AI recruitment has evolved from specialized algorithms to complex neural networks, offering a promising solution to reduce bias in hiring. As HR Tech Editor Anh Nguyen discusses in her recent article, when accompanied by effective AI policies and ethical practices, AI can address unconscious human bias and assess the entire pipeline of candidates, which is often impossible for human-only hiring teams due to time constraints.
Enhancing the Talent Experience with AI Recruitment Technologies
How AI is Used in Recruitment
AI can and is supporting the talent experience in multiple ways. These include powerful recruitment automation, personalized candidate experience, and DEI-supported recruitment initiatives.
AI for Recruitment Automation
Recruiters spend an average of 15 hours per week sourcing candidates for a single role. AI can significantly reduce this time by automating the initial screening process, allowing recruiters to quickly sift through thousands of resumes using technologies like AI applicant tracking systems (ATS).
This recruiting process automation speeds up the time-to-hire and ensures that the best candidates are shortlisted for the role.
For instance, the multinational consumer goods company Unilever uses AI-driven video interview software to streamline its recruitment process. The company uses AI to schedule interviews and analyze interviewees’ responses based on factors such as word choice, body language, and facial expressions to determine potential candidates. This has reportedly reduced the hiring process from four months to four weeks, demonstrating the significant efficiency gains possible with AI.
AI for Personalized Candidate Experience
Besides cutting down on the time-consuming and repetitive tasks human resources professionals need to do, AI also enhances the quality of the recruitment process by prioritizing candidate experience.
For instance, recruiting chatbots use conversational AI and interview analytics to pre-screen quality candidates for an open role in real time. This ensures a consistent interview experience for all job candidates, regardless of location or time zone.
AI for Diversity Hiring Practices
Another significant benefit of AI is its ability to expand talent pools. Traditional recruitment methods often limit the search for candidate profiles within specific geographical areas or industries. However, AI-powered recruitment solutions such as recruiting CRMs and candidate sourcing tools can reroute top talent (including passive candidates) from different locations or industries to where they are needed most.
This workflow is particularly beneficial in today's competitive marketplace where diversity hiring has increasingly become crucial.
How AI Benefits Recruiting
AI's evolution has been marked by its increasing ability to simulate human processes. Initially, AI was designed to perform specific tasks using specialized algorithms. However, with the advent of neural networks, AI has become capable of machine learning to learn from humans, mimicking their behavior, and even making predictions based on patterns in data.
Benefits of using AI in hiring include helping companies, especially those with high-volume hiring, efficiently present their employer brand in their job ads, distribute their openings across job boards, gather key recruitment metrics, and understand candidates' skills by analyzing their resumes, LinkedIn and other social media profiles, and other digital footprints.
Machine learning can assess a candidate's suitability for a role based on their skill set, experience, and qualifications, eliminating the unconscious bias that often creeps into human decision-making. Many of these approaches have already been incorporated into recruitment automation software.
AI Recruiting Top Challenges
The increasing use of AI in recruitment has sparked a debate on its potential drawbacks. While AI can streamline the hiring process, it also presents challenges such as the need for human involvement, potential bias in AI algorithms, and data privacy and security concerns.
Bias in AI Recruiting
Any human-managed process is vulnerable to influence from unconscious bias. AI's ability to analyze language, words, images, video, and audio may also be crucial in inclusive hiring processes.
For instance, the value of AI in recruitment is emphasized as the tech can analyze job descriptions to identify gender-biased language that might deter specific job seekers from applying. Some video interview software can even review recordings of job applicants to assess body language, tone of voice, and other non-verbal cues, providing a more holistic view of the candidate.
However, as Polli points out, AI's potential to eliminate hiring bias is not without challenges. The primary source of bias in AI is the biased data set used to train the algorithm. If the data used to train the AI is biased, the AI will inevitably perpetuate these biases. Therefore, using diverse and representative data sets to train AI algorithms is crucial.
Moreover, AI should be designed to meet certain beneficial specifications. AI practitioners are already developing principles to make AI ethical and fair.
One fundamental principle is that AI should be auditable, and any bias should be removable. This is akin to the safety testing of a new car before it is allowed into production. With NYC already leading the way with AI regulations, we should expect more stringent controls and data requisitions in the coming years.
AI recruitment platforms claim to reduce bias typical of human recruiters by incorporating machine-based decisions. However, these talent management tools are often trained on historical data, which can perpetuate existing biases. For instance, if the tech industry's historical employee data is predominantly male and white, AI systems built on this data will likely carry the same biases.
Jelena Kovačević, IEEE Fellow and Dean of the NYU Tandon School of Engineering, explains that if the data set lacks diversity, the algorithm built into any AI recruiting solution that trains on it will be biased towards what the data set represents, comparing all future candidates to that archetype.
Bias in AI is a significant concern, especially in industries like IT, which have had historical issues with diversity. Ben Winters, an AI and human rights fellow at the Electronic Privacy Information Center, notes that many systems have shown biased effects based on race and disability. This bias can lead to the exclusion of underrepresented groups, further exacerbating the diversity problem and rejecting people who may have been the right candidates for the job.
Data Privacy Challenges in AI Recruitment
Data privacy and security are also significant concerns. AI can collect vast candidate data, including video interviews, assessments, resumes, and social media profiles. However, candidates may need to be made aware that AI tools are analyzing them, and there are few regulations on how this data is managed (e.g., the recent Zoom T&C changes).
Matthew Scherer, senior policy counsel for worker privacy at the Center for Democracy & Technology, warns that companies' eagerness to cut costs can cause them to overlook potential negatives of the software they're using for screening candidates.
Expect to see more examples of AI streamlining the hiring process and identifying qualified candidates alongside its challenges. Companies must be strategic and thoughtful about implementing AI in recruitment, ensuring their hiring process and tools do not exclude traditionally underrepresented groups. They must also prioritize data privacy and security and maintain human involvement in hiring to counterbalance potential biases in AI algorithms.
4 Steps of Implementing AI in Recruiting
AI for recruiting is a rapidly evolving field with the potential to revolutionize how talent acquisition teams source, screen, and hire talent. However, to leverage AI effectively, recruiters must follow best practices, conduct thorough research, and ensure compliance with legal and ethical standards.
Step 1. Get Input - Don't Go It Alone
A crucial first step is to involve key stakeholders, including the recruitment team, hiring managers, HR professionals, and candidates.
By ensuring everyone is on board and understands the benefits and limitations of AI, recruiters can avoid misunderstandings and resistance down the line.
Step 2. Understand AI's Strengths and Bolster its Shortfalls
Recruiters must understand the AI functionality available and how they can be applied to their needs. This includes understanding the capabilities of generative AI in candidate sourcing, resume and social media screening, and personalized outreach tasks.
However, it's important to remember that AI is a tool, not a replacement for human interaction. Candidates are humans who make decisions based on more than just data, and no amount of automation can replace the impact of talking to a real person.
Step 3. Follow a Clearly-Defined Process to Implement AI Tools
To implement AI in recruitment, recruiters can follow a step-by-step process:
- Identify the talent acquisition tasks that could be automated, such as candidate sourcing or resume screening.
- Research and select the best AI recruiting tools.
- Involve key stakeholders and ensure they understand AI technology's benefits and limitations. Implement the tools, monitor their performance, and make adjustments as necessary.
- Ensure compliance with legal and ethical standards, addressing any issues around bias or data privacy.
Step 4: Level up Generative AI skills with Basic Prompt Engineering
Many videos offer the ideal prompt to master your profession. Still, a small investment in learning how generative AI models like ChatGPT work will help you stand out from peers stuck with prompt guides.
Rather than expecting a model to generate an answer to any question immediately, picture yourself asking a fresh college grad to complete the same task. To get optimal results from your AI tool, write unambiguous instructions and give the model-specific steps to follow to work out an answer.
This approach will ensure a higher-quality output and reduce the likelihood of fictional statements, known as AI hallucinations. For further insights, check out our video on how HR teams are using AI to boost their productivity in hiring and daily HR tasks.
AI is Changing the Game for Recruiting: Conclusion
Overall, the future of AI in recruitment looks positive. One of the key trends in AI recruitment automation is the shift toward skills ontology and job architecture. AI can screen resumes and rank candidates by categorizing them into a ranking system for recruiters. This allows for a more efficient and accurate assessment of candidates' skills and qualifications, reducing the time spent on screening and enabling faster, more precise hiring decisions.
AI has the potential to support diversity and inclusion initiatives by providing employees with more choices and opportunities for career advancement. By giving employees access to information about career trajectories and the skills needed for different roles, AI can empower employees to take control of their career development and make informed decisions about their future.
Advancements in AI are set to bring about significant changes in recruitment, focusing on skills ontology, job architecture, and DEI hiring strategies. By leveraging these technologies, companies can create a more efficient, inclusive, and personalized recruitment experience, ultimately leading to better hiring outcomes and improved employee retention.
