In today's competitive hiring landscape, the integration of Generative Artificial Intelligence (GenAI) has become a hot topic for talent acquisition and recruitment. But, understanding the nuances of GenAI tools, and particularly Large Language Models (LLMs), is crucial to leveraging those technologies responsibly and effectively.
Here are six things recruiters should consider when evaluating GenAI tools:
1. Data Privacy and Security
It’s important to determine what data an LLM is using, and what happens with that data after you share it. Since data you share with an LLM through prompts or inputs can be used to further train the model, you could risk a data leak by uploading sensitive information to the wrong tool (like Samsung did in 2023).
Plus, if an external tool is trained on your company’s proprietary data, there’s a chance it could be offered to your competitors after that training has occurred. That’s why it’s key to determine if and how your data is protected when using GenAI tools.
2. Potential for Recruitment Bias
Avoiding bias is essential in recruiting—especially when introducing AI to the hiring process. It’s no secret that human decision-making can be shaped by unconscious bias. The same goes for AI. If it’s trained on biased data, it can mirror those biases in its outputs.
Some GenAI tools and LLMs may help combat this issue with reinforcement learning from human feedback, to help LLMs learn to recognize biased responses and correct potential issues. But it can still be hard to predict the exact questions and responses that might lead to biased output.
For that reason, it can be helpful to learn more about how a GenAI tool’s models were trained, and whether that training data reflects stereotypes or other inequalities. Recruiters can also take additional steps to combat bias by ensuring that AI tools and processes are used in tandem with human interaction and initiatives like anti-bias training.
3. Data Quality & Training
Good data is at the core of good GenAI functionality. GenAI tools that are trained on large, specialized data sets will be able to perform more precise analysis. So if you’re looking for a GenAI tool to help with specific recruiting tasks, you’d get better results from an LLM trained specifically on recruiting data.
For example, Daxtra’s AI solutions are trained on a large volume of specialized and anonymized resume data. In comparison, an LLM trained on an unspecified data set, or a smaller set of data, would have less relevant information to inform its outputs. Ensuring that a GenAI tool is trained on relevant data for your organization is critical because training an LLM yourself could cost millions of dollars for specific use cases.
4. Where/How Automation is Leveraged
Automation is a valuable tool, but it’s not the same thing as AI. When considering GenAI solutions that are marketed as fully automated, it can be helpful to understand the underlying processes. Automation and AI are often paired together when AI-powered tools go to market, but you should examine which components of a solution are automated, how those elements interact with AI, and how the solution fits into your workflows and processes to ensure that the solution will actually provide ROI.5. Speed to Output
Processing text with GenAI can be time-consuming. For each query, an LLM model computes several layers of statistical inference networks, which can amount to billions of numerical computations. Depending on the size of the query, this could take anywhere from seconds to several minutes.
When adding a GenAI or LLM-powered tool to your workflows, evaluate whether the model’s configuration will speed up your workflow or hinder it with slow response times.
6. Organizational Guardrails
GenAI can have a propensity to “hallucinate” outputs that are factually inaccurate. For that reason, it’s important to integrate human oversight and review processes into any GenAI-assisted workflows.
Setting up organizational guidelines for when and how employees can use generative AI will help your organization protect its data and comply with any relevant legislation or regulations around the use of AI tools. Hiring teams should also consider the issues of explainability and legal compliance. In many places, the use of AI in hiring decisions is regulated, and it’s critical for organizations to take care in the design and deployment of AI systems to stay within what’s allowed by law.
Using GenAI Efficiently in Recruiting
How a GenAI tool works, along with how quickly it works, what it does with your data, and how it will serve your business, are all questions to consider when evaluating AI-powered solutions. Something like a standalone LLM is likely to be less effective and more difficult to implement than an integrated GenAI tool, because LLMs on their own don’t provide much structure for recruiting operations. However, when vendors integrate LLMs into their tools so they are part of a larger functional framework, this can provide additional security, oversight, computing power and speed, which can make LLMs more efficient and more useful to businesses as a whole.
Want to learn more about how recruiters can leverage GenAI? Check out our Recruiter’s Guide to LLMs and Generative AI.
For more information on how Daxtra’s AI-powered solutions can improve recruiting workflows, check out our suite of solutions, or get in touch with our team to learn about your specific use case.
