//this is the mailchimp popup form //ShareThis code for sharing images
Home / Blog / AI in Recruiting: Circumventing Bias and Leveraging Automation to Improve Your Hiring Funnel

AI in Recruiting: Circumventing Bias and Leveraging Automation to Improve Your Hiring Funnel

An in-depth look at how AI recruiting tools are shaping the future of talent acquisition.

Christopher Mannion
Systems engineer with 15 years experience in People Management, HR Analytics, and Talent Acquisition
Contributing Experts
No items found.
An HR professional and AI recruitment bot posing for a photo.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Contributing Experts

Table of Contents

Share this article

Subscribe to weekly updates

Join 20,000 HR Tech Nerds who get our weekly insights
Thanks for signing up, we send our newsletter every Wednesday at 10 AM ET!
Oops! Something went wrong while submitting the form.
15 Best Employee Rewards Programs (2024)

In recruitment, artificial intelligence (AI) has evolved from specialized algorithms to complex neural networks, offering a promising solution to eliminate bias in hiring. As Frida Polli discusses in her Harvard Business Review article, "Using AI to Eliminate Bias from Hiring," 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.

In This Article

The Evolution of AI to Streamline Hiring

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.

In hiring, AI can help understand candidates' skills by analyzing their resumes, LinkedIn and other social media profiles, and other digital footprints. It 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 AI recruiting platforms.

Mitigating Bias with AI

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 unbiased talent acquisition processes.

For instance, AI can analyze job descriptions to identify gender-biased language that might deter specific candidates 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.

A graphic representation of AI training on unaudited employee data which contains hiring bias.

Biased Input Leads to Biased Output

The increasing use of artificial intelligence (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.

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 hiring tools claim to reduce bias typical of human recruiters by incorporating machine-based decisions. However, these 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 hiring 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.

AI is Not Flawless

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 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 the eagerness to cut costs can cause companies 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 the use of 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.

A slide from a presentation about using ChatGPT to review candidate data.

4 Steps of Implementing AI in Recruiting

AI for recruiting is a rapidly evolving field with the potential to revolutionize how companies 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

Involving key stakeholders is a crucial first step. This includes the recruitment team, hiring managers, HR leaders, and even 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 tools available and how they can be applied to their needs. This includes understanding the capabilities of generative AI tools like Chat GPT and Bard, which can automate candidate sourcing, resume 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:

  1. Identify the talent acquisition tasks that could be automated, such as candidate sourcing or resume screening.
  2. Research and select the appropriate AI recruiting tools.
  3. Involve key stakeholders and ensure they understand the benefits and limitations of AI technology. Implement the tools, monitor their performance, and make adjustments as necessary.
  4. 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 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.

Not only will this approach ensure you get a higher quality output, but you will also reduce the likelihood of fictional statements, known as AI hallucinations. I cover this in my Chat-GPT Level Ups for Recruiters video and will publish more walkthroughs in the future.

How AI Has Changed Recruiting


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-enhanced Applicant Tracking Systems (ATS).

This automation speeds up the recruitment process (time-to-hire) and ensures that the best candidates are shortlisted for the role.

For instance, Unilever, a multinational consumer goods company, has used AI to streamline its recruitment process. The company uses AI to analyze video interviews, looking at factors such as word choice, body language, and facial expressions to assess candidates. This has reportedly reduced the hiring process from four months to four weeks, demonstrating the significant efficiency gains possible with AI.


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 providing a personalized candidate experience.

For instance, AI-powered physical interviews conducted by bots in real-time use natural language processing (NLP) and interview analytics to assess a candidate's suitability. This ensures a consistent interview experience for all candidates, regardless of location or time zone.

Casting a Wider Net

AI's ability to expand labor pools is another significant benefit. Traditional recruitment methods often limit the talent pool to a specific geographical area or industry. However, AI can reroute talent from different locations or industries to where they are needed most.

This workflow is particularly beneficial in today's competitive marketplace where building and maintaining a diverse talent pool is crucial.

Some Last Thoughts on AI for Recruitment

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 DEI 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 initiatives. 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.

Christopher Mannion
Systems engineer with 15 years experience in People Management, HR Analytics, and Talent Acquisition
LinkedIn logoTwitter logo

Chris Mannion is the co-founder and CEO of Sonar Talent, a recruiting intelligence platform that lets recruiters access untapped talent to accelerate hiring. Chris holds an MBA from the MIT Sloan School of Management and has spent the last 15 years improving systems and teams across the world.

With experience ranging from aerospace engineering in the military, e-commerce supply chain operations, and building out the first talent acquisition COO function at Wayfair, Chris brings unique perspectives to recruiting. He is now drawing on his operational experience to launch a product that brings this capability to all recruiters

Related posts

Join 35,000 HR Tech Nerds who get our weekly insights

More posts
Read HR Tech Reviews