AI Resume Screening: How Applicant Scoring Works, and Where Humans Should Decide
Amaan Farhan
6 min read
AI resume screening is software that reads each application it can parse, compares it with the job you posted, and gives every applicant a score or rank. You then start with the closest apparent matches instead of going top to bottom. It is useful for sorting a large pile quickly. It is not reliable enough to reject people or pick a hire on its own, so a person on your team should own every "no", every shortlist and every offer.
Last checked: 9 November 2026.
In May 2026, Syed Muhammad Zubair in Lahore posted on LinkedIn about hiring an AI Engineer at catalase.ai. In his own account, more than 300 applications were read through an API, scored and summarised at about PKR 14 each in reported API cost, and he finished shortlisting in one sitting (LinkedIn post). That PKR 14 figure is his reported model cost for that round, not a full hiring cost and not a Taraki price. It is one self-reported example, not a typical result and not an independently measured accuracy study.
The first reply asked: "how do you know the AI made the right decisions?" Another said AI can organise applications but should not "become the main gatekeeper for someone's career opportunity." This post answers both points for employers.
How does AI resume screening work?
Most tools do three things in order. A mistake at any step carries through to the ranking (Rework).
Step | What the software does | Where it goes wrong |
|---|---|---|
1. Read | Pulls text from a readable file the tool accepts and sorts it into fields: jobs, dates, education, skills | Two-column layouts and text inside images can come out scrambled or empty |
2. Compare | Checks those fields against your job. Older tools match keywords; newer ones use language models that read meaning | A vague job description gives the tool nothing solid to compare against |
3. Score | Turns the match into a number, a grade or a ranked list | Some tools learn from past hiring decisions and copy their patterns |
The UK government's guidance on AI in recruitment notes that tools trained on past hiring data can inherit old bias, and some may treat employment gaps in ways that hurt parents, carers and people with health conditions (UK DSIT guidance, PDF).
Check whether the tool can read the files you actually receive. A CV photographed on WhatsApp is an image; if the tool cannot extract readable text, that applicant may score low for reasons unrelated to the job. Ask which text-based formats it accepts rather than assuming every PDF will parse.
Is AI resume screening accurate?
There is no single accuracy number. Reading a date correctly is one task. Spotting a qualified person is another, and predicting who will do well in the job is a third. A tool can be good at one and weak at the others (Yena).
What some published research shows is that rankings can lean in ways you would never accept from a person. In a University of Washington test of more than 3 million CV and job pairings, three language models favoured white-associated names 85% of the time (UW Information School). That study used American names and a specific test design. It is not a measurement of Tara, or of hiring in Pakistan.
On 9 November 2026 we searched for a published evaluation of how screening tools treat Pakistani names, universities, cities or Urdu-medium schooling and did not find one that day. That is a search result, not proof that no such study exists. Ask any vendor for dated evidence that is relevant to the applicants you hire.
One recruiter on Reddit had an AI tool and three human recruiters score the same 50 CVs and reported 71% overlap (r/recruitinghell). That is one experiment, not a study, but it shows how often a score and a human read can disagree.
Does a human check catch the AI's mistakes?
Only if the check is built to. In a second University of Washington study, 528 people picked candidates with help from simulated AI recommendations. When the recommendations were heavily biased, people followed them about 90% of the time (UW News).
A follow-up analysis of the same data found people spent up to 55.6% longer on CVs when no AI recommendation was shown, and for candidates the AI did not recommend, every extra 30 seconds of reading raised selection chance by about 4% (Wilson et al., FAccT 2026).
A recruiter who only opens the top ten and clicks "approve" is not checking anything. The useful step is reading some of the low scores properly, before looking at the number.
Which hiring decisions should stay with people?
Use AI for order and speed. Keep judgement with your team. The chart below is a suggested division of labour, not a verified map of what any one product automates.
Suggested split between score and team. Not a verified Taraki automation map.
Task | Let the score do it? | Why |
|---|---|---|
Sort a large pile of applications | Yes | Order is cheap to correct; you can still scroll down |
Decide who to read first | Yes | Saves the most time with the least risk |
Reject an applicant | No, a person confirms | A rejection is final for the candidate |
Build the interview shortlist | No | Needs context the CV does not show |
Judge a career gap, a switch of field or a non-standard CV | No | These are where scoring tools trip most often |
Make the offer | No | Pay, notice period and fit are human conversations |
If you already shortlist by hand, this slots into the process from our guide on how to shortlist CVs. The score can replace the first sort. It does not replace the read.
What should you check about candidate data?
CVs contain personal data: phone numbers, home addresses and sometimes CNIC details. Before you buy or configure a screening tool, check who on your team can view or download applications, how long they are kept, and how they can be deleted. If applicants' files will leave your systems (for example into a general chatbot), review that tool's terms and privacy policy first. This is practical hygiene, not legal advice; ask counsel what duties apply to your company today.
A weekly check you can run on the scores
This is a suggested comparison routine, not a validated audit and not a timed test we have run. Use it when a role is getting many applications and you want your own evidence.
Pick a small sample from the top, middle and bottom of the ranked list.
Hide the score. Read each CV and write your own assessment.
Reveal the score band and record job-related evidence, possible ranking issues and points to investigate.
Look for a pattern (image CVs, one city, career-switchers).
Fix the input: rewrite a vague job description, or ask applicants for a readable text-based format the tool accepts.
Applicant ID | Score band | My assessment (score hidden) | Review notes |
|---|---|---|---|
Top / Middle / Bottom |
Keep one sheet tab per role. Patterns you see are a starting point for your process, not proof the tool is fair or accurate overall.
What Taraki's score is for, and what it leaves to you
We make Taraki, so read this part with that in mind. Product pages describe Tara scoring applicants against the job you posted (AI scoring). Treat a score as a sorting aid inside applicant tracking, not as proof that Tara is accurate or fair for every role. Past applicants can stay searchable in your candidate database. This article does not establish Tara's accuracy, bias performance, default sort behaviour, or auto-rejection behaviour; confirm those in a demo.
AI resume screening has multiple benefits that I've seen when it comes to hiring. When you get around 200-300 applications per role on a regular basis, it is very difficult to go through each CV with the same amount of attention, remembering the good and the bad whilst making notes. Every collaborator on this, or person who also views the CV, might have a bias attached. At times you may even miss out on applications just because you get tired of going through so many and leave the rest because you think you found the right candidate, or just because you got tired. Therefore, the AI score sort of does this for you, telling you from the get-go the 20-30 applications that are worth your time for a deep dive.
Silver is PKR 10,000 a month (3 job posts); Gold is PKR 25,000 a month (unlimited). Both can send each job to your LinkedIn company page, Google Jobs and Indeed (pricing). Our recruitment software guide for Pakistan compares options. Our explainer on what an applicant tracking system is covers the basics.
Questions employers ask about AI screening
Can AI reject candidates automatically?
Some tools let you set rules that send low scorers straight to Rejected. Behaviour varies by product. Prefer letting the score sort, and having a person confirm each rejection.
Should I tell applicants that software reads their CV?
Often useful, because candidates already ask. Only if this matches your process, you could say: "Applications may be sorted by software first; a person on our team makes the hiring decisions." If any decision stage is automated, describe that accurately instead. Confirm with counsel what your company should disclose.
Can applicants trick an AI screener?
Some try, for example with hidden text stuffed with keywords (Sapia). Reading the top-scored CVs yourself is the simplest check.
Can I paste CVs into a general chatbot to screen them?
Only if your organisation has approved that tool for applicant data. Review its terms and privacy policy first. Removing names from a paste does not guarantee anonymity if other details remain.
Want a score that helps you decide who to read first, with the final call still yours? See how Tara's AI scoring is described.
Amaan Farhan, Marketing Associate at Taraki. He leads marketing and growth with an AI-native approach, and writes about how Pakistani teams can hire with less admin and better tools. LinkedIn
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