Using AI for hiring: HR tools and guidance for eliminating AI bias
Unsure about using AI in your hiring process? We provide guidance on using AI-powered HR tools and removing AI bias from the recruitment process.
Jan 26, 2024 • 4 Minute Read
From customer service and sales to business analytics and code generation, the applications of AI are boundless. Another use of AI tools? Recruiting and hiring.
We explain how HR tools with AI automation enhance the hiring process and how to mitigate bias, ensure ethical AI use, and promote inclusive hiring practices.
Table of contents
How AI tools can streamline the recruitment process
30 days. That’s the average time it takes to hire a candidate for an open tech/IT role. The longer the hiring process, the greater the chance skilled candidates will accept positions elsewhere.
Using AI makes the hiring process more efficient and can help your organization secure the most talented candidates in the market before they’re snatched up by the competition.
Resume scanning and authentication
On average, recruiters spend 5 – 7 seconds looking at a resume. If a candidate seems promising, recruiters will spend more time on their resume to determine if they want to proceed with the hiring process. And they need to do that for hundreds of applications. Those seconds turn into days, even weeks.
AI-powered HR tools perform automated resume scanning to review hundreds of resumes in mere minutes. AI tools can screen resumes for certain criteria, quickly identify candidates with skills that match the open role, and shorten the overall recruitment process.
Because these AI-powered resume scanning tools assess resumes on fixed criteria, they help reduce implicit bias and ensure all applications are considered equitably, even those that may not be 100% qualified for the role. (You still need to consider AI bias, though.)
AI recruiting tools can also authenticate resumes. For example, they might assess a candidate’s LinkedIn profile or online portfolio to ensure it aligns with the information they provided in their application.
Predictive analytics for candidate sourcing and evaluation
Predictive analytics assist with candidate sourcing and candidate evaluation. Using data like LinkedIn profiles, tenure, and company turnover rates, predictive analytics can help recruiters find passive candidates who would be willing to leave their current employer for a new opportunity.
Hiring managers can also use predictive analytics to make more informed hiring decisions. AI algorithms can predict how likely candidates are to perform well and stay with the organization long term. As a result, recruiters and hiring managers hone in on candidates with high potential faster.
Chatbots to enhance the candidate experience
Most organizations already use online chatbots for customer service and sales. You can also use them in the talent acquisition process to improve the candidate experience. AI-powered chatbots use natural language processing (NLP) to communicate with candidates and streamline recruitment process tasks like scheduling interviews.
However, if your chatbot doesn’t answer candidate questions or is frustrating to use, it can negatively affect the candidate experience. And chatbots can’t replace human intelligence or genuine interaction.
Policies to ensure ethical AI recruiting without implicit bias
AI relies on historical data to make decisions. If AI models are trained on data that contains explicit or implicit bias, the AI models will also adopt those biases and use them when making decisions.
Without the right data or policies, AI-powered HR tools can inadvertently discriminate against applicants and break equal employment opportunity laws. Local governments have even started creating AI hiring laws that require employers to audit AI tools for bias.
To comply with these laws, and most importantly, recruit and hire equitably, organizations need to define policies and best practices for using AI during the hiring process.
Review and update AI recruitment policies
Before you adopt AI for recruitment, create or update your AI recruitment policy.
AI use: How will AI fit into your current teams and processes? What is acceptable AI use? What isn’t?
Laws and ethics: What laws and regulations do you need to comply with? How can you ensure responsible and ethical AI use? How will you address data privacy?
Employee training: What AI skills and knowledge do teams need to use AI tools successfully and ethically?
Measurement: What metrics will you use to measure the success of your AI implementation?
Evaluate AI recruiting tools for fair and inclusive hiring practices
When you’re ready to adopt AI recruiting tools, consider the following to reduce risk and practice inclusive, equitable hiring:
Ensure AI models are trained on diverse data sets so they can understand different resume formats
Look for AI-powered HR tools that use predictive analytics to assess an applicant’s potential, not just their prior experience
Create privacy and security protocols to protect data
Create feedback loops to refine the algorithm and help it better identify suitable candidates over time
Constantly monitor data and make sure it’s still relevant and unbiased
Tell candidates you plan to use AI tools during the recruitment process and create alternate options for those who are uncomfortable with AI-powered processes
Train HR business leaders and recruiters in AI tools and ethics
To ensure equitable policies and practices stick, business leaders and recruiters need AI skills and knowledge. These courses provide a good foundation:
Partnering ethical AI use with inclusive hiring practices
Using AI in the hiring process streamlines workflows for recruiters, hiring managers, and business leaders at the same time it improves the candidate experience.
Despite these benefits, AI automation can heighten bias and, ultimately, impede workplace diversity. As your organization looks towards AI implementation, remember the importance of empathy and the human connections at the heart of the hiring process.
Explore the complete collection of Pluralsight’s AI courses and learning paths.
