality of human resources and makes recruitment fair

Creating a premise for future developments: By demonstrating its technological capabilities, GROW gainsthe confidence of Japanese businesses and gains momentum toward becoming a useful tool for hiring As a result,numerous contracts were made with Japanese businesses, helping GROW increase its user base

Toward the desire to help students develop: Identifying the strengths and weaknesses of individuals helpseach individual take appropriate action to make changes to promote strengths and overcome weaknesses, whichimproves the hiring and human resource development process

3 Setting goals

Regarding helping businesses assess the ability and personality of candidates, GROW must gain efforts toshorten recruitment time for client businesses to 40% compared to before, helping businesses save at least 80% ofrecruitment costs

To make recruitment fair, GROW aims to develop high-precision data collection and analysis technology,increasing the accuracy of recruitment to 98%

According to the objective of creating a premise for future developments, GROW needs to serve more than40% of Japanese university graduates every year

To help students develop, in 2023 GROW must reach 300,000 users, with a growth rate of at least 10% peryear

4 Summary

Table 3.

Summary of GROW’s objectives and goals

Helping businesses assess

the ability and personality

of candidates

Making recruitment fair Creating a premise for

future developments Toward the desire tohelp students developCurrent

situations

(data)

As of June 2017,GROW had 74,000users, including students

at both prestigious andlesser-knownuniversities.Goals - Shortening recruitment

time for client businesses

of recruitment to 98%

Serving more than 40%

of Japanese universitygraduates every year

In 2023 GROW mustreach 300,000 users,with a growth rate of atleast 10% per year

IV KEY ISSUES

1 List of key issues

The conflict between expanding the use of GROW or only focusing on the areas where it is likely to have themost meaningful impact

AI can lead to some errors during the process of evaluating the competency and personalities of candidates.The interaction between the human resources department of a company and the candidates is limited.GROW can be competed by other competitors in its industry

The standards of competency and personality assessment are limited

2 Analysis of key issues

2.1 The conflict between expanding the use of GROW or only focusing on the areas where it is likely to

However, this advancement can also pose a great threat for the platform In particular, the growing number ofusers and data has encouraged the company to expand the use of GROW in a wider scope, but this decisionmay create a potentially misleading effect that the AI learning algorithms cannot be used to the areas that they

do not receive enough training and input data Specifically, due to a large amount of customer input data in thenewly expanded sectors, any redundancy in the data may cause failures in learning, thus the AI algorithms mayshow unpredictable results

Moreover, the company has to remember to update the data and information systems frequently if theywant to expand the use of GROW on a large scale If not doing so, due to the lack of improvement, theinconsistency in the results may happen when the AI machine learning cannot have the ability to process bits ofinformation

Besides, if the AI machine learning does not have enough data about the novel areas, then the AIapplication can generate inaccurate results and cause great losses This is also a matter that can affect GROW’scurrent working efficiency

As a result, the conflict between the GROW expansion into new areas or only concentrated on thesectors where it possibly has the most powerful effects becomes a major problem for the development ofGROW

2.2 AI can lead to some errors during the process of evaluating the competency and personalities of candidates

The functions of evaluation about the competency and personalities of candidates become core valuesthat GROW brings to its company customers and helps its platform achieve the remarkable milestones in thenumber of users However, while GROW uses AI to reduce diagnostic errors that the traditional interviewprocess often suffers from, there is the risk that the use of AI can introduce new potential errors during theprocess of evaluating the competency and personalities of candidates Specificially, candidates who completethe test on the GROW platform can recceive inaccurate results about their personalitites and competency, as thetest does not reflect the actual evaluation This is the negative result when the GROW’s AI systems cannotflexibly adapt to the differences between the real-world situations, the proper data and the situations used intraining the intelligence The major reason is because AI systems are not as equipped as humans to recognizewhen there is a relevant change in context or data that can impact the validity of learned predictive

assumptions Therefore, AI systems may unknowingly apply programmed methodology for assessmentinappropriately, resulting in errors

2.3 The interaction between the HR department of a company and the candidates is limited

Thanks to the advancement of GROW and its benefits, companies and organizations can save a largeamount of time and money recruiting candidates that best fit their demands and their job requirements.Moreover, this type of recruitment can help students opt out of paying expensive travel costs for the interviewthat are far away from their living locations However, this kind of recruitment still reveals some challenges,typically the interaction between the HR department of a company and the candidates is reduced Particularly,

by using the GROW's predictions on evaluating candidates, the company eliminates the traditional face-to-faceinterview, thus the HR department does not need to create an offline interview with their candidates This leads

to the elimination of the face-to-face interview significantly and hence reduces the interaction between thecompany and the candidates The cohesion and understanding between the company and the candidate cantherefore disappear because the company does not understand the personality of the candidates by person, butonly through the data that GROW collects and the AI learning machine generates

2.4 GROW can be competed by other competitors in its industry

Throughout the research from the case study, it can easily be seen that GROW’s business network onlyconcentrates on targeting three primary markets: Japan, France and The United Arab Emirates Its primaryfunding company that supports for its marketing activities is Asahi Shimbun, one of the most popular dailynewspapers in Japan This enterprise primarily enhances GROW’s marketing strategies in Japan As a result,GROW has not focused a lot on marketing activities on a larger scale outside Japan yet And this leads to thelow level of GROW’s brand identity GROW can also be affected by the competition from numerous fiercecompetitors These companies with huge budgets can enhance their marketing activities to its large targeting

personality assessment model can help the recruiting firms easily find out special characteristics of eachpotential candidate However, the use of the Big Five personality traits can cause a wide variety of limitations.First, it may screen out qualified candidates For many jobs, there is not a mainstream personality that fits thejob type Such tests may also exclude talented candidates who think outside the box Secondly, this model ofevaluation can cause flawed results Candidates may respond based on what they think the employer wantsrather than on their true personalities; therefore, results are not always accurate Moreover, the purpose of thetest may not fit into the recruiting firms’ hiring process In particular, there is an increase in the number ofpersonality requirements for a position in a company, while the model of personality evaluation only assessescandidates based on the consistency of behaviors in certain situations Besides, GROW only provides 25behavioral characteristics when assessing the competency of candidates As a result, the standards ofcompetency and personality assessment are still restricted that need to be updated.

3 Summary of key issues

Table 4.

Summary of GROW’s key issues