The philosophy of selection in hiring processes has evolved significantly over time, particularly with the advent of artificial intelligence and machine learning technologies. This evolution has led to an ongoing debate about the merits of human-driven versus machine-based selection methods.

Philosophy of Selection

Employee selection aims to identify and hire individuals who will perform well in a given role and contribute positively to an organization. Traditionally, this process has been guided by several fundamental principles:
• Validity: Ensuring that selection methods accurately predict job performance.
• Reliability: Producing consistent results across different times and contexts.
• Fairness: Avoiding discrimination and providing equal opportunities to all candidates.
• Efficiency: Balancing the costs and benefits of the selection process.

Human vs. Machine-Based Selection

Human-driven selection processes have been the norm for most of history. They typically involve resume screening, interviews, and sometimes additional assessments by hiring managers or HR professionals.
Advantages:
1. Intuition and emotional intelligence: Humans can pick up on subtle cues and assess cultural fit.
2. Flexibility: Ability to adapt to unique situations and consider context.
3. Relationship building: Can establish rapport with candidates, potentially improving candidate experience.
Disadvantages:
1. Bias: Susceptible to cognitive biases (e.g., halo effect, confirmation bias).
2. Inconsistency: Judgments may vary based on factors like mood or fatigue.
3. Limited processing capacity: Difficulty handling large volumes of applications efficiently.

Machine-Based Selection

AI and machine learning algorithms are increasingly being used to automate parts of the selection process, from resume screening to video interview analysis.
Advantages:
1. Efficiency: Can process large volumes of data quickly.
2. Consistency: This applies the same criteria to all candidates.
3. Potential for reduced bias: It can ignore irrelevant demographic factors if adequately designed.
4. Data-driven insights: Can identify patterns and predictors of success that humans might miss.
Disadvantages:
1. “Black box” problem: The decision-making process can be opaque and difficult to explain.
2. Potential for algorithmic bias: If trained on biased historical data, it can perpetuate or amplify existing biases.
3. Lack of human touch: This may miss nuances that human recruiters can detect.
4. Candidate perception: Some applicants may feel dehumanized by automated processes.

Back to our topic:

The Comprehensive Ranking System (CRS) score, while designed to be an objective measure for selecting skilled immigrants, has several flaws that can lead to overlooking highly qualified and experienced candidates. Here are some arguments supporting the stance that the CRS score is not the right tool for talent selection, mainly due to its age-based bias and potential disregard for valuable experience:

1. Age bias disadvantages experienced professionals:

The CRS awards maximum points (110 for single applicants) to candidates aged 20-29, with points decreasing sharply from age 30 onward. This system significantly disadvantages mid-career professionals who often possess the most valuable skills and experience. For instance, a 45-year-old executive with 20 years of industry experience receives zero points for age, potentially being outscored by a recent graduate with minimal work experience.

2. Undervaluing accumulated expertise:

While the CRS awards points for work experience, it caps these points at a relatively low level compared to age. This needs to adequately recognize the depth of expertise and industry knowledge that comes with decades of professional experience. A candidate with 30 years of specialized expertise in a high-demand field may receive fewer points than a younger candidate with just a few years of work history.

3. Misalignment with industry needs:

Many industries, particularly those requiring high expertise or leadership roles, seek candidates with substantial experience. The CRS’s age bias can result in a mismatch between the selected immigrants and the actual needs of Canadian employers, potentially leading to unfilled positions in critical sectors.

4. Overlooking career progression and achievements:

The CRS does not adequately account for career progression, leadership roles, or significant professional achievements. A candidate who has risen to a senior management position or made notable contributions to their field may be undervalued compared to a younger candidate with less impressive career accomplishments.

5. Disregarding the value of diverse life experiences:

Older candidates often bring diverse life experiences, including cross-cultural competencies and global perspectives, which can be invaluable in Canada’s multicultural work environment. The CRS’s age bias needs to recognize these intangible but crucial qualities.

6. Potential for brain drain:

By disadvantaging experienced professionals, Canada risks losing out on top talent to other countries with immigration systems that better value experience. This could lead to a “brain drain” scenario where highly skilled mid-career professionals choose other destinations, depriving Canada of their expertise and potential economic contributions.

7. Inconsistency with retirement trends:

As people work longer and retire later, the CRS’s age bias seems increasingly out of step with modern career trajectories. A system that heavily penalizes candidates over 40 fails to recognize that these individuals may have 20-30 years of productive work life ahead of them.

8. Overlooking industry-specific expertise:

In some fields, such as academia, scientific research, or specialized technical roles, the most valuable contributions often come from professionals later in their careers. The CRS’s age bias could prevent Canada from gaining access to world-class experts in these crucial areas.

9. Potential for indirect discrimination:

The age factor in the CRS could be seen as indirect discrimination, particularly against immigrants from countries where advanced education or international career opportunities may come later in life due to various socio-economic factors.

10. Failure to recognize non-linear career paths:

The CRS’s rigid scoring system may not adequately value candidates who have taken non-traditional career paths, such as those who have pursued further education mid-career or made significant career changes that have enhanced their skills and experiences.

Despite the importance of quality in selection, we can see that the selection criteria often prioritize quantity in its selection processes. Several factors contribute to this tendency:

a) Time pressure: Urgent hiring needs can lead to rushed processes prioritizing speed over thoroughness.
b) Cost considerations: In-depth quality assessments can be expensive, leading companies to opt for cheaper, high-volume approaches.
c) Difficulty in measuring quality: While quantity is easily measurable (e.g., time-to-hire, number of hires), quality is often more subjective and takes longer to assess.
d) Short-term thinking: Pressure to fill positions quickly can overshadow long-term employee performance and retention considerations.
e) Overconfidence in selection methods: Some organizations may believe their current processes need to be revised to identify quality candidates, even when using high-volume approaches.
f) Technological capabilities: AI’s ability to process large numbers of applications quickly can lead to an emphasis on quantity, with the assumption that quality will naturally emerge from a larger pool.
g) Misaligned incentives: Recruiters or hiring managers may be evaluated on metrics prioritizing speed and volume over quality of hire.

Research supports these observations. For example, a study (Nathan R. Kuncel, 2014) found that algorithmic decision-making outperformed human judgment in personnel selection, suggesting that reliance on human intuition may lead to suboptimal hiring decisions. However, as noted by Campion and Campion (Michael A. Campion, 2023), while machine learning shows promise in improving selection processes, its effectiveness compared to traditional methods is still being studied, and concerns about potential biases and lack of transparency in AI systems persist.
By critically examining our selection philosophies and leveraging the strengths of both human insight and technological capabilities, organizations can work towards more effective, fair, and balanced hiring practices that prioritize quality without sacrificing efficiency. It’s important to note that while age is a significant factor, it is not the only criterion in the Express Entry system that directly relies on the Comprehensive Ranking Score. Other factors such as education, work experience, language proficiency, and having a valid job offer also play crucial roles in determining a candidate’s overall CRS score and likelihood of receiving an ITA. In my opinion, and with my little experience as a regulated Canadian Immigration Consultant and having Immigration and Refugee Board representation, the point-based scoring should have an age alternative.

A case study

Continuing with the research and reviewing a 2017 report, I came across data that showed that while a 29-year-old scores 110 CRS points for age, this drops sharply from age 30 onwards. Let us assume Mr. John Hoe and Ms. Jane are two people uploading their profiles for the Express Entry pool of candidates. Mr. John is thrilled; he turned 39 last week and has been promoted to Senior Vice President of Risk Analysis at his company. As a Harvard graduate with a successful career, he feels accomplished, secure, and financially stable. With a strong sense of confidence, he believes he has all the right qualities to soar to new heights. Sitting in front of his laptop, he enters his details and feels optimistic about receiving an invitation to apply after being selected from the pool of Express Entry candidates.
A few hundred kilometres away from Mr. John, Ms. Jane lives in a small town. She graduated from a local university four years ago and has been living alone and working at a bar for over six years. At just 24 years old, she has had relationships in the past, but her financial obligations and personal priorities have prevented her from settling down. Recently, she experienced a breakup and felt heartbroken, so she reached out to a few friends for support. They all suggest that she consider relocating. After some searching, she decided to go to Canada and upload her profile to the Express Entry pool.
This scenario highlights some of the key issues with the Comprehensive Ranking System (CRS) used in Canada’s Express Entry immigration system, particularly regarding the age factor. Let’s analyze the situation for both Mr. John and Ms. Jane:

Mr. John’s situation:

1. Age disadvantage: At 39, Mr. John will receive only 50 CRS points for age (assuming he’s single). This is a significant disadvantage compared to younger applicants.
2. Overlooked experience: Despite his senior position and presumably extensive experience in risk analysis, the CRS does not adequately reward this level of expertise.
3. Education undervalued: While his Harvard degree will earn him points, the system needs to distinguish between the prestige of institutions or the relevance of his education to his current role.
4. Career achievements ignored: His recent promotion and senior position are not directly factored into the CRS score.

Ms. Jane’s situation:

1. Age advantage: At 24, Ms. Jane will receive the maximum 110 CRS points for age (assuming she’s single), giving her a significant edge over Mr. John.
2. Limited work experience: Despite having less relevant or specialized work experience, the age factor alone puts her at an advantage.
3. Potential adaptability: The system assumes younger applicants like Ms. Jane will adapt more easily to life in Canada, despite potentially having less life experience or professional skills.

Analysis:

1. Age bias: The scenario clearly demonstrates how the CRS heavily favours younger applicants, potentially at the expense of more experienced and skilled professionals.

2. Undervaluation of expertise: Mr. John’s senior position and extensive experience in a specialized field are not adequately recognized by the system.

3. Oversimplification of potential: The CRS assumes that younger applicants like Ms. Jane have more potential to contribute to Canada’s economy, without considering the immediate value that experienced professionals like Mr. John could bring.

4. Disregard for career achievements: The system doesn’t account for career progression or leadership roles, which are strong indicators of an individual’s potential economic contribution.

5. Potential mismatch with labour market needs: By favouring younger, less experienced candidates, the system may not align with the immediate needs of Canadian industries requiring seasoned professionals.

6. Life stage considerations: The system doesn’t account for the stability and financial security that older applicants like Mr. John might bring, which could be beneficial for successful integration.

7. Overlooking transferable skills: Mr. John’s experience in risk analysis and leadership could be highly valuable across various sectors, but this is not reflected in the CRS scoring.

This scenario illustrates how the CRS’s heavy emphasis on age can lead to potentially overlooking highly qualified, experienced professionals who could make immediate and significant contributions to Canada’s economy and society. It suggests that a more nuanced approach, one that better balances age with experience, expertise, and career achievements, might be more effective in selecting immigrants who can best contribute to Canada’s economic needs and long-term growth.

1. In 2023, over 54% of Express Entry invitations went to candidates aged 20-34. The system must consider a more balanced approach that still favours youth but allocates more points to candidates in their 30s and early 40s with valuable skills and experience.
2. Implement occupation-specific age criteria to better align with labour market needs in different sectors.
3. Expand programs like the Global Talent Stream to fast-track experienced professionals in high-demand occupations.
4. Conduct further longitudinal studies on the economic outcomes of immigrants across different age groups to refine the points allocation.

While the current system has merits in addressing long-term demographic and economic goals, adjustments could help Canada attract a more diverse range of skilled immigrants to meet immediate and future labour market needs. A nuanced approach considering both age and experience could optimize the selection of candidates most likely to succeed and contribute to Canada’s economy.

Forward this document with complete confidence in our authorized representation and licensure as a responsible and ethical immigration consultancy in Canada.

Works Cited
Michael A. Campion, E. D. (2023, September 29). Machine learning applications to personnel selection: Current illustrations, lessons learned, and future research. Denver, Colarado, USA.
Nathan R. Kuncel, D. S. (2014, May). Kuncel et al. (2014). Minnesota , United States of America.

This blog post has been produced by Mirza Asma Azim, RCIC-IRB for Moving Life Immigration Solutions Inc. – The experts in Canadian Immigration Law.

For Appointment: appointment@movinglifeimmigration.com
Call us at +1(647)366 9736
For information: info@movinglifeimmigration.com
Web: https://movinglifeimmigration.com