How to Build a Fair, Transparent AI Candidate Shortlisting Process

Hiring is evolving, and so are the tools behind it. With thousands of applications pouring in for every role, recruitment teams are increasingly turning to AI-powered shortlisting tools to speed up their hiring decisions. 

But speed alone isn’t enough. AI shortlisting must also be fair, transparent, and accountable. Otherwise, it risks reinforcing existing biases or violating compliance standards. 

In this blog, we’ll walk you through how to build a responsible AI candidate shortlisting process. You’ll learn how the technology works and how to use it the right way—so your hiring is faster, more inclusive, and free from bias.

Let’s dive in.

Benefits of using AI Shortlisting tools for hiring 

1. Faster screening

Manually evaluating resumes and applications takes up the lion’s share of the time and effort, leading to a delayed hiring process. 

Wasting so much time on shortlisting candidates is more dangerous than your team can realize. Delayed responses often frustrate top candidates, increasing the risk of losing them to your competitors. 

With AI tools for resume parsing, application scoring, and ranking, hiring managers can screen candidates in seconds and reach out to them instantly. 

2. Improved candidate quality

In AI shortlisting tools, hiring teams can add their parameters specific to requirements, be it skills, knowledge, or others. These set parameters ensure only highly qualified candidates with relevant experience are shortlisted automatically. 

3. Scalability

Whether you receive 50 or 5,000 applications, AI systems can handle it without compromising speed or quality. This makes AI ideal for large-scale hiring or seasonal recruitment drives.

However, not all ATS tools come with such efficient AI. Hence, when choosing an ATS to support your hiring needs, ensure that it meets all fairness benchmarks and performs regular audits for accuracy, compliance checks, and diversity, equity, and inclusion (DEI) purposes. 

4. Enhanced candidate experience

As per the SHRM Talent Pulse Q1 2025 report, 72% of candidates drop off in between the process due to a lack of timely communication. Top talent easily gets frustrated and moves on to a new opportunity when they don’t hear back. 

With AI tools, candidates are informed faster, which makes them feel valued. 

5. Objective and bias-reduced selection

One of the biggest advantages of AI shortlisting is its bias-free results. You feed the specific criteria to your AI tools, based on which candidates are selected. AI doesn’t get influenced by names, photos, gender, or unconscious biases. Again, make sure to choose the ATS tool with fair and transparent AI. 

How does AI shortlisting work?

Resume parsing and keyword matching

First, resumes are scanned automatically to extract key information and record it in a structured format.  Then, it matches the extracted information against the JD using a keyword-based algorithm. 

It uses algorithms and machine learning to extract key information like contact details, work history, education, and skills from resumes in formats like PDF, DOCX, or TXT.

For example, if a role requires "project management" and "Agile methodology,". AI scans work history, education, and skills in seconds to match candidates with relevant terms and related context. This helps identify candidates who meet the basic role requirements quickly and accurately.

AI-driven candidate scoring and ranking

Once resumes are parsed, the AI assigns each candidate a score based on how closely their profile aligns with the job description and the fed keywords. Keywords are based on factors like relevant skills, years of experience, education level, and past job titles. 

The system thoroughly analyzes all the information in seconds and then ranks candidates from most to least suitable. This helps recruiters to focus on top candidates instantly.

Deep Dive – Best Practices for Responsible and Scalable AI Shortlisting

Understand AI Filters vs Bias

AI tools use filters to screen candidates based on predefined parameters like skills, experience, education, and keywords in resumes. These filters help automate and expedite the shortlisting process, ensuring that only candidates who meet specific job criteria are considered for further evaluation.

It is very natural for bias to creep into AI because it is trained on historical data. This creates a possibility of AI bias when choosing candidates based on previous hiring decisions, undermining the fairness and diversity of your hiring process.

To bring in more efficiency to your AI:

• Continuously test and monitor AI models to detect and correct biased patterns in shortlisting outcomes.
• Program AI with DEI (Diversity, Equity, and Inclusion) in mind, ensuring it doesn’t favor or exclude candidates based on non-relevant traits like gender, race, or age.
• Use diverse training data and regularly audit AI recommendations against real hiring outcomes to improve fairness and accuracy.
• Ensure compliance with hiring regulations

A responsible AI is not just about speed. It is important that AI gives results in compliance with legal frameworks that govern data privacy and anti-discrimination in hiring. Any non-compliance can lead to fines and penalties. 

Some of the key regulations for compliance are:

• NYC Local Law 144 compliance- It applies to companies using AEDT(An automated employment decision tool) and requires an independent auditor to assess these tools for potential bias.‍
• EU AI Act: It regulates the use of AI and ensures AI systems used within the EU are safe, transparent, non-discriminatory, and respect fundamental rights.

Learn 10+ global AI recruiting regulations every company should be aware of.

Here is how to ensure compliance: 

• Data Protection: AI systems must store and process candidate data securely, and candidates must know how their data will be used. This includes encryption, restricted access, and clear data retention policies.
• ‍Audit Trails: Keep records of how AI decisions were made, and why a candidate was or wasn’t shortlisted. These audit trails are important for transparency and defending hiring decisions if challenged.‍
• Documentation: Document your AI’s design, training data sources, and decision-making criteria. This ensures accountability and makes it easier to comply with audits or regulatory checks.
• Keep humans in the loop

While AI shortlisting tools provide end-to-end support, from resume and application scanning to making clear decisions, they must still serve the purpose of assisting, rather than replacing, recruiters.

AI is trained on historical data and lacks a comprehensive understanding of the full context. For example, there is a high chance that a candidate with a non-traditional background but with potential might get rejected by AI filters. A recruiter can spot such talent.

Also, in assessing cultural fit, human intuition plays an important role. While AI can identify red flags based on keywords, only a recruiter can assess the nuances of human characteristics. 

Here is how to keep humans in the loop:

• Let humans make the final call. Add a human review stage to validate AI-shortlisted candidates before final decisions are made. 
• Involve multiple key stakeholders in the human review stage to ensure diverse perspectives and reduce unconscious bias.
• Enable feedback loop on AI decisions so the system can learn and improve over time.
• Train recruiters on AI limitations so your team understands what AI can and cannot do, so they use it wisely and with context.
• Measure ROI from pilot to scale

Before fully implementing AI, test it through a pilot program to measure if the tool delivers real value and aligns with your hiring goals. 

You can start small by choosing one department, role type, or hiring round, and track key recruiting metrics such as:

• Time Saved: How much recruiter time is saved during resume screening and initial filtering?
  
• Cost per Hire: Is there a reduction in sourcing, labor, or agency costs?
  
• Quality of Hire: Are AI-selected candidates performing better or staying longer in their roles?
  
• Candidate Experience: Are candidates moving through the process faster and receiving timely feedback?

Once the pilot shows positive results, expand gradually. Continue to:

• Collecting feedback from recruiters and hiring managers.
  
• Refining AI algorithms based on hiring outcomes.
  
• Ensuring compliance and ethical usage are maintained as scale increases.

Why use Kula AI for shortlisting candidates?

Kula is a complete ATS tool with modern AI features. For shortlisting candidates, Kula offers AI scoring for screening applications. 

With AI scoring, hiring teams can screen and rank candidates based on customized criteria like education, skills, and experience. The system instantly reviews a large number of profiles based on this criterion and filters candidates based on AI scores. 

Kula also offers advanced interview kits with automated summaries and notetaking, along with auto-fill scorecards to support interview screening for faster hiring. You can also add a human review stage with different key stakeholders for approving final decisions.

Kula AI meets all fairness benchmarks and takes its AI seriously. Kula performs monthly audits to uphold its AI integrity and makes hiring 5X faster with objective and fair decisions.

‍Want to see Kula in action? Schedule a personalized demo today!