case study grow using ai to screen human inteligence

UNIVERSITY OF ECONOMICS AND LAW VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY CASE STUDY GROW - Using AI to screen Human Inteligence Class: Digital transformation and artificial intell

UNIVERSITY OF ECONOMICS AND LAW VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY

CASE STUDY

GROW - Using AI to screen Human Inteligence

Class: Digital transformation and artificial intelligence

Lecturer: Nguyen The Dai Nghia Class code: 222MI5216 Group members:

Abstract

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TABLE OF CONTENT

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GROW: USING AI TO SCREEN HUMAN INTELLIGENCE

Abstract Masahiro Fukuhara - CEO of Tokyo-based Human Analytics Startup Institute (IGS) proposed the idea to get rid of the human "intuition" and instead using “Big Data” as a rational tool to evaluate individual data points among many people, not only that, but also use integration with Machine learning and AJ to develop a logical, objective and constantly improved model

to enhance recruitment ability recruitment, screening and development of human resources After only approximately 7 years after establishment, GROW has achieved great milestones

in development and success gained trust by numerous customers with many ways of utilization

I/ Objectives and key issues

1 Objectives

From the momentum of the first successful 7-year milestone, the current top goal of GROW

is to promote the improvement of old technologies and to innovate and create new technologies to continue to be a pioneer in the field GROW developed specific objectives to increases their chances to achieve success:

¢ Improve the technology:

- AI algorithms are used to analyze the resulting data, from both candidates and evaluators, in order to develop and customize HR-related services for many clients and users

- With more client data on how GROW’s recommended hires were performing, and more applicant data (IAT and peer competency ratings) from an increasing number

of users, GROW’s Al was poised to deliver ever more robust hiring recommendations to clients

¢ Decrease discomfort:

- Exchanging honest feedback was deemed uncomfortable to many people This may affect accurate results Therefore, GROW developed an application that evaluates through tasks or asks questions in a more subtle way so that users feel comfortable giving feedback

- GROW helps holding weekly brainstorming sessions with former colleagues, friends, and researchers to flesh out a tool

¢ Develop and customize Hk-related services:

- Human “intuition” is considerably replaced with “big data” — disparate data points across many people — to develop a scientific, objective, and constantly improving the engine to recruit, screen, and develop human capital

- Inthe recruitment, GROW helped companies enhance their talent pool by finding hidden promising candidates, reduce time-consuming activities like manually screening resumes to save time and labor cost in human resources processes For example, in a test of GROW, one client had both its HR professionals and GROW evaluates the same 200 students GROW not only surfaced nearly the same top 50

candidates (the two lists were statistically indifferentiable), but more importantly,

it did so with specific data-based and competency - based justifications

- GROW helps individuals to evaluate their own strengths and weaknesses, then apply for the jobs that match their competencies Users have biases about the competencies they should be targeting for their ideal job position

- GROW’s AI had the potential to improve both the hiring process and the hiring criteria Fukuhara also cautiously saw potential to expand GROW’s capabilities

As IGS collected data not only on current recruits but on the performance of previously recommended hires

¢ Build the image of the company in the market:

- Being the pioneer to offer these services, up to date, there are seemlingly no competitor in the market

- Reliability is built to users including increasing client acquisition, expanding the user base, decreasing discomfort as well as increasing honest feedback (preventing

a neutral response) in order to get the most reliable testing results At the same time, a wide array of data is collected to develop GROW

- The use of GROW should be strategically focused where it was likely to have the most meaningful (and least potentially misleading) impact

2, Key issues

¢ Extension or contraction?

The transition from traditional face-to-face interviews to GROW's AI implements the requirement of critical elements and skills to ensure accurate, complete, and effective information The widespread interest in GROW tends to provide Fukuhara with a GROWing base of users, data, and institutional support On the flipped side, there required a wide array

of roles when using this type of machine, so companies with limited human resources can be

in an inactive situation when approaching GROW and finally tend to be strongly excluded from it Hence, strategically focusing on the use where it was likely to have the most meaningful impact seems to be quite essential as it might keep GROW in the marketplace in the long run

e the replacement of GROW with humans

To screen, and develop human capital, GROW utilized scientific, objective, and continuously improving “big data’ as an alternative to human “intuition” In addition to current hires, IGS also collects data on the performance of previously proposed hires This provides a strong basis for improving both recruitment processes as well as hiring criteria Consequently, apart from many tremendous advances from that supervised learning machine, there comes a question of whether AI is intelligent enough to completely replace humans in analyzing all data, then retaining the mindset of the human resource department Following this effect, a sense of worrying and suspicion from clients can be triggered when AI is thought to be smarter to make a decision

II/ Evaluate options

1 Criterions

© Competencies: To power GROW’s AI engine, IGS first developed a list of competencies and associated queries that met several specific criteria Given the many

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ways a competency could be defined, each competency was initially approached through six or seven questions However, to increase the chances that evaluators would complete the assessment, IGS then used principal component analysis to reduce the number of queries down to the most relevant and least redundant three

Weighting: Users received ratings from multiple evaluators (four to five, on average,

in addition to the self-evaluation), but each evaluation was not weighted equally Instead, GROW employed a patent- pending,probabilistic (Bayesian) machine learning (AI) algorithm that used data on how that competency had been evaluated historically (called “priors”), along with many data points about an evaluator to determine the posterior likelihood that such an evaluation was genuine and calibrated

to reality The algorithm also took into account an evaluator’s IAT results, as certain personality tendencies (such as conscientiousness) often led to more reliable evaluations Another key data point was the social graph of the evaluator: an evaluator that gave evaluations for multiple users in various social networks was typically more reliable than an evaluator who only joined the app to give evaluations to a particular

user

Personality trait The Implicit-Association Test (IAT) was a well-known test in social psychology to reveal attributes and biases people have, and the test had been connected to personality traits In GROW, users progressed through a series of implicit association tasks in which they dragged certain attributes, appearing at the bottom of the screen, to the box containing the correct attribute (for example, talkative

to extroverted) The box containing the correct attribute was either juxtaposed or matched with a concept (myself, others), and the manner in which the user swiped the moveable attribute was assessed to predict the personality traits of the user GROW’s patented mobile IAT used unsupervised machine learning to reveal anomalies in patterns of swiping behavior and thus better predict personality

Options:

With the development of technology, utilizing the internet into every aspect, GROW can be used to adopt unique approaches to recruit and cultivate the best entrepreneurial talent who could then effectively GROW subsidiaries and therefore

revenues

= Attracting candidates who can not affort time and money to do interview at the work place With the need to visit the company personally excluded, the range of candidate expand tremendously

Clusters of students with high potential are more accurately targeted to advance through the recruitment process

= Reducing needles misses in the haystack—students with the potential to be future leaders but who were screened out too early in the recruiting process

The company is able to assist sourcing new highly capable managerial talent candidates in the applicant pool

= Discovering missed talented applicants

III/ Recommendations

1 Market analysis

1.1 SWOT

STRENGTH

- Strong relationship with existing

companies Being an incumbent in the

industry, GROW has strong relationships

with its suppliers and other members of

the supply chain

- Diverse data source from each

corporation The students and employers

data can be gathered from others since

others are allowed to gift each others

feedback on their personality traits

WEAKNESS

- Lack of workplace diversity GROW may provide chances to hire ideal students for the job, but since they can only target specific personality traits, companies can only create specific working

environments, reduce the diversity of the workplace

- May lose some entrepreneurial talent GROW provides informations and social graph of a person / students based on the gifted reviews of others, but some talented person maybe overlooked by false

reviews

- Informations manipulated As mentioned above, information given by others can be false, and can be manipulated by using others' reviews to boost or reduce one's worth

OPPORTUNITY

- Growing the market size and evolving

preferences of consumers GROW fits in

the GROWing market and can provide up

to date information, which suits the

consumers needs and preferences in the

evolving market

- Opportunities in international markets

Globalization has led to opportunities in

the international market GROW is in the

prime positions since international markets

need more and more labor forces THREAT

- Al may reduce some individuals in HR department Since AI can do most of the data analyzing at an amazing precision, and in most cases it can even better than normal human beings

- Government regulation and bureaucracy GROW should keep a close eye on the fast changing government regulations under the GROWing pressure from protest groups and non government organizations especially regarding environmental and labor safety aspects

1.2 Porter’s Five Forces

GROW Using Artificial Intelligence to Screen Human Intelligence Porter five forces reflect the competitive environment of an industry It is a strategic tool that is used to avoid or minimize the risk of losing the competitive edge that the organization has and to ensure the profitability of the products in the long run The company holds its vision closely as it allows them to orientate its innovation in terms of choices regarding the investment and strategies Within the industry, business profitability is dependent upon the following forces:

Threats of new entrants

Threats of new entrants in the Japanese Publishing Market are moderate The prospective growth in the market tends to bring in new entrants to the publishing market However, the presence of extreme competitors and the requirement of big capital tends to demotivate new entrants to enter in the market

Threats of substitute

Risk of Substitution is high for the JapanesePublishing Market The replacement items for the released documents are the files presented in the digital libraries on certain websites The changing consumer preferences towards digital knowledge increase the risk of substitution for the industry, including Arya, and Hiretual

Bargaining power of suppliers

Bargaining power of suppliers will be high for GROW if:

+ Suppliers have concentrated into a specific region, and their concentration is higher than their buyers

+ This force is particularly strong when the cost to switch from one supplier to other is high for buyers (for example, due to contractual relationships)

+ Suppliers’ forward integration weakens the GROW position as they also become the competitors in that area

+ If GROW is not well educated, does not have adequate market knowledge and lacks the price sensitivity, it automatically strengthens the suppliers' position against the organisation

+ Other factors that increase the suppliers’ bargaining power include high product differentiation offered by suppliers, a small proportion of suppliers’ overall sales and the unavailability of the substitute products

Bargaining power of suppliers will be low for GROW if:

+ Suppliers are not concentrated

+ Switching costs are low

+ Product lacks differentiation

+ Substitute products are available

+ GROW is highly price sensitive and has adequate market knowledge

+ There is no threat of forward integration by suppliers

Bargaining power of purchasers

Business to Business (B2B)

+ Septeni Holdings

“+ The need for students to be sufficiently aware of and interested in Septeni as

a potential employer (invest the time and money to join the group interviews, which they did not always do since Septeni was just a mid-sized company)

“+ The need for students to perform exceptionally well in the one-shot group interviews, since that was the only chance Septeni had to assess them before making a hiring decision

“* Septeni created a new online hiring process but without the data it had traditionally collected through the in-person group interviews

> Solution of IGS:

Septeni turned to GROW as a substitute for that data By collecting evaluations from peers throughout a student’s time at university, GROW fit well with Septeni’s internal evaluation criteria, which was focused on personality traits and on capabilities that an individual demonstrated as she or he worked with others over time Early results with GROW during the 2017 recruiting season were promising Not only did GROW’s predictions closely match the outcomes of Septeni’s internal evaluations, but it doubled the talent Septeni sourced from outside Tokyo, made potentially obsolete the group interview process, and increased Septeni’s name recognition among students (since GROW’s other clients included well- known firms in Japan) The final outcome: a 90% reduction of the overall processing effort while Septeni’s year-over- year acceptance rate of its job offers jumped four-fold, all with no apparent impact on candidate quality

+ All Nippon Airways

“+ A limited number of HR staff, however, ANA feared it would miss needles

in the haystack — students with the potential to be future leaders but who were screened out too early in the recruiting process

> Solution of IGS

Students interested in ANA then used the GROW app to have their competencies and personality traits assessed, which was used to create a “total score.” Based on all of the other data IGS collected about a student and her evaluators, GROW’s AI engine also produced a “confidence score” to rate the degree of confidence IGS had in that total score Moreover, the data revealed several insights: 1) ANA could use GROW to screen out candidates who were unlikely to make it to a final interview, as candidates with a less than -4 confidence score and less than -5 total score were extremely unlikely to become a

“green dot”; 2) The clustering of green dots in the upper right of the graph lent confidence

to the use of GROW as a tool for ANA’s HR staff; 3) There were many students who did not make it past the application screening and yet could not be distinguished from final round interviewees using GROW

+ Mitsubishi Corporation

“+ The firm historically had few issues attracting the best and brightest graduates from Japan

“+ The evolution of its business model had increased its need for highly capable managerial talent Interested in increasing its pool of such candidates, MC began discussions with IGS to use GROW

> Solution of IGS

IGS and MC ran the algorithm on candidates already registered in MC’s application database who were also among the GROW users who had completed GROW’s competency and disposition screening IGS created a social graph of the “ideal” candidates, identifying those individuals who were among the five or more peers who had given the “ideal” candidates feedback A lot of candidates applied to MC after the session, but eventually decided to join MC after passing the interviews To MC, GROW’s greatest value was as a tool to help discover talented applicants who had not discovered them

- Business To Consumer (B2C)

+ Each competency was selected from hundreds of competencies and skills reported in social sciences literature based on several criteria: 1) Representation across different countries and cultures; 2) Correlation to success in relevant job functions (as verified by executive search firms); and 3) Assessable by peers + Implicit - Association Test (LAT), GROW’s Personality Assessment

¢ Competitor analysis

IGS runs in an extremely competitive industry with the presence of large number of competitors The company has a competitive position in the market with the greatest market share in the Japan publishing market Significant competitors of Grow Using Artificial Intelligence To Screen Human Intelligence include:

- Arya

Arya is a revolutionary AI recruiting platform that delivers better hires, faster The company has developed an easy-to-use tool that leverages artificial intelligence to find top talent and comnects you to employees With Arya, recruiters can reduce their time spent on sourcing and sifting through resumes, enabling them to focus more of their efforts on interviewing and hiring the best talent available

The company provides tools that empower recruiters to find the right candidate quickly without spending hours upon hours searching themselves If you want recruitment done right the first time, then Arya is definitely one tool to consider

> Key Feature

+ Quickly identify the best talent (faster than your competitors) so that you can hire the best people fast

+ Avoid spending time searching through resumes by letting Arya do all your job for you + Provide tools to identify top talent quickly and easily so that you can focus more of your efforts on interviewing

+ Find people who have the skills you need and are similar to your current candidates with this hiring software

+ Build high-quality candidate pipelines that are ready to move and automatically engaged with personalized communications so that you can make better, faster, and more accurate hiring decisions

- Hiretual

Hiretual is a job search engine that connects recruiters with the best talent in their industries The company has helped more than 5,000 leading recruiting teams hire faster and smarter by sourcing qualified candidates from a diverse network of over 50 million resumes and

profiles

Hiretual’s proprietary matching algorithm matches resumes to jobs based on skill sets, experience, location, education history, and other factors important to employers

> Key Features

+ Their contact-finding success rate is about 85 percent, which means you'll get the right personal emails, business emails, phone numbers, and social media connections

+ Hiretual does not believe in cluttered data which means that they maintain the integrity of your candidate data across all the systems you use so that you have access to the latest information

+ Hiretual includes everything you need for the right candidate experience — no extra fees or surprises included

+ Your data will be kept safe Hiretual is certified by the CCPA, GDPR, and SOC 2 Type 1 and Type 2 standards, as well as the EU-US Privacy Shield program All this means Hiretual 1S committed to protecting data and ensuring customer trust

2, Financial analysis

Cash and cash equivalent

¢ The Company considers all liquid investments with a maturity of three months or less from the date of purchase readily convertible into cash to be cash equivalents

® Fukuharu embarked on meetings with venture capitalists (VCs) In the summer of

2017, earned a $6 million Series A round with funding from the University of Tokyo Edge Capital, the University of Tokyo Science Investment Management, and Keio Innovation Initiative These investments make IGS the first and only joint venture in Japan to be exclusively funded by university VCs

= Depreciation expenses for the three and nine months ended September 30, 2021, and

2020 were $617 and $0, respectively

e The Company financed its GROWth by utilizing cash reserves, loans from related parties, and issuance of common shares Loans from related parties were unsecured,