In recent weeks, TLNT has been pointing the spotlight on the emerging issue of resumes being created by artificial intelligence. We’ve debated both sides, looking at whether this poses new risks for employers who may not be able to determine a candidate’s true individuality or the authenticity of people’s accomplishments or skills.
But Mark Lieberwitz, co-founder and CPO of KarmaCheck, thinks employers, education establishments, accreditation bodies, and trade bodies all need to unite to tackle this issue head-on, by creating a universal ‘credit-score’ summary of someone’s achievements that is less likely to be distorted.
How would it work? Could it work? Mark gives his thoughts below in this exclusive article for TLNT:
As all CHROs will no-doubt be fully aware, the advent of Large Language Models (or LLMs) – like ChatGPT – has ushered in both an era of unprecedented potential, but also complex challenges for the staffing industry.
These powerful AI tools can literally transform many aspects of the recruitment process, from resume crafting on the candidate side, to influencing the interview experience itself.
But this is not without its pitfalls.
The misuse of LLMs by candidates to embellish their resumes, misrepresent their backgrounds, and even cheat in live interviews presents a significant problem.
On the flip side, employing LLMs for candidate screening based on job descriptions and resumes could streamline the hiring process, but at the expense of diversity and equity.
As such, I believe navigating these challenges requires the staffing sector to pioneer a new background and credential verification approach, one that’s much more akin to a universally recognized “credit score” for professional backgrounds.
The need for change
The misuse of LLMs by job seekers is a growing concern. Candidates can leverage these tools to enhance their resumes artificially, adding skills and accomplishments that may not accurately reflect their capabilities or experience.
This misrepresentation undermines the integrity of the recruitment process and places employers at risk of hiring under-qualified candidates.
Furthermore, instances where candidates use LLMs to receive live answers during video interviews raise questions about the authenticity of candidates’ abilities and knowledge.
Conversely, the application of LLMs in screening candidates based on resumes and job descriptions offers a glimpse into a potentially more efficient recruitment process.
By analyzing vast amounts of data, LLMs can identify the most suitable candidates for a position, potentially reducing the time and resources spent on manual screening.
However, this approach has its drawbacks. Relying solely on AI for candidate screening can inadvertently perpetuate biases encoded within the algorithms, thereby sacrificing diversity and equity in the hiring process.
Moreover, AIs might need to pay more attention to the nuanced understanding of a candidate’s unique experiences and potential for growth.
To address these challenges, the staffing industry must develop and adopt a standardized background and credential verification system.
This system should function similarly to a credit score, providing a universally recognized and accepted measure of a candidate’s professional background and achievements.
By ensuring that candidates maintain knowledge and ownership of their own backgrounds, this system would empower individuals to present their credentials transparently and accurately.
Implementing such a system would require collaboration across multiple stakeholders, including employers, educational institutions, accreditation bodies, and technology providers.
Key features of the system could include:
- Verification Mechanisms: Utilizing blockchain or similar secure technologies to verify the authenticity of educational qualifications, professional certifications, and work experiences.
- Dynamic Scoring: Allowing for the continuously updating of a candidate’s “score” based on new achievements, skills acquisition, and employer feedback.
- Privacy Controls: Enabling candidates to control who can access their information, ensuring privacy and data protection.
- Bias Mitigation: Incorporating mechanisms to identify and mitigate biases, ensuring that the system promotes diversity and equity.
I believe this approach offers several benefits.
For employers: it provides a reliable and efficient means of verifying candidates’ backgrounds, reducing the risk of hiring based on misrepresented information and speeding up the credentialing or verification process.
For candidates: it offers a way to distinguish themselves in a competitive job market by showcasing verified achievements and skills that may otherwise go unrecognized. Moreover, by promoting transparency and accountability, such a system could restore trust in the recruitment process.
Any implementation of this type of system would not be without its challenges.
It would require widespread adoption and standardization to be effective and ongoing efforts to ensure that the technology remains secure, unbiased, and up-to-date with evolving labor market needs.
But the rise of LLMs like ChatGPT in the staffing world requires some sort of response.
To harness the potential of these technologies while mitigating their negative impacts, the staffing industry must pioneer a new approach to background and credential verification.
By developing a universally recognized and accepted system, akin to a professional “credit score,” the industry can navigate the complexities of modern recruitment, promoting integrity, diversity, and equity in the hiring process along the way.