Does 🤖 Reinforcement Learning offer strategic value for recruiting process automation?
With the current AI Cycle not slowing down, I am hearing more and more about diverse use cases for AI in recruitment automation and sourcing.
One topic I see often mentioned is that AI systems based on Reinforcement Learning can significantly impact recruiting automation.
What is Reinforcement Learning
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions within an environment by performing actions and receiving feedback and rewards based on those actions.
The agent's objective is to develop a strategy known as a policy that maximizes the total rewards it accumulates over time. This learning process involves continuously adjusting the policy based on the outcomes of actions and the evolving state of the environment.
The concept of a system that learns from real-time feedback to optimize recruiting strategies is undeniably captivating, promising to revolutionize our approach to talent acquisition.
Recruiting Use Cases for Reinforcement Learning
I can think of two immediate applications, and I am sure you are also coming up with them:
Dynamic channel selection:
For instance, by dynamically selecting which platforms to use based on the success rate of past postings, RL can streamline how recruiters connect with qualified candidates.
For example, a job posting on LinkedIn has historically attracted more qualified candidates for this category/title/location than on other platforms; the RL system would prioritize LinkedIn for future postings.
Resume screening:
Imagine a system that evolves to identify the most promising candidates based on outcomes from previous hiring cycles and considering dynamic feedback from the recruiter on proposed resumes.
Imagine the possibilities of a system that can dynamically adapt and improve its candidate sourcing and resume screening strategies! Sounds incredible, right?
However, it's crucial to acknowledge that integrating RL in such critical areas carries significant risks and challenges.
Risks and Challenges of Reinforcement Learning Systems in Recruiting
The feedback (reward) necessary for an RL system often comes much later in the employment cycle, after the hire.
The most valuable information – whether the hire was victorious, can be distributed between multiple fragmented HR Tech systems that are not connected with each other.
Delayed and Sparse Feedback: The feedback necessary for RL systems often comes much later in the employment cycle, potentially leading to slower and less responsive learning.
Fragmented Data Systems: The practical training of RL systems necessitates seamless data integration, often needed by fragmented HR tech ecosystems.
Even if we get proper signals that a candidate was hired, it can take months to determine whether the hire was good or not. Even then, there might be factors beyond the hiring channel or resume that impact the quality of the candidate. For example, a candidate’s performance might suffer due to a health issue not shared with the employer. This information cannot be used for the RL system at all.
Last but not least, we have to talk about bias - the data driving these AI systems can reflect historical biases, inadvertently leading to discriminatory practices unless actively mitigated. Today, no one knows how to define bias clearly, which is the first step in fighting bias.
Join the Discussion
đź’ˇ I encourage other HR professionals and technologists to share their opinions. Your thoughts and feedback are invaluable as we discuss the future implications of AI in HR.
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Alexander Chukovski