Generative AI in Online Employment Markets: Positive Impact
In this blog, Dr Tao Zhang, a Data Scientist working on Responsible AI in Artificial Intelligence & Platform Services (AIPS) at SEEK, Melbourne, describes the beneficial effects of generative AI on job seekers and employers in online employment markets.
Introduction
Generative AI (GenAI) has the great potential to revolutionize plenty of markets, and recruitment will be undoubtedly one of them. As Australia’s leading online employment marketplace, SEEK is excited to explore how GenAI can revolutionize user experiences and improve the recruitment process. In the preceding post, we highlighted the queries and discussions that were raised in the workshop “Generative AI in Online Employment Markets” during the ChatLLM23 symposium at the University of Sydney. In this post, our attention will be centred on the positive impact that GenAI could bring.
Positive impact
GenAI refers to the application of AI techniques to generate or create content, such as text, images and videos. One of the most successful applications of GenAI is large language models (LLMs), which have the ability to comprehend, interpret, and generate texts. While GenAI has broader applications like image and voice generation, for the purposes of this discussion, we will mostly focus on LLMs due to their relevance to employment-related text content. GenAI offers a suite of advantages that can dramatically transform businesses in the online employment markets. Broadly speaking, the positive impact includes boosting operational efficiency and improving user experience.
In terms of boosting operational efficiency, GenAI can streamline business processes, increasing overall efficiency. It can help with repetitive tasks and semi-automate a wide range of tasks in recruitment. In terms of improving user experience: GenAI can significantly enhance the experience of both job seekers and employers. By understanding the behaviours and preferences of job seekers and employers, GenAI can deliver highly personalized experiences, contributing to increased user satisfaction and engagement.
Utilizing generative AI also requires careful planning, execution, and certain assumptions. Here we list a few requirements:
- Data Availability: GenAI relies on substantial data for training. Thus, to effectively use GenAI in recruitment, it’s necessary to have a comprehensive set of recruitment-related data.
- Quality of GenAI: GenAI is a complex technology that may not always be perfectly achieved. It's required that GenAI can generate high-quality content based on the data it's trained on.
- Legal and Ethical Compliance: The use of GenAI in recruitment should align with the prevailing laws and ethical standards. For instance, GenAI models must be carefully designed to avoid any form of discrimination or bias.
- User Acceptance: Both employers and job seekers need to be comfortable using GenAI-based systems.
- Cost: The expense associated with training GenAI is huge. For its widespread application, the costs involved in both training and utilizing GenAI need to be reasonably priced for the majority.
In the following, we will discuss a subset of the potential positive impacts that the workshop participants believed to be the most impactful from the perspective of job seekers and hirers.
Job Seeker Opportunities
Figure 1 provides an overview of the opportunities available to job seekers in the employment markets. This is only a subset of the potential positive impacts that the workshop participants believed to be the most impactful. In the following, we will dive into more specifics in the following sections.
Resume/cover letter writing and optimization: We can use the generation ability of LLMs to write high-quality resumes. For example, job seekers are tired of writing resumes when applying for different jobs. Job seekers often find the process of crafting resumes for multiple job applications time-consuming and repetitive. LLMs can relieve this burden by generating personalized resumes tailored to specific job requirements (i.e., according to the job’s requirements it is possible to in realtime draft a job seeker’s resume able to highlight the job seeker’s qualifications and attributes more relevant to each specific job), allowing job seekers to focus on other aspects of the job search.
Also, job seekers can use LLMs to polish resumes. Sometimes, job seekers may create a resume that is of subpar quality and too vague. For example, international job seekers who may face additional challenges in crafting resumes that align with local conventions and expectations can use LLMs to polish their resumes. LLMs can help optimise their resumes, ensuring that they effectively communicate their skills and experiences in a manner that receives the attention of potential employers.
Interview Preparation: Interview preparation is a critical aspect of the job-seeking process, and GenAI can play a valuable role in this regard. With their natural language processing capabilities, LLMs can generate sample interview questions and responses in terms of the job seeker’s background and specific job requirements.
LLMs have the ability to analyze a job seeker’s qualifications, skills, and experiences, along with the job description and requirements. Using this information, they can generate a set of interview questions that are relevant to the job seeker’s profile and the specific position they are applying for. These questions can cover various aspects such as technical knowledge, problem-solving abilities, situational judgement, and behavioural questions.
In addition, LLMs can customize general interview questions based on specific job requirements, rather than just generating general interview questions. For example, if a position requires professional knowledge of a specific programming language, LLM can generate technical questions related to that language. This personalized approach enhances the relevance and practicality of the generated questions and answers, enabling job seekers to better prepare for interviews and increase their chances of success.
Career development: LLMs can process and analyze job seekers’ information to gain a comprehensive understanding of their qualifications, interests, and aspirations, which can help identify potential career paths. LLMs can generate personalized insights and suggestions based on job seekers’ personal information, career aspirations, and skill gaps. These suggestions can include potential job roles, industries, or professional areas that align with the job seeker’s interests and long-term career goals. LLMs can generate a visual representation of a job seeker’s career roadmap, outlining the proposed progress, milestones, and necessary steps to achieve their ideal career trajectory. This roadmap can serve as a valuable guide for job seekers, providing clear direction and actionable steps.
Hiring Opportunities
On the hiring side, GenAI can streamline the recruitment process, which can be used broadly before interviews, during interviews and after interviews. The summary of hirers’ opportunities in the employment markets is given in Figure 2, and more details will be presented in the following.
Before Interviews
Job ads optimization: LLMs can assist in optimizing job descriptions to attract the most suitable job seekers. By analyzing text patterns and industry requirements, LLM can propose improvement suggestions for job postings, ensuring that they effectively convey the requirements and expectations of the role while attracting a target job seeker pool.
Finding suitable job seekers: LLMs can analyze job descriptions, resumes, and profiles to identify the best possible match between job seekers and the available positions. By understanding the context used in job descriptions and profiles, LLMs can generate recommended job seekers that align closely with the specific job requirements, skills, and qualifications needed for a particular role.
In addition, LLMs could enhance search capabilities in the context of hiring. LLMs can improve the performance of searches by understanding the context and semantics behind search questions. This allows for more precise matching of job titles, skills, qualifications, and other relevant criteria. Further, LLMs could enable users to enter search queries in natural language instead of predefined keywords. This allows for more flexible and intuitive searches, mimicking human-like interactions with the search system. For example, a user can ask a question like, “Which job seekers have experience with laws and machine learning?”
During Interviews
Interview questions generation: Recruiters can use LLM to generate interview questions based on job ads and resumes, which can simplify the interview preparation process, ensure that the questions are directly aligned with job requirements, and tailor them for evaluating the qualifications of job seekers. This method can save time for recruiters, help them conduct more targeted and persuasive interviews, and increase the chances of finding the most suitable job seeker for the position.
Video-interview enhancement: In the realm of interviews, a growing trend is to develop video-based interview methods that analyze various aspects such as gestures, tones, speech content, facial expressions, and more to evaluate job seekers (sounds scary!). However, to further enhance this process, GenAI can play a crucial role, such as voice generation, video generation and text generation, etc. GenAI has the potential to improve the video interview process by generating more meaningful conversations with job seekers in a genuine and efficient manner. GenAI can dynamically adapt to job seeker responses and generate natural, relevant follow-up questions and prompts. This enhances the interactive nature of the interview, making it feel more like a real conversation rather than a one-sided evaluation.
After Interviews
Feedback generation: GenAI can help provide constructive feedback to job seekers who complete interviews. Interviews without feedback can sometimes disappoint job seekers. GenAI can generate feedback highlighting their strengths and areas for improvement based on the evaluation of their performance and response. This personalized feedback can help job seekers develop their careers and demonstrate the organization’s commitment to supporting their growth.
Job seeker selection: GenAI can be utilized to assist in selecting job seekers after interviews based on their performance and qualifications. It is essential to establish the criteria that are important for assessing the performance of job seekers, such as technical skills, communication abilities, problem-solving capabilities, and cultural fit. Train the GenAI to learn patterns and relationships between the interview performance data and the desired evaluation criteria. While GenAI provides valuable insights, it is important to combine its assessments with human validation and decision-making.
Summary
The use of GenAI provides enormous potential for job seekers and employers to strengthen the recruitment process, thereby improving efficiency, effectiveness, and ultimately successfully matching positions. Leveraging the power of GenAI allows companies to fill vacancies faster and job seekers to find jobs more quickly, reducing unemployment duration and potentially boosting overall productivity in society. Moreover, GenAI has the potential to help minimize human bias in recruitment if it is properly designed and implemented. This could make the labour market more equitable by giving every job seeker a fair chance based on their skills and qualifications. However, opportunities never come alone, GenAI also brings risks and ethical concerns that we will discuss in the next post!
Acknowledgement
We express our sincere gratitude to the University of Sydney to host the ChatLLM23 symposium. We sincerely thank each participant for their valuable contributions, insightful ideas, and in-depth discussions in the Workshop “Generative AI in Online Employment Markets” hosted by SEEK.
